Author(s) ID,Title,Year,DOI,Link,Abstract
"8632797000;7401526171;7005052907;7403872687;","Precipitation estimation from remotely sensed imagery using an artificial neural network cloud classification system",2004,"10.1175/jam2173.1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-13244261088&doi=10.1175%2fjam2173.1&partnerID=40&md5=fa0c498f34df1b9752ed8fbce447184b","A satellite-based rainfall estimation algorithm, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) Cloud Classification System (CCS), is described. This algorithm extracts local and regional cloud features from infrared (10.7 μm) geostationary satellite imagery in estimating finescale (0.04° × 0.04° every 30 min) rainfall distribution. This algorithm processes satellite cloud images into pixel rain rates by 1) separating cloud images into distinctive cloud patches; 2) extracting cloud features, including coldness, geometry, and texture; 3) clustering cloud patches into well-organized subgroups; and 4) calibrating cloud-top temperature and rainfall (Tb-R) relationships for the classified cloud groups using gauge-corrected radar hourly rainfall data. Several cloud-patch categories with unique cloud-patch features and Tb-R curves were identified and explained. Radar and gauge rainfall measurements were both used to evaluate the PERSIANN CCS rainfall estimates at a range of temporal (hourly and daily) and spatial (0.04°, 0.12°, and 0.25°) scales. Hourly evaluation shows that the correlation coefficient (CC) is 0.45 (0.59) at a 0.04° (0.25°) grid scale. The averaged CC of daily rainfall is 0.57 (0.63) for the winter (summer) season. © 2004 American Meteorological Society."
"8844176800;6602614473;","3D terrestrial lidar data classification of complex natural scenes using a multi-scale dimensionality criterion: Applications in geomorphology",2012,"10.1016/j.isprsjprs.2012.01.006","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84857315359&doi=10.1016%2fj.isprsjprs.2012.01.006&partnerID=40&md5=f8545f60c2c613bc6ccd549e4d35f394","3D point clouds of natural environments relevant to problems in geomorphology (rivers, coastal environments, cliffs,. ...) often require classification of the data into elementary relevant classes. A typical example is the separation of riparian vegetation from ground in fluvial environments, the distinction between fresh surfaces and rockfall in cliff environments, or more generally the classification of surfaces according to their morphology (e.g. the presence of bedforms or by grain size). Natural surfaces are heterogeneous and their distinctive properties are seldom defined at a unique scale, prompting the use of multi-scale criteria to achieve a high degree of classification success. We have thus defined a multi-scale measure of the point cloud dimensionality around each point. The dimensionality characterizes the local 3D organization of the point cloud within spheres centered on the measured points and varies from being 1D (points set along a line), 2D (points forming a plane) to the full 3D volume. By varying the diameter of the sphere, we can thus monitor how the local cloud geometry behaves across scales. We present the technique and illustrate its efficiency in separating riparian vegetation from ground and classifying a mountain stream as vegetation, rock, gravel or water surface. In these two cases, separating the vegetation from ground or other classes achieve accuracy larger than 98%. Comparison with a single scale approach shows the superiority of the multi-scale analysis in enhancing class separability and spatial resolution of the classification. Scenes between 10 and one hundred million points can be classified on a common laptop in a reasonable time. The technique is robust to missing data, shadow zones and changes in point density within the scene. The classification is fast and accurate and can account for some degree of intra-class morphological variability such as different vegetation types. A probabilistic confidence in the classification result is given at each point, allowing the user to remove the points for which the classification is uncertain. The process can be both fully automated (minimal user input once, all scenes treated in large computation batches), but also fully customized by the user including a graphical definition of the classifiers if so desired. Working classifiers can be exchanged between users independently of the instrument used to acquire the data avoiding the need to go through full training of the classifier. Although developed for fully 3D data, the method can be readily applied to 2.5D airborne lidar data. © 2012 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS)."
"7003899619;55464238000;7003519431;","Scientific basis and initial evaluation of the CLAVR-1 global clear/cloud classification algorithm for the advanced very high resolution radiometer",1999,"10.1175/1520-0426(1999)016<0656:SBAIEO>2.0.CO;2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0032787145&doi=10.1175%2f1520-0426%281999%29016%3c0656%3aSBAIEO%3e2.0.CO%3b2&partnerID=40&md5=6a067c1d64bb05716a89991d867d8b7b","An algorithm for the remote sensing of global cloud cover using multispectral radiance measurements from the Advanced Very High Resolution Radiometer (AVHRR) on board National Oceanic and Atmospheric Administration (NOAA) polar-orbiting satellites has been developed. The CLAVR-1 (Clouds from AVHRR-Phase I) algorithm classifies 2 x 2 pixel arrays from the Global Area Coverage (GAC) 4-km-resolution archived database into CLEAR, MIXED, and CLOUDY categories. The algorithm uses a sequence of multispectral contrast, spectral, and spatial signature threshold tests to perform the classification. The various tests and the derivation of their thresholds are presented. CLAVR-1 has evolved through experience in applying it to real-time NOAA-11 data, and retrospectively through the NOAA AVHRR Pathfinder Atmosphere project, where 16 years of data have been reprocessed into cloud, radiation budget, and aerosol climatologies. The classifications are evaluated regionally with image analysis, and it is concluded that the algorithm does well at classifying perfectly clear pixel arrays, except at high latitudes in their winter seasons. It also has difficulties with classifications over some desert and mountainous regions and when viewing regions of ocean specular reflection. Generally, the CLAVR-1 fractional cloud amounts, when computed using a statistically equivalent spatial coherence method, agree to within about 0.05-0.10 of image/analyst estimates on average. There is a tendency for CLAVR-1 to underestimate cloud amount when it is large and to overestimate it when small."
"55054898400;57207603330;7006446406;","Automatic cloud classification of whole sky images",2010,"10.5194/amt-3-557-2010","https://www.scopus.com/inward/record.uri?eid=2-s2.0-80053965495&doi=10.5194%2famt-3-557-2010&partnerID=40&md5=77368910608967137fb2ad16e3dbf1b0","The recently increasing development of whole sky imagers enables temporal and spatial high-resolution sky observations. One application already performed in most cases is the estimation of fractional sky cover. A distinction between different cloud types, however, is still in progress. Here, an automatic cloud classification algorithm is presented, based on a set of mainly statistical features describing the color as well as the texture of an image. The k-nearest-neighbour classifier is used due to its high performance in solving complex issues, simplicity of implementation and low computational complexity. Seven different sky conditions are distinguished: high thin clouds (cirrus and cirrostratus), high patched cumuliform clouds (cirrocumulus and altocumulus), stratocumulus clouds, low cumuliform clouds, thick clouds (cumulonimbus and nimbostratus), stratiform clouds and clear sky. Based on the Leave-One-Out Cross-Validation the algorithm achieves an accuracy of about 97%. In addition, a test run of random images is presented, still outperforming previous algorithms by yielding a success rate of about 75%, or up to 88% if only ""serious"" errors with respect to radiation impact are considered. Reasons for the decrement in accuracy are discussed, and ideas to further improve the classification results, especially in problematic cases, are investigated. © Author(s) 2010."
"26643250500;57203053317;","A parameterization of cirrus cloud formation: Homogeneous freezing including effects of aerosol size",2002,"10.1029/2001JD001429","https://www.scopus.com/inward/record.uri?eid=2-s2.0-18144449447&doi=10.1029%2f2001JD001429&partnerID=40&md5=d372b2c88f30330eb0f6c124dd294b71","In a previous study, we have derived an approximate, physically based parameterization of cirrus cloud formation by homogeneous freezing, applying to a wide class of supercooled aerosols in the upper troposphere and tropopause region. In this study, the parameterization scheme is extended to include the effects of aerosol size on the freezing process in adiabatically rising air parcels. Aerosol size effects become important when the timescale of the freezing event is fast compared to the timescale of depositional growth of the pristine ice particles. The generalized parameterization scheme is validated with parcel model simulations and can directly be applied in models that do not explicitly resolve the ice nucleation process, such as cloud-resolving models, weather forecast models, and climate models. The relationship between aerosol and ice crystal number concentrations in cirrus clouds formed by homogeneous freezing is discussed. This relationship is much weaker than in liquid water clouds. It is shown that even freezing of enhanced levels of sulfate aerosol originating from strong volcanic eruptions is unlikely to exert a sensible influence on cirrus formation. Copyright 2002 by the American Geophysical Union."
"56219012200;56624502400;55886067800;57202531041;26023140500;55672593500;57189340211;57189342126;55703016100;7006614214;6506606807;","The libRadtran software package for radiative transfer calculations (version 2.0.1)",2016,"10.5194/gmd-9-1647-2016","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84969567799&doi=10.5194%2fgmd-9-1647-2016&partnerID=40&md5=7307fa34c9ebf8e0ceb62f28a33eef72","libRadtran is a widely used software package for radiative transfer calculations. It allows one to compute (polarized) radiances, irradiance, and actinic fluxes in the solar and thermal spectral regions. libRadtran has been used for various applications, including remote sensing of clouds, aerosols and trace gases in the Earth's atmosphere, climate studies, e.g., for the calculation of radiative forcing due to different atmospheric components, for UV forecasting, the calculation of photolysis frequencies, and for remote sensing of other planets in our solar system. The package has been described in Mayer and Kylling (2005). Since then several new features have been included, for example polarization, Raman scattering, a new molecular gas absorption parameterization, and several new parameterizations of cloud and aerosol optical properties. Furthermore, a graphical user interface is now available, which greatly simplifies the usage of the model, especially for new users. This paper gives an overview of libRadtran version 2.0.1 with a focus on new features. Applications including these new features are provided as examples of use. A complete description of libRadtran and all its input options is given in the user manual included in the libRadtran software package, which is freely available at http://www.libradtran.org. © 2016 Author(s)."
"35262555900;55317303400;57188803262;","A review of merged high-resolution satellite precipitation product accuracy during the Tropical Rainfall Measuring Mission (TRMM) era",2016,"10.1175/JHM-D-15-0190.1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84963594731&doi=10.1175%2fJHM-D-15-0190.1&partnerID=40&md5=39a93da021864f0fad2c42bd673f64ff","A great deal of expertise in satellite precipitation estimation has been developed during the Tropical Rainfall Measuring Mission (TRMM) era (1998-2015). The quantification of errors associated with satellite precipitation products (SPPs) is crucial for a correct use of these datasets in hydrological applications, climate studies, and water resources management. This study presents a review of previous work that focused on validating SPPs for liquid precipitation during the TRMM era through comparisons with surface observations, both in terms of mean errors and detection capabilities across different regions of the world. Several SPPs have been considered: TMPA 3B42 (research and real-time products), CPC morphing technique (CMORPH), Global Satellite Mapping of Precipitation (GSMaP; both the near-real-time and the Motion Vector Kalman filter products), PERSIANN, and PERSIANN-Cloud Classification System (PERSIANN-CCS). Topography, seasonality, and climatology were shown to play a role in the SPP's performance, especially in terms of detection probability and bias. Regions with complex terrain exhibited poor rain detection and magnitude-dependent mean errors; low probability of detection was reported in semiarid areas. Winter seasons, usually associated with lighter rain events, snow, and mixed-phase precipitation, showed larger biases. © 2016 American Meteorological Society."
"7601453628;6603421020;7402546593;7201914101;","A Neural Network Approach to Cloud Classification",1990,"10.1109/36.58972","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0025495202&doi=10.1109%2f36.58972&partnerID=40&md5=16926f560aeb101add696f0e7d4829d8","Recent cloud retrieval validation studies suggest that there are major discrepancies between the various algorithms. Other studies have demonstrated that the use of texture-based pattern recognition features can signifcantly improve cloud identifcation accuracy. However, the capabilities and accuracies which can be attained with spatial information remain poorly understood and undocumented, and the choice of an optimal feature set is unknown. The results from this study demonstrate that, using high spatial resolution data, very high cloud classifcation accuracies can be obtained. A texture-based neural network classifer using only single-channel visible LANDSAT MSS imagery achieves an overall cloud identifcation accuracy of 93%. 11 is remarkable that cirrus can be distinguished from boundary layer cloudiness with an accuracy of 96%, without the use of an infrared channel. Stratocumulus is retrieved with an accuracy of 92%, cumulus at 90%. The use of the neural network does not improve cirrus classifcation accuracy. Rather, its main effect is in the improved separation between stratocumulus and cumulus cloudiness. While most cloud classifcation algorithms rely on linear, parametric schemes, the present study is based on a nonlinear, nonparametric four-layer neural network approach. Intercomparisons are made to a three-layer neural network architecture, the nonparametric K-nearest neighbor approach, and the linear stepwise discriminant analysis procedure. A signifcant finding is that signifcantly higher accuracies are attained with the nonparametric approaches using only 20% of the database as training data, compared to 67% of the database in the linear approach. © 1990 IEEE"
"8213128500;9943212600;6603354695;7006892410;36177028800;","Semisupervised one-class support vector machines for classification of remote sensing data",2010,"10.1109/TGRS.2010.2045764","https://www.scopus.com/inward/record.uri?eid=2-s2.0-77954757586&doi=10.1109%2fTGRS.2010.2045764&partnerID=40&md5=e227b7b31f7aefaed98d96a77322c255","This paper presents two semisupervised one-class support vector machine (OC-SVM) classifiers for remote sensing applications. In one-class image classification, one tries to detect pixels belonging to one of the classes in the image and reject the others. When few labeled pixels of only one class are available, obtaining a reliable classifier is a difficult task. In the particular case of SVM-based classifiers, this task is even harder because the free parameters of the model need to be finely adjusted, but no clear criterion can be adopted. In order to improve the OC-SVM classifier accuracy and alleviate the problem of free-parameter selection, the information provided by unlabeled samples present in the scene can be used. In this paper, we present two state-of-the-art algorithms for semisupervised one-class classification for remote sensing classification problems. The first proposed algorithm is based on modifying the OC-SVM kernel by modeling the data marginal distribution with the graph Laplacian built with both labeled and unlabeled samples. The second one is based on a simple modification of the standard SVM cost function which penalizes more the errors made when classifying samples of the target class. The good performance of the proposed methods is illustrated in four challenging remote sensing image classification scenarios where the goal is to detect one of the classes present on the scene. In particular, we present results for multisource urban monitoring, hyperspectral crop detection, multispectral cloud screening, and change-detection problems. Experimental results show the suitability of the proposed techniques, particularly in cases with few or poorly representative labeled samples. © 2006 IEEE."
"57193882808;57203012011;7006095466;56216811200;","Cloud-resolving modeling of cloud systems during phase III of GATE. Part II: Effects of resolution and the third spatial dimension",1998,"10.1175/1520-0469(1998)055<3264:CRMOCS>2.0.CO;2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0033232461&doi=10.1175%2f1520-0469%281998%29055%3c3264%3aCRMOCS%3e2.0.CO%3b2&partnerID=40&md5=13e9dc279fc90f6c4f5fad71cbae51a7","Two- and three-dimensional simulations of cloud systems for the period of 1-7 September 1974 in phase III of the Global Atmospheric Research Programme (GARP) Atlantic Tropical Experiment (GATE) are performed using the approach discussed in Part I of this paper. The aim is to reproduce cloud systems over the GATE B-scale sounding array. Comparison is presented between three experiments driven by the same large-scale conditions: (i) a fully three-dimensional experiment, (ii) a two-dimensional experiment that is an east-west section of the three-dimensional case, and (iii) a high-resolution version of the two-dimensional experiment Differences between two- and three-dimensional frameworks and those related to spatial resolution are analyzed. The three-dimensional experiment produced a qualitatively realistic organization of convection: nonsquall clusters, a squall line, and scattered convection and transitions between regimes were simulated. The two-dimensional experiments produced convective organization similar to that discussed in Part I. The thermodynamic fields evolved very similarly in all three experiments, although differences between model fields and observations did occur. When averaged over a few hours, surface sensible and latent heat fluxes and surface precipitation evolved very similarly in all three experiments and evaluated well against observations. Model resolution had some effect on the upper-troposheric cloud cover and consequently on the upper-tropospheric temperature ten-dency due to radiative flux divergence. When compared with the fully three-dimensional results, the two-dimensional simulations produced a much higher temporal variability of domain-averaged quantities The results support the notion that, as long as high-frequency temporal variability is not of primary importance, low-resolution two-dimensional simulations can be used as realizations of tropical cloud systems in the climate problem and for improving and/or testing cloud parameterizations for large-scale models."
"7006698304;6603422104;","Objective identification of cloud regimes in the Tropical Western Pacific",2003,"10.1029/2003GL018367","https://www.scopus.com/inward/record.uri?eid=2-s2.0-1642280532&doi=10.1029%2f2003GL018367&partnerID=40&md5=8b279de0a2202add879ac1d8dd2a67fb","Identifying cloud regimes and their role in the climate system can serve a multitude of purposes, ranging from a better understanding of clouds to guiding field experiments to improving the representation of clouds in models. This study describes early results in identifying cloud regimes from ISCCP data using cluster analysis. A simple algorithm for cloud regime identification is introduced and applied to data in the Tropical Western Pacific region. Four major cloud regimes, namely a shallow cumulus regime, a transparent isolated cirrus regime, thick cirrus with convection and a deep and probably organized convective regime are identified and their frequency of occurrence is quantified. The use of the regime information for various applications is discussed and the use of regime classifications for representativeness studies is presented using the ARM TWP sites as an example. Copyright 2003 by the American Geophysical Union."
"57193132723;36851768400;57204886915;7403318365;","The MJO Transition from shallow to deep convection in cloudsat/CALIPSO data and GISS GCM simulations",2012,"10.1175/JCLI-D-11-00384.1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84862732224&doi=10.1175%2fJCLI-D-11-00384.1&partnerID=40&md5=90f15c894eb5bd57e82f2cec5051f093","The relationship between convective penetration depth and tropospheric humidity is central to recent theories of the Madden-Julian oscillation (MJO). It has been suggested that general circulation models (GCMs) poorly simulate the MJO because they fail to gradually moisten the troposphere by shallow convection and simulate a slow transition to deep convection. CloudSat and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) data are analyzed to document the variability of convection depth and its relation to water vapor during the MJO transition from shallow to deep convection and to constrain GCM cumulus parameterizations. Composites of cloud occurrence for 10 MJO events show the following anticipated MJO cloud structure: shallow and congestus clouds in advance of the peak, deep clouds near the peak, and upper-level anvils after the peak. Cirrus clouds are also frequent in advance of the peak. The Advanced Microwave Scanning Radiometer for Earth Observing System (EOS) (AMSR-E) columnwater vapor (CWV) increases by~5 mmduring the shallow- deep transition phase, consistent with the idea of moisture preconditioning. Echo-top height of clouds rooted in the boundary layer increases sharply with CWV, with large variability in depth when CWV is between;46 and 68 mm. International Satellite Cloud Climatology Project cloud classifications reproduce these climatological relationships but correctly identify congestus-dominated scenes only about half the time. A version of the Goddard Institute for Space Studies Model E2 (GISS-E2) GCM with strengthened entrainment and rain evaporation that produces MJO-like variability also reproduces the shallow-deep convection transition, including the large variability of cloud-top height at intermediate CWV values. The variability is due to small grid-scale relative humidity and lapse rate anomalies for similar values of CWV."
"7006802750;","Cloud classification of AVHRR imagery in maritime regions using a probabilistic neural network",1994,"10.1175/1520-0450(1994)033<0909:CCOAII>2.0.CO;2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0028603789&doi=10.1175%2f1520-0450%281994%29033%3c0909%3aCCOAII%3e2.0.CO%3b2&partnerID=40&md5=ec2cecb933790adfb596f2c2ed3c4164","Using Advanced Very High Resolution Radiometer data, 16 pixel × 16 pixel sample areas are classified into one of 10 output classes using a probabilistic neural network (PNN). The 10 classes are cirrus, cirrocumulus, cirrostratus, altostratus, nimbostratus, stratocumulus, stratus, cumulus, cumulonimbus, and clear. Over 200 features drawn from spectral, textural, and physical measures are computed from the pixel data for each sample area. The input patterns presented to the neural network are a subset of these features selected by a routine that indicates the discriminatory potential of each feature. The training and testing input data used by the PNN are obtained from 95 expertly labeled images taken from seven maritime regions; these images provide 1633 sample areas. Theoretical accurcy of the PNN classifier is determined using two methods. In the hold-one-cut method, the network is trained on all data samples minus one and is tested on the remaining sample. Using this technique, 79.8% of the samples are classified correctly. A bootstrap method of 100 randomly determined sample sets produces an average overall accuracy of 77.1%, with a standard deviation of 1.4%. In a more general classification using five classes (low clouds, altostratus, high clouds, precipitating clouds, and clear, 91.2% of the samples are accurately classified. -from Author"
"7003711370;6507344930;6507312836;6507267924;6506472454;6505723859;6505939312;","Automatic cloud detection applied to NOAA-11 /AVHRR imagery",1993,"10.1016/0034-4257(93)90046-Z","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0027789431&doi=10.1016%2f0034-4257%2893%2990046-Z&partnerID=40&md5=f9324c99c93b71f416754294aafd19e2","The imagery from the AVHRR on board NOAA polar orbiting satellites allows a description of cloud cover, oceanic, and continental surfaces that is used by Météo-France for nowcasting activities and as input for numerical weather prediction models (NWP). A real-time processing scheme has been designed at the Centre de Météorologie Spatiale (CMS) in Lannion to extract cloud cover and surface parameters from NOAA-11 AVHRR imagery received at CMS. The key step of this scheme is cloud detection. It is based upon threshold tests applied to different combinations of channels. Its main originality is its complete automation by the computation of the 11 μm infrared threshold from a monthly sea surface temperature (SST) climatology over the oceans and from air temperature (near the surface) forecast by NWP over land. A special test has been implemented to detect cloud edges and subpixel clouds over continental surfaces during daytime. It is applied daily in deferred time only to compute normalized difference vegetation index (NDVI). This scheme has been used operationally since February 1990, and its quality has been checked. It has enabled the routine production of various products. A nighttime cloud classification is sent to all French forecasters; NDVI values are computed daily and used to map the vegetation cover; and SST and thermal fronts are derived operationally from nighttime imagery. © 1993."
"8632797000;6602948135;7405938832;7401526171;7005052907;","Evaluation of PERSIANN-CCS rainfall measurement using the NAME event rain gauge network",2007,"10.1175/JHM574.1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-34447631519&doi=10.1175%2fJHM574.1&partnerID=40&md5=888a152e4885499bd064f48e0907c490","Robust validation of the space-time structure of remotely sensed precipitation estimates is critical to improving their quality and confident application in water cycle-related research. In this work, the performance of the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS) precipitation product is evaluated against warm season precipitation observations from the North American Monsoon Experiment (NAME) Event Rain Gauge Network (NERN) in the complex terrain region of northwestern Mexico. Analyses of hourly and daily precipitation estimates show that the PERSIANN-CCS captures well active and break periods in the early and mature phases of the monsoon season. While the PERSIANN-CCS generally captures the spatial distribution and timing of diurnal convective rainfall, elevation-dependent biases exist, which are characterized by an underestimate in the occurrence of light precipitation at high elevations and an overestimate in the occurrence of precipitation at low elevations. The elevation-dependent biases contribute to a 1-2-h phase shift of the diurnal cycle of precipitation at various elevation bands. For reasons yet to be determined, the PERSIANN-CCS significantly underestimated a few active periods of precipitation during the late or ""senescent"" phase of the monsoon. Despite these shortcomings, the continuous domain and relatively high spatial resolution of PERSIANN-CCS quantitative precipitation estimates (QPEs) provide useful characterization of precipitation space-time structures in the North American monsoon region of northwestern Mexico, which should prove useful for hydrological applications. © 2007 American Meteorological Society."
"23004944100;35109924900;23005893600;55936805100;6603065779;","Water surface mapping from airborne laser scanning using signal intensity and elevation data",2009,"10.1002/esp.1853","https://www.scopus.com/inward/record.uri?eid=2-s2.0-70349869481&doi=10.1002%2fesp.1853&partnerID=40&md5=c9842663c836d85cd48b91ca5b0216b1","In recent years airborne laser scanning (ALS) evolved into a state-of-the-art technology for topographic data acquisition. We present a novel, automatic method for water surface classification and delineation by combining the geometrical and signal intensity information provided by ALS. The reflection characteristics of water surfaces in the near-infrared wavelength (1064 nm) of the ALS system along with the surface roughness information provide the basis for the differentiation between water and land areas. Water areas are characterized by a high number of laser shot dropouts and predominant low backscatter energy. In a preprocessing step, the recorded intensities are corrected for spherical loss and atmospheric attenuation, and the locations of laser shot dropouts are modeled. A seeded region growing segmentation, applied to the point cloud and the modeled dropouts, is used to detect potential water regions. Object-based classification of the resulting segments determines the final separation of water and non-water points. The water-land-boundary is defined by the central contour line of the transition zone between water and land points. We demonstrate that the proposed workflow succeeds for a regulated river (Inn, Austria) with smooth water surface as well as for a pro-glacial braided river (Hintereisfernerbach, Austria). A multi-temporal analysis over five years of the pro-glacial river channel emphasizes the applicability of the developed method for different ALS systems and acquisition settings (e.g. point density). The validation, based on real time kinematic (RTK) global positioning system (GPS) field survey and a terrestrial orthophoto, indicate point cloud classification accuracy above 97% with 0·45 m planimetric accuracy (root mean square error) of the water-land boundary. This article shows the capability of ALS data for water surface mapping with a high degree of automation and accuracy. This provides valuable datasets for a number of applications in geomorphology, hydrology and hydraulics, such as monitoring of braided rivers, flood modeling and mapping. © 2009 John Wiley & Sons, Ltd."
"56769708700;56422351400;24829272400;35099690000;8557497200;7004040532;","SEMANTIC3D.NET: A NEW LARGE-SCALE POINT CLOUD CLASSIFICATION BENCHMARK",2017,"10.5194/isprs-annals-IV-1-W1-91-2017","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85040692236&doi=10.5194%2fisprs-annals-IV-1-W1-91-2017&partnerID=40&md5=fd39b83943b97e48d15923780fea366d","This paper presents a new 3D point cloud classification benchmark data set with over four billion manually labelled points, meant as input for data-hungry (deep) learning methods. We also discuss first submissions to the benchmark that use deep convolutional neural networks (CNNs) as a work horse, which already show remarkable performance improvements over state-of-the-art. CNNs have become the de-facto standard for many tasks in computer vision and machine learning like semantic segmentation or object detection in images, but have no yet led to a true breakthrough for 3D point cloud labelling tasks due to lack of training data. With the massive data set presented in this paper, we aim at closing this data gap to help unleash the full potential of deep learning methods for 3D labelling tasks. Our semantic3D.net data set consists of dense point clouds acquired with static terrestrial laser scanners. It contains 8 semantic classes and covers a wide range of urban outdoor scenes: churches, streets, railroad tracks, squares, villages, soccer fields and castles. We describe our labelling interface and show that our data set provides more dense and complete point clouds with much higher overall number of labelled points compared to those already available to the research community. We further provide baseline method descriptions and comparison between methods submitted to our online system. We hope semantic3D.net will pave the way for deep learning methods in 3D point cloud labelling to learn richer, more general 3D representations, and first submissions after only a few months indicate that this might indeed be the case. © 2017 Copernicus GmbH. All rights reserved."
"7003414581;15059495000;6701697023;7403361959;22954523900;6602215448;6603611663;6603816167;23991118400;6602999057;7003478309;","Influence of Saharan dust on cloud glaciation in southern Morocco during the Saharan Mineral Dust Experiment",2008,"10.1029/2007JD008785","https://www.scopus.com/inward/record.uri?eid=2-s2.0-42549086207&doi=10.1029%2f2007JD008785&partnerID=40&md5=41a5700072760f81da4a566c2cce4078","Multiwavelength lidar, Sun photometer, and radiosonde observations were conducted at Ouarzazate (30.9°N, 6.9°W, 1133 m above sea level, asl), Morocco, in the framework of the Saharan Mineral Dust Experiment (SAMUM) in May-June 2006. The field site is close to the Saharan desert. Information on the depolarization ratio, backscatter and extinction coefficients, and lidar ratio of the dust particles, estimates of the available concentration of atmospheric ice nuclei at cloud level, profiles of temperature, humidity, and the horizontal wind vector as well as backward trajectory analysis are used to study cases of cloud formation in the dust with focus on heterogeneous ice formation. Surprisingly, most of the altocumulus clouds that form at the top of the Saharan dust layer, which reaches into heights of 4-7 km asl and has layer top temperatures of -8°C to -18°C, do not show any ice formation. According to the lidar observations the presence of a high number of ice nuclei (1-20 cm-3) does not automatically result in the obvious generation of ice particles, but the observations indicate that cloud top temperatures must typically reach values as low as -20°C before significant ice production starts. Another main finding is that liquid clouds are obviously required before ice crystals form via heterogeneous freezing mechanisms, and, as a consequence, that deposition freezing is not an important ice nucleation process. An interesting case with cloud seeding in the free troposphere above the dust layer is presented in addition. Small water clouds formed at about -30°C and produced ice virga. These virga reached water cloud layers several kilometers below the initiating cloud cells and caused strong ice production in these clouds at temperatures as high as -12°C to -15°C. Copyright 2008 by the American Geophysical Union."
"8918197800;55232388000;7005793536;7006010456;57219546794;","Cloud detection and classification with the use of whole-sky ground-based images",2012,"10.1016/j.atmosres.2012.05.005","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84861554997&doi=10.1016%2fj.atmosres.2012.05.005&partnerID=40&md5=0b4ed5136935bd623a8a0d8aa72c486e","A simple whole sky imaging system, based on a commercial digital camera with a fish-eye lens and a hemispheric dome, is used for the automatic estimation of total cloud coverage and classification. For the first time, a multi color criterion is applied on sky images, in order to improve the accuracy in detection of broken and overcast clouds under large solar zenith angles. The performance of the cloud detection algorithm is successfully compared with ground based weather observations. A simple method is presented for the detection of raindrops standing on the perimeter of hemispheric dome. Based on previous works on cloud classification, an improved k-Nearest-Neighbor algorithm is presented, based not only on statistical color and textural features, but taking also into account the solar zenith angle, the cloud coverage, the visible fraction of solar disk and the existence of raindrops in sky images. The successful detection percentage of the classifier ranges between 78 and 95% for seven cloud types. © 2012 Elsevier B.V."
"56111060800;14048744800;56735366800;","A hybrid thresholding algorithm for cloud detection on ground-based color images",2011,"10.1175/JTECH-D-11-00009.1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-82955194501&doi=10.1175%2fJTECH-D-11-00009.1&partnerID=40&md5=cf1ad5d1ffb98c51d6956fd39d20add8","Cloud detection is the precondition for deriving other information (e.g., cloud cover) in ground-based sky imager applications. This paper puts forward an effective cloud detection approach, the Hybrid Thresholding Algorithm (HYTA) that fully exploits the benefits of the combination of fixed and adaptive thresholding methods. First, HYTA transforms an input color cloud image into a normalized blue/red channel ratio image that can keep a distinct contrast, even with noise and outliers. Then, HYTAidentifies the ratio image as either unimodal or bimodal according to its standard deviation, and the unimodal and bimodal images are handled by fixed and minimum cross entropy (MCE) thresholding algorithms, respectively. The experimental results demonstrate that HYTA shows an accuracy of 88.53%, which is far higher than those of either fixed or MCE thresholding alone. Moreover, HYTA is also verified to outperform other state-of-the-art cloud detection approaches. © 2011 American Meteorological Society."
"37011423400;36064917000;25926243500;7005906188;55500134600;7101947159;55577486600;56381700700;55724964400;","A multiscale and hierarchical feature extraction method for terrestrial laser scanning point cloud classification",2015,"10.1109/TGRS.2014.2359951","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84920999915&doi=10.1109%2fTGRS.2014.2359951&partnerID=40&md5=828929b320d96fb5be93afd734ddb08c","The effective extraction of shape features is an important requirement for the accurate and efficient classification of terrestrial laser scanning (TLS) point clouds. However, the challenge of how to obtain robust and discriminative features from noisy and varying density TLS point clouds remains. This paper introduces a novel multiscale and hierarchical framework, which describes the classification of TLS point clouds of cluttered urban scenes. In this framework, we propose multiscale and hierarchical point clusters (MHPCs). In MHPCs, point clouds are first resampled into different scales. Then, the resampled data set of each scale is aggregated into several hierarchical point clusters, where the point cloud of all scales in each level is termed a point-cluster set. This representation not only accounts for the multiscale properties of point clouds but also well captures their hierarchical structures. Based on the MHPCs, novel features of point clusters are constructed by employing the latent Dirichlet allocation (LDA). An LDA model is trained according to a training set. The LDA model then extracts a set of latent topics, i.e., a feature of topics, for a point cluster. Finally, to apply the introduced features for point-cluster classification, we train an AdaBoost classifier in each point-cluster set and obtain the corresponding classifiers to separate the TLS point clouds with varying point density and data missing into semantic regions. Compared with other methods, our features achieve the best classification results for buildings, trees, people, and cars from TLS point clouds, particularly for small and moving objects, such as people and cars. © 1980-2012 IEEE."
"7003341789;6701797047;13404307100;","Colour indices for the detection and differentiation of cloud types in infra-red limb emission spectra",2004,"10.1016/S0273-1177(03)00585-4","https://www.scopus.com/inward/record.uri?eid=2-s2.0-2042419832&doi=10.1016%2fS0273-1177%2803%2900585-4&partnerID=40&md5=9bf52ff52deaa5c2479ce1723c5ea9e9","Simple radiance ratios have been used for the detection of clouds around the tropopause and in the winter polar stratosphere from the infra-red spectra of two remote sensing instruments, the cryogenic infrared spectrometers and telescopes for the atmosphere (CRISTA) flown on two space shuttle missions in 1994 and 1997 and the Michelson interferometer for passive atmospheric sounding (MIPAS) launched on ENVISAT in March 2002. This very successful approach was first applied to different wavelength regions of the CRISTA measurements and was then used as a pre-flight validation test for a cloud detection algorithm of the operational retrieval processor for MIPAS. Preliminary results are now available from the MIPAS instrument and are presented here. First, cloud top heights have been derived down to 12 km by the detection method and show quite reasonable results. In addition, modelled spectra and measurements show that the extension of the method to lower altitudes - potentially down to 6 km - should be possible. Second, the high spectral resolution of the MIPAS measurements allows in addition the detection of scattering effects in the spectra, which gives the future opportunity to retrieve information about the size of the scattering particles. In particular, an index has been developed which allows large particle clouds to be identified. Finally, investigations of CRISTA spectra have already shown that the differentiation of polar stratospheric cloud (PSC) types is possible based on their characteristic spectral features. Application of the differentiation method to the tropics shows no indication for clouds containing PSC-like nitric-acid-hydrate particles. © 2003 COSPAR. Published by Elsevier Ltd. All rights reserved."
"55972035800;55545672000;55613230864;24479005300;","Classification of airborne laser scanning data using JointBoost",2015,"10.1016/j.isprsjprs.2014.04.015","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84920926017&doi=10.1016%2fj.isprsjprs.2014.04.015&partnerID=40&md5=b39ad491412229187832e0b4b44d6439","The demands for automatic point cloud classification have dramatically increased with the wide-spread use of airborne LiDAR. Existing research has mainly concentrated on a few dominant objects such as terrain, buildings and vegetation. In addition to those key objects, this paper proposes a supervised classification method to identify other types of objects including power-lines and pylons from point clouds using a JointBoost classifier. The parameters for the learning model are estimated with various features computed based on the geometry and echo information of a LiDAR point cloud. In order to overcome the shortcomings stemming from the inclusion of bare ground data before classification, the proposed classifier directly distinguishes terrain using a feature step-off count. Feature selection is conducted using JointBoost to evaluate feature correlations thus improving both classification accuracy and operational efficiency. In this paper, the contextual constraints for objects extracted by graph-cut segmentation are used to optimize the initial classification results obtained by the JointBoost classifier. Our experimental results show that the step-off count significantly contributes to classification. Seventeen effective features are selected for the initial classification results using the JointBoost classifier. Our experiments indicate that the proposed features and method are effective for classification of airborne LiDAR data from complex scenarios. © 2014 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS)."
"6603354695;6603888005;7006892410;6506644813;","Mean map kernel methods for semisupervised cloud classification",2010,"10.1109/TGRS.2009.2026425","https://www.scopus.com/inward/record.uri?eid=2-s2.0-73249149045&doi=10.1109%2fTGRS.2009.2026425&partnerID=40&md5=06ba6528d9a0cf7982122cdf758fde1c","Remote sensing image classification constitutes a challenging problem since very few labeled pixels are typically available from the analyzed scene. In such situations, labeled data extracted from other images modeling similar problems might be used to improve the classification accuracy. However, when training and test samples follow even slightly different distributions, classification is very difficult. This problem is known as sample selection bias. In this paper, we propose a new method to combine labeled and unlabeled pixels to increase classification reliability and accuracy. A semisupervised support vector machine classifier based on the combination of clustering and the mean map kernel is proposed. The method reinforces samples in the same cluster belonging to the same class by combining sample and cluster similarities implicitly in the kernel space. A soft version of the method is also proposed where only the most reliable training samples, in terms of likelihood of the image data distribution, are used. Capabilities of the proposed method are illustrated in a cloud screening application using data from the MEdium Resolution Imaging Spectrometer (MERIS) instrument onboard the European Space Agency ENVISAT satellite. Cloud screening constitutes a clear example of sample selection bias since cloud features change to a great extent depending on the cloud type, thickness, transparency, height, and background. Good results are obtained and show that the method is particularly well suited for situations where the available labeled information does not adequately describe the classes in the test data. © 2009 IEEE."
"6603025800;6603631763;6701764148;","Daytime global cloud typing from AVHRR and VIIRS: Algorithm description, validation, and comparisons",2005,"10.1175/JAM2236.1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-24044524592&doi=10.1175%2fJAM2236.1&partnerID=40&md5=0006b05bd215b1b50447bf7eba87a107","Three multispectral algorithms for determining the cloud type of previously identified cloudy pixels during the daytime, using satellite imager data, are presented. Two algorithms were developed for use with 0.65-, 1.6-/3.75-, 10.8-, and 12.0-μm data from the Advanced Very High Resolution Radiometer (AVHRR) on board the National Oceanic and Atmospheric Administration (NOAA) operational polar-orbiting satellites. The AVHRR algorithms are identical except for the near-infrared data that are used. One algorithm uses AVHRR channel 3a (1.6 μm) reflectances, and the other uses AVHRR channel 3b (3.75 μm) reflectance estimates. Both of these algorithms are necessary because the AVHRRs on NOAA-15 through NOAA-17 have the capability to transmit either channel 3a or 3b data during the day, whereas all of the other AVHRRs on NOAA-7 through NOAA-14 can only transmit channel 3b data. The two AVHRR cloud-typing schemes are used operationally in NOAA's extended Clouds from AVHRR (CLAVR)-x processing system. The third algorithm utilizes additional spectral bands in the 1.38- and 8.5-μm regions of the spectrum that are available on the Moderate Resolution Imaging Spectroradiometer (MODIS) and will be available on the Visible-Infrared Imaging Radiometer Suite (VIIRS). The VIIRS will eventually replace the AVHRR on board the National Polar-Orbiting Operational Environmental Satellite System (NPOESS), which is currently scheduled to be launched in 2009. Five cloud-type categories are employed: warm liquid water, supercooled water-mixed phase, opaque ice, nonopaque high ice (cirrus), and cloud overlap (multiple cloud layers). Each algorithm was qualitatively evaluated through scene analysis and then validated against inferences of cloud type that were derived from ground-based observations of clouds at the three primary Atmospheric Radiation Measurement (ARM) Program sites to help to assess the potential continuity of a combined AVHRR channel 3a-AVHRR channel 3b-VIIRS cloud-type climatology. In this paper, ""validation"" is strictly defined as comparisons with ground-based estimates that are completely independent of the satellite retrievals. It was determined that the two AVHRR algorithms produce nearly identical results except for certain thin clouds and cloud edges. The AVHRR 3a algorithm tends to incorrectly classify the thin edges of some low- and midlevel clouds as cirrus and opaque ice more often than the AVHRR 3b algorithm. The additional techniques implemented in the VIIRS algorithm result in a significant improvement in the identification of cirrus clouds, cloud overlap, and overall phase identification of thin clouds, as compared with the capabilities of the AVHRR algorithms presented in this paper. © 2005 American Meteorological Society."
"7006508549;7103142686;7201914101;35453054300;","Impact of land use on Costa Rican tropical montane cloud forests: Sensitivity of cumulus cloud field characteristics to lowland deforestation",2003,"10.1029/2001jd001135","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0346461695&doi=10.1029%2f2001jd001135&partnerID=40&md5=9162992f23633e33d7a03cdee39320b4","Recent studies have shown that there has been a reduction in dry season moisture input from direct interception of cloud water and wind-blown mist at the lee edge of the Monteverde cloud forest, Costa Rica, since the mid 1970s. This reduction of moisture could be responsible for the population crashes of anurans observed in the region. It has been hypothesized that this behavior is a result of increases in cloud base height, linked to increased sea surface temperatures. In this study we present a complementary hypothesis, that deforestation upwind of the Monteverde cloud forest preserve is responsible for the observed changes in cloud base height. An automated cumulus cloud classification scheme extracts monthly spatial maps of the frequency of occurrence of cumulus cloudiness over Costa Rica from GOES 8 visible channel satellite imagery. We find that cumulus cloud formation in the morning hours over deforested regions is suppressed compared to forested areas. The degree of suppression appears to be related to the extent of deforestation. This difference in cloud formation between forested and deforested areas is a clear signal of land use change influencing the regional climate. Regional Atmospheric Modeling System numerical modeling simulations are used to explore the differences in cloud field characteristics over the lowland pasture and forest landscapes. Statistically significant differences in cloud base height and cloud thickness occur between the forest and pasture simulations. Clouds have higher base heights and are thinner over pasture landscapes than over forested ones. On the other hand, these simulations show no statistically significant differences in cloud top heights, cloud cover, mean cloud water mixing ratio, or cloud liquid water path between pasture and forest simulations. However, in the simulations there are enhanced sensible heat fluxes and reduced latent heat fluxes over pasture compared to forest. It is the drier and warmer air over pasture surfaces that results in the formation of elevated thinner clouds. This study suggests that deforestation results in warmer, drier air upwind of the Monteverde cloud forests and that this could influence the base height of orographic cloudbanks crucial to the region during the dry season."
"57202527939;7202162685;7409715209;","Classification of clouds over the western equatorial Pacific Ocean using combined infrared and microwave satellite data",1995,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-0029473549&partnerID=40&md5=262633d42c35d155e67396b5fdad6d8c","A new cloud classification scheme is presented that combines infrared and microwave satellite data. Because microwave radiation can penetrate deep into the cloud layer, this scheme is able to determine characteristics for both thin and deep clouds. Additionally, the new scheme can provide information on precipitation, which traditional infrared-visible cloud classification schemes have been unable to. The proposed cloud classification scheme utilizes the cloud top temperature obtained from infrared measurements and a microwave index that includes both emission and scattering signals. The classification scheme is validated by aircraft radar data obtained from Tropical Ocean-Global Atmosphere Coupled Ocean-Atmosphere Response Experiment. -from Authors"
"55800795200;53165154100;53163419700;57213986355;23491426900;6507785309;","A method for cloud detection and opacity classification based on ground based sky imagery",2012,"10.5194/amt-5-2881-2012","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84880526065&doi=10.5194%2famt-5-2881-2012&partnerID=40&md5=c50299668a683d488f394b4e655f7fee","Digital images of the sky obtained using a total sky imager (TSI) are classified pixel by pixel into clear sky, optically thin and optically thick clouds. A new classification algorithm was developed that compares the pixel red-blue ratio (RBR) to the RBR of a clear sky library (CSL) generated from images captured on clear days. The difference, rather than the ratio, between pixel RBR and CSL RBR resulted in more accurate cloud classification. High correlation between TSI image RBR and aerosol optical depth (AOD) measured by an AERONET photometer was observed and motivated the addition of a haze correction factor (HCF) to the classification model to account for variations in AOD. Thresholds for clear and thick clouds were chosen based on a training image set and validated with set of manually annotated images. Misclassifications of clear and thick clouds into the opposite category were less than 1%. Thin clouds were classified with an accuracy of 60%. Accurate cloud detection and opacity classification techniques will improve the accuracy of short-term solar power forecasting. © 2012 Author(s)."
"55502994400;6602390932;6701608680;23011244600;8525148200;8215481100;7004486660;7801500046;7004022660;57209290505;7004459129;55784623400;","Inversion of tropospheric profiles of aerosol extinction and HCHO and NO2 mixing ratios from MAX-DOAS observations in Milano during the summer of 2003 and comparison with independent data sets",2011,"10.5194/amt-4-2685-2011","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84864529315&doi=10.5194%2famt-4-2685-2011&partnerID=40&md5=7ea94b8ff8a10e4dda135688c591bd1c","We present aerosol and trace gas profiles derived from MAX-DOAS observations. Our inversion scheme is based on simple profile parameterisations used as input for an atmospheric radiative transfer model (forward model). From a least squares fit of the forward model to the MAX-DOAS measurements, two profile parameters are retrieved including integrated quantities (aerosol optical depth or trace gas vertical column density), and parameters describing the height and shape of the respective profiles. From these results, the aerosol extinction and trace gas mixing ratios can also be calculated. We apply the profile inversion to MAX-DOAS observations during a measurement campaign in Milano, Italy, September 2003, which allowed simultaneous observations from three telescopes (directed to north, west, south). Profile inversions for aerosols and trace gases were possible on 23 days. Especially in the middle of the campaign (17-20 September 2003), enhanced values of aerosol optical depth and NO2 and HCHO mixing ratios were found. The retrieved layer heights were typically similar for HCHO and aerosols. For NO2, lower layer heights were found, which increased during the day. The MAX-DOAS inversion results are compared to independent measurements: (1) aerosol optical depth measured at an AERONET station at Ispra; (2) near-surface NO2 and HCHO (formaldehyde) mixing ratios measured by long path DOAS and Hantzsch instruments at Bresso; (3) vertical profiles of HCHO and aerosols measured by an ultra light aircraft. Depending on the viewing direction, the aerosol optical depths from MAX-DOAS are either smaller or larger than those from AERONET observations. Similar comparison results are found for the MAX-DOAS NO 2 mixing ratios versus long path DOAS measurements. In contrast, the MAX-DOAS HCHO mixing ratios are generally higher than those from long path DOAS or Hantzsch instruments. The comparison of the HCHO and aerosol profiles from the aircraft showed reasonable agreement with the respective MAX-DOAS layer heights. From the comparison of the results for the different telescopes, it was possible to investigate the internal consistency of the MAX-DOAS observations. As part of our study, a cloud classification algorithm was developed (based on the MAX-DOAS zenith viewing directions), and the effects of clouds on the profile inversion were investigated. Different effects of clouds on aerosols and trace gas retrievals were found: while the aerosol optical depth is systematically underestimated and the HCHO mixing ratio is systematically overestimated under cloudy conditions, the NO2 mixing ratios are only slightly affected. These findings are in basic agreement with radiative transfer simulations. © 2011 Author(s)."
"55499821700;57206332144;7202185413;7003289221;","Horizontal structure of marine boundary layer clouds from centimeter to kilometer scales",1999,"10.1029/1998JD200078","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0033608682&doi=10.1029%2f1998JD200078&partnerID=40&md5=71f1838ccac3896b69790bb4dbf1f96a","Horizontal transects of cloud liquid water content (LWC) measured at unprecedented 4-cm resolution are statistically analyzed scale-by-scale. The data were collected with a Particulate Volume Monitor (PVM) probe during the winter Southern Ocean Cloud EXperiment (SOCEX) on July 26, 1993, in a broken-stratocumulus/towering-cumulus cloud complex. Two scaling regimes are found in the sense that two distinct power laws, k-β, are needed to represent the wavenumber spectrum E(k) over the full range of scales r ≃ 1/k. Detailed numerical simulations show that the scale break at 2-5 m is not traceable to the normal variability of LWC in the PVM's instantaneous sampling volume (1.25 cm3) driven by Poissonian fluctuations of droplet number and size. The two regimes therefore differ physically. The non-Poissonian character of the small-scale LWC variability is consistent with a similar finding by Baker [1992] for droplet number concentration obtained from Forward Scattering Spectrometer Probe (FSSP) data: at scales of a few centimeters, spatial droplet distributions do not always follow a uniform Poisson law. With β = 0.9±0.1, the small-scale (8-12 cm ≤ r ≤ 2-5 m) regime is stationary; jumps in LWC are highly variable in size and rapidly cancel each other, leading to short-range correlations. By contrast, the large-scale (5 m ≤ r ≤ 2 km) variability with β = 1.6±0.1 is nonstationary: jumps are generally quite small, conveying a degree of pixel-to-pixel continuity and thus building up long-range correlations in the low-pass filtered signal. The large-scale structure of the complex SOCEX cloud system proves to be multifractal, meaning that large jumps do occur on an intermittent basis, that is, on a sparse fractal subset of space. Low-order, hence more robust, multifractal properties of the SOCEX clouds are remarkably similar to those of their First ISCCP Regional Experiment (FIRE) and Atlantic Stratocumulus Transition EXperiment (ASTEX) counterparts, and also to those of passive scalars in fully developed turbulence. This is indicative of a remarkable similarity in the microphysical and macrophysical processes that determine cloud structure in the marine boundary layer at very remote locales, especially since the particular SOCEX cloud system investigated here was rather atypical. Interesting differences are also found: in the scaling ranges on the one hand, and in higher-order moments on the other hand. Finally, we discuss cloud-radiative effects of the large-and small-scale variabilities. Copyright 1999 by the American Geophysical Union."
"7004384155;7004540083;6602504047;7003597653;7202746102;","Clouds as seen by satellite sounders (3I) and imagers (ISCCP). Part I: Evaluation of cloud parameters",1999,"10.1175/1520-0442(1999)012<2189:casbss>2.0.co;2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0033172629&doi=10.1175%2f1520-0442%281999%29012%3c2189%3acasbss%3e2.0.co%3b2&partnerID=40&md5=2b51d1f93f0451f05e7b5ab23fbf61be","The improved initialization inversion (3I) algorithms convert TIROS-N Operational Vertical Sounder observations from the National Oceanic and Atmospheric Administration (NOAA) polar-orbiting environmental satellites into atmospheric temperature and water vapor profiles, together with cloud and surface properties. Their relatively good spectral resolution and coverage make IR sounders a very useful tool for the determination of cloud properties both day and night. The iterative process of detailed comparisons between cloud parameters obtained from this global dataset, which is available in the framework of the NOAA-National Aeronautics and Space Administration Pathfinder Program, with time-space-collocated observations of clouds from the recently reprocessed International Satellite Cloud Climatology Project (ISCCP) dataset has led to an improved 3I cloud analysis scheme based on a weighted-γ2 method described in the second article of this series. This process also provides a first evaluation of the ISCCP reanalysis. The new 3I cloud scheme obtains cloud properties very similar to those from ISCCP for homogeneous cloud scenes. Improvement is especially notable in the stratocumulus regimes where the new 3I scheme detects much more of the low-level cloudiness. Remaining discrepancies in cloud classification can now be explained by differences in cloud detection sensitivity, differences in temperature profiles used, and inhomogeneous or partly cloudy fields. Cirrus cloud identification during the daytime in the recent ISCCP dataset is improved relative to the first version of ISCCP, but is still an underestimate. At night only multispectral IR analyses like 3I can provide cirrus information. The reprocessed ISCCP dataset also shows considerable improvement in cloud cover at higher latitudes. Differences in 3I and ISCCP summertime cloud cover over deserts may be caused by different sensitivities to dust storms."
"7003663305;","A ground-based multisensor cloud phase classifier",2007,"10.1029/2007GL031008","https://www.scopus.com/inward/record.uri?eid=2-s2.0-38949149664&doi=10.1029%2f2007GL031008&partnerID=40&md5=8e413fa26373961f0e3b8b57393675fd","A method for classifying cloud phase from a suite of ground-based sensors is outlined. The method exploits the complementary strengths of cloud radar, depolarization lidar, microwave radiometer, and temperature soundings to classify clouds observed in the vertical column as ice, snow, mixed-phase, liquid, drizzle, rain, or aerosol. Although the classification has been specifically designed for observations of Arctic clouds, the general framework is applicable to other locations with minor modifications. An example classification demonstrates the application to actual measurements. Copyright 2007 by the American Geophysical Union."
"7003663305;6701764148;7003821079;","Arctic cloud microphysics retrievals from surface-based remote sensors at SHEBA",2005,"10.1175/JAM2297.1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-28144457011&doi=10.1175%2fJAM2297.1&partnerID=40&md5=3534b139d6d4a5195a32de36537ebdaf","An operational suite of ground-based, remote sensing retrievals for producing cloud microphysical properties is described, assessed, and applied to 1 yr of observations in the Arctic. All measurements were made in support of the Surface Heat Budget of the Arctic (SHEBA) program and First International Satellite Cloud Climatology Project Regional Experiment (FIRE) Arctic Clouds Experiment (ACE) in 1997-98. Retrieval techniques and cloud-type classifications are based on measurements from a vertically pointing 35-GHz Doppler radar, microwave and infrared radiometers, and radiosondes. The retrieval methods are assessed using aircraft in situ measurements from a limited set of case studies and by intercomparison of multiple retrievals for the same parameters. In all-liquid clouds, retrieved droplet effective radii Re have an uncertainty of up to 32% and liquid water contents (LWC) have an uncertainty of 49%-72%. In all-ice clouds, ice particle mean sizes Dmean can be retrieved with an uncertainty of 26%-46% while retrieved ice water contents (IWC) have an uncertainty of 62%-100%. In general, radar-only, regionally tuned empirical power-law retrievals were best suited among the tested retrieval algorithms for operational cloud monitoring at SHEBA because of their wide applicability, ease of use, and reasonable statistical accuracy. More complex multisensor techniques provided a moderate improvement in accuracy for specific case studies and were useful for deriving location-specific coefficients for the empirical retrievals but were not as frequently applicable as the single sensor methods because of various limitations. During the yearlong SHEBA program, all-liquid clouds were identified 19% of the time and were characterized by an annual average droplet Re of 6.5 μm, LWC of 0.10 g m -3, and liquid water path of 45 g m-2. All-ice clouds were identified 38% of the time with an annual average particle Dmean of 73 μm, IWC of 0.014 g m-3, and ice water path of 30 g m-2. © 2005 American Meteorological Society."
"8942000500;7005989723;","Cloud detection in Landsat imagery of ice sheets using shadow matching technique and automatic normalized difference snow index threshold value decision",2004,"10.1016/j.rse.2004.03.007","https://www.scopus.com/inward/record.uri?eid=2-s2.0-2542641611&doi=10.1016%2fj.rse.2004.03.007&partnerID=40&md5=767e0bd9927decfbc67989570f6358f1","This work presents a new algorithm designed to detect clouds in satellite visible and infrared (IR) imagery of ice sheets. The approach identifies possible cloud pixels through the use of the normalized difference snow index (NDSI). Possible cloud pixels are grown into regions and edges are determined. Possible cloud edges are then matched with possible cloud shadow regions using knowledge of the solar illumination azimuth. A scoring index quantifies the quality of each match resulting in a classified image. The best value of the NDSI threshold is shown to vary significantly, forcing the algorithm to be iterated through many threshold values. Computational efficiency is achieved by using sub-sampled images with only minor degradation in cloud-detection performance. The algorithm detects all clouds in each of eight test Landsat-7 images and makes no incorrect cloud classifications. © 2004 Elsevier Inc. All rights reserved."
"35569803200;7202632582;6603925960;8680433600;6701422868;22970696400;6701607011;","Cloud thermodynamical phase classification from the POLDER spaceborne instrument",2000,"10.1029/1999JD901183","https://www.scopus.com/inward/record.uri?eid=2-s2.0-16644378345&doi=10.1029%2f1999JD901183&partnerID=40&md5=cec106d7abd7238772c3c88a0e061661","Cloud phase recognition is important for cloud studies. Ice crystals correspond to physical process and properties that differ from those of liquid water drops. The angular polarization signature is a good mean to discriminate between spherical and nonspherical particles (liquid and ice phase, respectively). POLDER (Polarization and Directionality of Earth Reflectances) has been launched on the Japanese ADEOS platform in August 1996. Because of its multidirectional, multispectral, and multipolarization capabilities this new radiometer gives useful information on clouds and their influence on radiation in the shortwave range. The POLDER bidirectional observation capability provides the polarization signatures within a large range of scattering angles in three spectral bands centered on 0.443, 0.670, and 0.865 μm with a spatial resolution of 6.2 km x 6.2 km. These original features allow to obtain some information both on cloud thermodynamic phase and on cloud microphysics (size/shape). According to POLDER airborne observations, liquid cloud droplets exhibit very specific polarization features of a rainbow for scattering angles near 140°. Conversely, theoretical studies of scattering by various crystalline particles and also airborne measurements show that the rainbow characteristics disappear as soon as the particles depart from the spherical shape. In the paper the POLDER algorithm for cloud phase classification is presented, as well as the physical principle of this algorithm. Results derived from the POLDER spaceborne version are also presented and compared with lidar ground-based observations and satellite cloud classification. This cloud phase classification method is shown to be reliable. The major limitation appears when thin cirrus clouds overlap the liquid cloud layer. In this case, if the cirrus optical thickness is smaller than 2, the liquid phase may be retrieved. Otherwise, the ice phase is correctly detected as long as cloud detection works. Copyright 2000 by the American Geophysical Union."
"53264697700;6506388437;55955024200;","CONDITIONAL RANDOM FIELDS for LIDAR POINT CLOUD CLASSIFICATION in COMPLEX URBAN AREAS",2012,"10.5194/isprsannals-I-3-263-2012","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85041430565&doi=10.5194%2fisprsannals-I-3-263-2012&partnerID=40&md5=e4a25df6177dca6a173b1c8e642edf34","In this paper, we investigate the potential of a Conditional Random Field (CRF) approach for the classification of an airborne LiDAR (Light Detection And Ranging) point cloud. This method enables the incorporation of contextual information and learning of specific relations of object classes within a training step. Thus, it is a powerful approach for obtaining reliable results even in complex urban scenes. Geometrical features as well as an intensity value are used to distinguish the five object classes <i>building</i>, <i>low</i> <i>vegetation</i>, <i>tree</i>, <i>natural ground</i>, and <i>asphalt ground</i>. The performance of our method is evaluated on the dataset of Vaihingen, Germany, in the context of the 'ISPRS Test Project on Urban Classification and 3D Building Reconstruction'. Therefore, the results of the 3D classification were submitted as a 2D binary label image for a subset of two classes, namely <i>building</i> and <i>tree</i>."
"24079858400;7004264889;24080192300;55955024200;","Aspects of generating precise digital terrain models in the Wadden Sea from lidar-water classification and structure line extraction",2008,"10.1016/j.isprsjprs.2008.02.002","https://www.scopus.com/inward/record.uri?eid=2-s2.0-51249084982&doi=10.1016%2fj.isprsjprs.2008.02.002&partnerID=40&md5=74dad28dec46d281a7f6a320daee1659","The Wadden Sea is a unique habitat formed by the strong influence of tidal currents. Twice a day the area is flooded and falls dry afterwards. Due to the force of tidal streams, strong morphologic changes occur frequently. In order to monitor these changes, high precision digital terrain models (DTMs) are required. Lidar proved to be an adequate technique to deliver highly accurate 3D mass points of the surface and dense spacing. However, water often remains within tidal channels and depressions even at low tide, and near infrared lidar is not able to penetrate the water leading to a point cloud which contains surface and water points. Thus, the standard processing workflow for DTM generation from lidar is not suited for the Wadden Sea. In this article, a new workflow is proposed for DTM generation from lidar data in the Wadden Sea. Two major building blocks of this workflow, namely classification of the water points and structure line detection, are presented in detail. For both tasks suitable algorithms were developed tailored to meet special requirements of mudflat. Lidar measurements from water surfaces are detected by a supervised fuzzy classification using the features height, intensity, and 2D point density. Structure lines are derived through a piecewise reconstruction of the surface from the lidar data with a hyperbolic tangent function. The obtained results show that both methods considerably improve the accuracy of DTMs from lidar data. © 2008 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS)."
"26643530600;7102063963;57209184699;7201607592;7101677832;","High-spatial-resolution surface and cloud-type classification from MODIS multispectral band measurements",2003,"10.1175/1520-0450(2003)042<0204:HSRSAC>2.0.CO;2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0141649207&doi=10.1175%2f1520-0450%282003%29042%3c0204%3aHSRSAC%3e2.0.CO%3b2&partnerID=40&md5=e58271138dfc3a9e27373dbda1d7749d","A method for automated classification of surface and cloud types using Moderate Resolution Imaging Spectroradiometer (MODIS) radiance measurements has been developed. The MODIS cloud mask is used to define the training sets. Surface and cloud-type classification is based on the maximum likelihood (ML) classification method. Initial classification results define training sets for subsequent iterations. Iterations end when the number of pixels switching classes becomes smaller than a predetermined number or when other criteria are met. The mean vector in the spectral and spatial domain within a class is used for class identification, and a final 1-km-resolution classification mask is generated for such a field of view in a MODIS granule. This automated classification refines the output of the cloud mask algorithm and enables further applications such as clear atmospheric profile or cloud parameter retrievals from MODIS and Atmospheric Infrared Sounder (AIRS) radiance measurements. The advantages of this method are that the automated surface and cloud-type classifications are independent of radiance or brightness temperature threshold criteria, and that the interpretation of each class is based on the radiative spectral characteristics of different classes. This paper describes the ML classification algorithm and presents daytime MODIS classification results. The classification results are compared with the MODIS cloud mask, visible images, infrared window images, and other observations for an initial validation."
"7401984344;7004442182;7006689582;","Recent changes in cloud-type frequency and inferred increases in convection over the United States and the former USSR",2001,"10.1175/1520-0442(2001)014<1864:RCICTF>2.0.CO;2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0035870117&doi=10.1175%2f1520-0442%282001%29014%3c1864%3aRCICTF%3e2.0.CO%3b2&partnerID=40&md5=43133ea444e6216c0810051834ae2ebe","Significant changes and a general redistribution in the frequencies of various cloud types have been observed during the past 40-50 years over the midlatitude land areas of the Northern Hemisphere. This is evident for North America and northern Eurasia in the daytime synoptic data of the United States and the former Soviet Union (FUSSR). An abrupt increase prior to the 1960s largely contributed to the upward trend in the frequency of convective clouds over both regions, particularly in the warm season. However, over both regions during the intermediate seasons and during the winter season over the FUSSR, the frequencies of convective clouds still showed gradual increase after the 1960s. The increase in the frequency of convective clouds has been accompanied by increases in the frequency of observation of high-level cloudiness (at elevations above 6 km) and heavy precipitation. Low cloudiness (stratiform types) has decreased over the FUSSR but increased over the contiguous United States. The latter increase was due to an increase in the frequency of stratocumulus clouds, while the frequency of stratus clouds has decreased. Generally, it appears that during the post-World War II period over the FUSSR high cloud-type frequencies increased and low cloudiness decreased with a relatively small change (increase) in total cloud cover, while over the United States cloud cover has increased at both low and high levels. The analyses of cloudiness information from the United States and the FUSSR reveal noticeable differences in definitions and observational practices that affect the estimates of climatology and interpretation of the results presented here in terms of changes of convective activity and its relation to precipitation in these two regions of Eurasia and North America."
"55720588700;7405614202;24071119300;7403931916;7004697990;7402516470;6602636483;57207384828;7102063963;","Retrieval of cloud microphysical properties from MODIS and AIRS",2005,"10.1175/JAM2281.1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-20444473070&doi=10.1175%2fJAM2281.1&partnerID=40&md5=4bdfa19e8740b755f98b952bd9fe9b8b","The Moderate Resolution Imaging Spectroradiometer (MODIS) and the Atmospheric Infrared Sounder (AIRS) measurements from the NASA Earth Observing System Aqua satellite enable global monitoring of the distribution of clouds during day and night. The MODIS is able to provide a high-spatial-resolution (1-5 km) cloud mask, cloud classification mask, cloud-phase mask, cloud-top pressure (CTP), and effective cloud amount during both the daytime and the nighttime, as well as cloud particle size (CPS) and cloud optical thickness (COT) at 0.55 μm during the daytime. The AIRS high-spectral-resolution measurements reveal cloud properties with coarser spatial resolution (13.5 km at nadir). Combined, MODIS and AIRS provide cloud microphysical properties during both the daytime and nighttime. A fast cloudy radiative transfer model for AIRS that accounts for cloud scattering and absorption is described in this paper. One-dimensional variational (IDVAR) and minimum-residual (MR) methods are used to retrieve the CPS and COT from AIRS longwave window region (790-970 cm-1 or 10.31-12.66 μm, and 1050-1130 cm-1 or 8.85-9.52 μm) cloudy radiance measurements. In both 1DVAR and MR procedures, the CTP is derived from the AIRS radiances of carbon dioxide channels while the cloud-phase information is derived from the collocated MODIS 1-km phase mask for AIRS CPS and COT retrievals. In addition, the collocated 1-km MODIS cloud mask refines the AIRS cloud detection in both lDVAR and MR procedures. The atmospheric temperature profile, moisture profile, and surface skin temperature used in the AIRS cloud retrieval processing are from the European Centre for Medium-Range Weather Forecasts forecast analysis. The results from 1DVAR are compared with the operational MODIS products and MR cloud microphysical property retrieval. A Hurricane Isabel case study shows that 1DVAR retrievals have a high correlation with either the operational MODIS cloud products or MR cloud property retrievals. lDVAR provides an efficient way for cloud microphysical property retrieval during the daytime, and MR provides the cloud microphysical property retrievals during both the daytime and nighttime. © 2005 American Meteorological Society."
"7201826462;6507014384;57190691771;7201914101;","Automated cloud classification of global AVHRR data using a fuzzy logic approach",1997,"10.1175/1520-0450(1997)036<1519:ACCOGA>2.0.CO;2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0031401993&doi=10.1175%2f1520-0450%281997%29036%3c1519%3aACCOGA%3e2.0.CO%3b2&partnerID=40&md5=7b17965781e8c26f20eb3da0ab9b4ef3","A fuzzy logic classification (FLC) methodology is proposed to achieve the two goals of this paper: 1) to discriminate between clear sky and clouds in a 32 × 32 pixel array, or sample, of 1.1-km Advanced Very High Resolution Radiometer (AVHRR) data, and 2) if clouds are present, to discriminate between single-layered and multilayered clouds within the sample. To achieve these goals, eight FLC modules are derived that are based broadly on airmass type and surface type (land or water): equatorial over land, marine tropical over land, marine tropical/equatorial over water, continental tropical over land, marine polar over land, marine polar over water, continental polar over land, and continental polar/arctic over water. Derivation of airmass type is performed using gridded analyses provided by the National Centers for Environmental Prediction. The training and testing data used by the FLC are collected from more than 150 daytime AVHRR local area coverage scenes recorded between 1991 and 1994 over all seasons and over all continents and oceans. A total of 190 textural and spectral features are computed from the AVHRR data. A forward feature selection method is implemented to reduce the number of features used to discriminate between classes in each FLC module. The number of features selected ranges from 13 (marine tropical over land) to 24 (marine tropical/equatorial over water). An estimate of the classifier accuracy is determined using the hold-one-out method in which the classifier is trained with all but one of the data samples; the classifier is applied subsequently to the remaining sample. The overall accuracies of the eight classification modules are calculated by dividing the number of correctly classified samples by the total number of manually labeled samples of clear-sky and single-layer clouds. Individual module classification accuracies are as follows: equatorial over land (86.2%), marine tropical over land (85.6%). marine tropical/equatorial over water (88.6%), continental tropical over land (87.4%), marine polar over land (86.8%), marine polar over water (84.8%), continental polar over land (91.1%), and continental polar/arctic over water (89.8%). Single-level cloud samples misclassified as multilayered clouds range between 0.5% (continental polar over land) and 3.4% (marine polar over land) for the eight airmass modules. Classification accuracies for a set of labeled multilayered cloud samples range between 64% and 81% for six of the eight airmass modules (excluded are the continental polar over land and continental polar/arctic over water modules, for which multilayered cloud samples are difficult to find). The results indicate that the FLC has an encouraging ability to distinguish between single-level and multilayered clouds."
"52364778400;8255132900;6506700491;57191510259;","Neural networks and support vector machine algorithms for automatic cloud classification of whole-sky ground-based images",2015,"10.1109/LGRS.2014.2356616","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84907992182&doi=10.1109%2fLGRS.2014.2356616&partnerID=40&md5=f11b6a978e842c7cd619d6b9b36ffdb6","Clouds are one of the most important meteorological phenomena affecting the Earth radiation balance. The increasing development of whole-sky images enables temporal and spatial high-resolution sky observations and provides the possibility to understand and quantify cloud effects more accurately. In this letter, an attempt has been made to examine the machine learning [multilayer perceptron (MLP) neural networks and support vector machine (SVM)] capabilities for automatic cloud detection in whole-sky images. The approaches have been tested on a significant number of whole-sky images (containing a variety of cloud overages in different seasons and at different daytimes) from Vigna di Valle and Tor Vergata test sites, located near Rome. The pixel values of red, green, and blue bands of the images have been used as inputs of the mentionedmodels, while the outputs provided classified pixels in terms of cloud coverage or others (cloud-free pixels and sun). For the test data set, the overall accuracies of 95.07%, with a standard deviation of 3.37, and 93.66%, with a standard deviation of 4.45, have been obtained from MLP neural networks and SVM models, respectively. Although the two approaches generally generate similar accuracies, the MLP neural networks gave a better performance in some specific cases where the SVM generates poor accuracy. © 2014 IEEE."
"6701705818;7102540239;","Estimating cloud type from pyranometer observations",1999,"10.1175/1520-0450(1999)038<0132:ECTFPO>2.0.CO;2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0032599686&doi=10.1175%2f1520-0450%281999%29038%3c0132%3aECTFPO%3e2.0.CO%3b2&partnerID=40&md5=12d57509b3b5f4875a923561fdca8d69","In this paper the authors evaluate an inexpensive and automatable method to estimate cloud type at a given location during daylight hours using the time series of irradiance from a pyranometer. The motivation for this investigation is to provide ground-based estimates of cloud type at locations where there are no human observations of sky condition. A pyranometer naturally measures the effect of intervening clouds along the solar beam path to the sensor. Because a daily time series of irradiance is nonstationary, it is appropriately scaled to yield a stationary time series. From the latter, the standard deviation and ratio of observed irradiance to clear-sky irradiance derived from a 21-min moving window are related to one of the following cloud types or conditions: cirrus, cumulus, cirrus and cumulus, stratus, precipitation or fog, no clouds, and other clouds. Comparisons with human observations at the Department of Energy Atmospheric Radiation Measurement Calibration and Radiation Testbed site in northern Oklahoma show that the pyranometer method and human observations are in agreement about 45% of the time. Many of the differences can be attributed to two factors: 1) the pyranometer method is weighted toward clouds crossing the sun's path, while the human observer can view clouds over the entire sky, and 2) the presence of aerosols causes the pyranometer to overestimate the occurrence of cirrus and cirrus plus cumulus. When attenuation of the solar beam by aerosols is negligible or can be accounted for, the pyranometer method should be especially useful for cloud-type assessment where no other sky observations are available."
"36678944300;56158622800;7006393267;57199033967;56487065200;56434996400;","A global survey of cloud overlap based on CALIPSO and CloudSat measurements",2015,"10.5194/acp-15-519-2015","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84921416854&doi=10.5194%2facp-15-519-2015&partnerID=40&md5=6a20941432acc73ce0292d3cf9fabe2d","Using 2B-CLDCLASS-LIDAR (radar-lidar) cloud classification and 2B-FLXHR-LIDAR radiation products from CloudSat over 4 years, this study evaluates the co-occurrence frequencies of different cloud types, analyzes their along-track horizontal scales and cloud radiative effects (CREs), and utilizes the vertical distributions of cloud types to evaluate cloud-overlap assumptions. The statistical results show that high clouds, altostratus (As), altocumulus (Ac) and cumulus (Cu) tend to coexist with other cloud types. However, stratus (St) (or stratocumulus, Sc), nimbostratus (Ns) and convective clouds are much more likely to exhibit individual features than other cloud types. On average, altostratus-over-stratus/stratocumulus cloud systems have a maximum horizontal scale of 17.4 km, with a standard deviation of 23.5 km. Altocumulus-over-cumulus cloud types have a minimum scale of 2.8 km, with a standard deviation of 3.1 km. By considering the weight of each multilayered cloud type, we find that the global mean instantaneous net CREs of multilayered cloud systems during the daytime are approximately -41.3 and -50.2 W m-2, which account for 40.1 and 42.3% of the global mean total net CREs at the top of the atmosphere (TOA) and at the surface, respectively. The radiative contributions of high-over-altocumulus and high-over-stratus/stratocumulus (or cumulus) in the all multilayered cloud systems are dominant due to their frequency. Considering the overlap of cloud types, the cloud fraction based on the random overlap assumption is underestimated over vast oceans, except in the west-central Pacific Ocean warm pool. Obvious overestimations mainly occur over tropical and subtropical land masses. In view of a lower degree of overlap than that predicted by the random overlap assumption to occur over the vast ocean, particularly poleward of 40° S, the study therefore suggests that a linear combination of minimum and random overlap assumptions may further improve the predictions of actual cloud fractions for multilayered cloud types (e.g., As + St/Sc and Ac + St/Sc) over the Southern Ocean. The establishment of a statistical relationship between multilayered cloud types and the environmental conditions (e.g., atmospheric vertical motion, convective stability and wind shear) would be useful for parameterization design of cloud overlap in numerical models. © Author(s) 2015."
"6603316080;","Pattern recognition techniques for the identification of cloud and cloud systems",1995,"10.1002/met.5060020309","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84979391449&doi=10.1002%2fmet.5060020309&partnerID=40&md5=e08edbcfd034782ea4e739ee6b105297","A wealth of often under‐used information is present in visible and infrared meteorological satellite imagery, in the form of cloud size, shape, texture and context. In an effort to provide an objective system that can identify meteorological objects over a range of scales, a study of currently available pattern recognition techniques has been undertaken. These techniques, which include supervised and unsupervised classification, image segmentation and scale context, are reviewed, together with a method for selecting the optimal image characteristics for the application of interest. This paper also considers a number of meteorological applications where these techniques can be used, such as cloud classification for operational or climatological purposes and the improvement of numerical weather prediction models. The result of the study has been the design of a synoptic‐scale object recognition system based on three layers of artificial neural networks, each operating at a different scale in a bottom‐up approach. Copyright © 1995 Royal Meteorological Society"
"7004315232;25652717800;6602136577;","Comparison of marine stratocumulus cloud top heights in the southeastern Pacific retrieved from satellites with coincident ship-based observations",2008,"10.1029/2008JD009975","https://www.scopus.com/inward/record.uri?eid=2-s2.0-56549117983&doi=10.1029%2f2008JD009975&partnerID=40&md5=06cbe27d4020cdc379ea0de8bb6fe3fa","In order to better understand the general problem of satellite cloud top height retrievals for low clouds, observations made by NOAA research vessels in the stratocumulus region in the southeastern Pacific during cruises in 2001 and 2003 to 2006 were matched with near-coincident retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Multiangle Imaging SpectroRadiometer (MISR) instruments on the Terra satellite, along with a limited set of ISCCP 30-km (DX) retrievals. The ISCCP cloud top heights, determined from the cloud top pressures, were found to be biased high by between 1400 and 2000 m within the limited comparison data set. Like the International Satellite Cloud Climatology Project (ISCCP) results, the MODIS retrievals were biased high by more than 2000 m, while the MISR retrievals had errors on the order of 230 to 420 m, with the wind corrected heights having almost no bias. The extremely large bias in the ISCCP and MODIS retrievals was traced to their reliance on low-resolution observations or models of the atmospheric temperature structure. Cloud top height retrievals based on satellite cloud top temperatures and a constant atmospheric lapse rate agreed substantially better with the ship-based measurements. Copyright 2008 by the American Geophysical Union."
"7004011998;8632797000;36069144100;8747183100;36605450500;7005523706;","Hydrologic evaluation of rainfall estimates from radar, satellite, gauge, and combinations on Ft. Cobb basin, Oklahoma",2011,"10.1175/2011JHM1287.1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-79960276322&doi=10.1175%2f2011JHM1287.1&partnerID=40&md5=fe6a99a707f8fca11efda74d51d5543e","This study evaluates rainfall estimates from the Next Generation Weather Radar (NEXRAD), operational rain gauges, Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Cloud Classification System (PERSIANN-CCS) in the context as inputs to a calibrated, distributed hydrologic model. A high-density Micronet of rain gauges on the 342-km 2 Ft. Cobb basin in Oklahoma was used as reference rainfall to calibrate the National Weather Service's (NWS) Hydrology Laboratory Research Distributed Hydrologic Model (HL-RDHM) at 4-km/l-h and 0.25°/3-h resolutions. The unadjusted radar product was the overall worst product, while the stage IV radar product with hourly rain gauge adjustment had the best hydrologic skill with a Micronet relative efficiency score of -0.5, only slightly worse than the reference simulation forced by Micronet rainfall. Simulations from TRMM-3B42RT were better than PERSIANNCCS- RT (a real-time version of PERSIANN-CSS) and equivalent to those from the operational rain gauge network. The high degree of hydrologic skill with TRMM-3B42RT forcing was only achievable when the model was calibrated atTRMM's 0.25°/3-h resolution, thus highlighting the importance of considering rainfall product resolution during model calibration. © 2011 American Meteorological Society."
"56427818500;6602731047;14122837900;","Cloud classification in a mediterranean location using radiation data and sky images",2011,"10.1016/j.energy.2011.04.043","https://www.scopus.com/inward/record.uri?eid=2-s2.0-79959376881&doi=10.1016%2fj.energy.2011.04.043&partnerID=40&md5=845386d0c7ff00b9688d4b41aab92b29","Knowledge regarding the solar radiation reaching the earth's surface and its geographical distribution is very important for the use of solar energy as a resource to produce electricity. Therefore, a proper assessment of available solar resource is particularly important to determine the placement and operation of solar thermal power plants. To perform this analysis correctly, it is necessary to determine the main factors influencing the radiation reaching the earth's surface, such as the earth's geometry, terrain, and atmospheric attenuation by gases, particles and clouds. Among these factors, it is important to emphasise the role of clouds as the main attenuating factor of radiation. Information about the amount and type of clouds present in the sky is therefore necessary to analyse both their attenuation levels and the prevalence of different sky conditions. Cloud cover is characterised according to attenuation levels, using the beam transmittance (kb, ratio of direct radiation incident on the surface to the extraterrestrial solar radiation) and hemispherical sky images. An analysis of the frequency and duration of each type of cloud cover blocking the sun's disk is also performed. Results show prevailing sky situations that make the studied area very suitable for the use of solar energy systems. © 2011 Elsevier Ltd."
"8629713500;7401796996;7006783796;6506582181;","A 10 year climatology of cloud fraction and vertical distribution derived from both surface and GOES observations over the DOE ARM SPG site",2010,"10.1029/2009JD012800","https://www.scopus.com/inward/record.uri?eid=2-s2.0-77954523639&doi=10.1029%2f2009JD012800&partnerID=40&md5=cdd45e36a17921032c5d116bca077c8f","Analysis of one decade of radar-lidar and Geostationary Operational Environmental Satellite (GOES) observations at the Department of Energy (DOE) Atmospheric Radiation Measurement Program (ARM) Southern Great Plains (SGP) site reveals that there is excellent agreement in the long-term mean cloud fractions (CFs) derived from the surface and GOES data, and the CF is independent of temporal resolution and spatial scales for grid boxes of size 0.5° to 2.5°. When computed over a a 0.5 h (4 h) period, cloud frequency of occurrence (FREQ) and amount when present (AWP) derived from the point surface data agree very well with the same quantities determined from GOES for a 0.5° (2.5°) region centered on the DOE ARM SGP site. The values of FREQ (AWP) derived from the radar-lidar observations at a given altitude increase (decrease) as the averaging period increases from 5 min to 6 h. Similarly, CF at a given altitude increases as the vertical resolution increases from 90 to 1000 m. The profiles of CF have distinct bimodal vertical distributions, with a lower peak between 1 and 2 km and a higher one between 8 and 11 km. The 10 year mean total CF, 46.9%, varies seasonally from a summer minimum of 39.8% to a maximum of 54.6% during the winter. The annual mean CF is 1%-2% less than that from previous studies, ∼48%-49%, because fewer clouds occurred during 2005 and 2006, especially during winter. The differences in single-and multilayered CFs between this study and an earlier analysis can be explained by the different temporal resolutions used in the two studies, where single-layered CFs decrease but multilayered CFs increase from a 5 min resolution to a 1 h resolution. The vertical distribution of nighttime GOES high cloud tops agrees well with surface observations, but during the daytime, fewer high clouds are retrieved by the GOES analysis than seen from the surface observations. The FREQs for both daytime and nighttime GOES low cloud tops are significantly higher than surface observations, but the CFs are in good agreement. Copyright 2010 by the American Geophysical Union."
"7202162685;7402450123;7006312044;7005284577;7007175473;","Occurrence and characteristics of lower tropospheric ice crystals in the arctic",1990,"10.1002/joc.3370100708","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0025586561&doi=10.1002%2fjoc.3370100708&partnerID=40&md5=463a30c070feaf20b272aa2a99d17c01","This paper discusses the occurrence and characteristics of small ice crystals that are encountered in the lower troposphere of the Arctic during the cold half of the year, when large reductions in visibility are present. Because of the difficulties in observing these ice crystals, both from surface and satellite observations, and owing to the ambiguities of cloud classification in the polar regions, these ice crystals are not included in current cloud climatologies. A summary is presented of what is known about the Arctic ice crystal climatology, formation mechanisms, and physical properties. Data obtained from the University of Washington Convair C‐131A research aircraft during April 1983 and 1986 are presented, including the frequency of occurrence of ice crystals as a function of height, temperature, and relative humidity. Radiative transfer calculations were made using the observed ice crystal size distributions. The radiative transfer through the ice crystal layers is inferred to have a substantial impact on visibility in the Arctic, the vertical temperature structure of the Arctic troposphere, and on the surface energy balance. Copyright © 1990 John Wiley & Sons, Ltd"
"36105786800;7401526171;7005052907;24081550100;6507229952;57189710110;","Bias adjustment of satellite precipitation estimation using ground-based measurement: A case study evaluation over the southwestern United States",2009,"10.1175/2009JHM1099.1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-77952589110&doi=10.1175%2f2009JHM1099.1&partnerID=40&md5=d8c748c7a519bf9d719a8991c46cf7b4","Reliable precipitation measurement is a crucial component in hydrologic studies. Although satellite-based observation is able to provide spatial and temporal distribution of precipitation, the measurements tend to show systematic bias. This paper introduces a grid-based precipitation merging procedure in which satellite estimates from the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS) are adjusted based on the Climate Prediction Center (CPC) daily rain gauge analysis. To remove the bias, the hourly CCS estimates were spatially and temporally accumulated to the daily 1°×1° scale, the resolution of CPC rain gauge analysis. The daily CCS bias was then downscaled to the hourly temporal scale to correct hourly CCS estimates. The bias corrected CCS estimates are called the adjusted CCS (CCSA) product. With the adjustment from the gauge measurement, CCSA data have been generated to provide more reliable high temporal/spatial-resolution precipitation estimates. In the case study, the CCSA precipitation estimates from the proposed approach are compared against ground-based measurements in high-density gauge networks located in the southwestern United States. © 2009 American Meteorological Society."
"7005523706;7004114883;","Stratiform and convective classification of rainfall using SSM/I 85-GHz brightness temperature observations",1997,"10.1175/1520-0426(1997)014<0570:SACCOR>2.0.CO;2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0000204820&doi=10.1175%2f1520-0426%281997%29014%3c0570%3aSACCOR%3e2.0.CO%3b2&partnerID=40&md5=cf9cbfaad30bd31b376cfa6c0fc766e1","A better understanding of global climate calls for more accurate estimates of liquid and ice water content profiles of precipitating clouds and their associated latent heating profiles. Convective and stratiform precipitation regimes have different latent heating and therefore impact the earth's climate differently. Classification of clouds over oceans has traditionally been part of more general rainfall retrieval schemes. These schemes are based on individual or combined visible and infrared, and microwave satellite observations. However, none of these schemes report validations of their cloud classification with independent ground observations. The objective of this study is to develop a scheme to classify convective and stratiform precipitating clouds using satellite brightness temperature observations. The proposed scheme probabilistically relates a quantity called variability index (VI) to the stratiform fractional precipitation coverage over the satellite field of view (FOV). The VI for a satellite pixel is the mean absolute 85-GHz brightness temperature difference between the pixel and the eight surrounding neighbor pixels. The classification scheme has been applied to four different rainfall regimes. All four regimes show that the frequency of stratiform rainfall in the satellite FOV increases as the satellite-based VI decreases. The results of this study demonstrate that the satellite-based VI is consistently related to the probability of occurrence of three classes (0%-40%, 40%-70%, and 70%-100%) of FOV stratiform coverage."
"57069455200;36064917000;55500134600;7005906188;55972035800;55545672000;37011423400;55986579100;","A Multilevel Point-Cluster-Based Discriminative Feature for ALS Point Cloud Classification",2016,"10.1109/TGRS.2016.2514508","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84955143082&doi=10.1109%2fTGRS.2016.2514508&partnerID=40&md5=f7231070671a7122daf33e184b907730","Point cloud classification plays a critical role in point cloud processing and analysis. Accurately classifying objects on the ground in urban environments from airborne laser scanning (ALS) point clouds is a challenge because of their large variety, complex geometries, and visual appearances. In this paper, a novel framework is presented for effectively extracting the shape features of objects from an ALS point cloud, and then, it is used to classify large and small objects in a point cloud. In the framework, the point cloud is split into hierarchical clusters of different sizes based on a natural exponential function threshold. Then, to take advantage of hierarchical point cluster correlations, latent Dirichlet allocation and sparse coding are jointly performed to extract and encode the shape features of the multilevel point clusters. The features at different levels are used to capture information on the shapes of objects of different sizes. This way, robust and discriminative shape features of the objects can be identified, and thus, the precision of the classification is significantly improved, particularly for small objects. © 2016 IEEE."
"14043813400;6603672796;7101677832;","Cloud classification of satellite radiance data by multicategory support vector machines",2004,"10.1175/1520-0426(2004)021<0159:CCOSRD>2.0.CO;2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-1842731703&doi=10.1175%2f1520-0426%282004%29021%3c0159%3aCCOSRD%3e2.0.CO%3b2&partnerID=40&md5=b0fc20d5ab0b0f27e38cb4e8ae83aa7f","Two-category support vector machines (SVMs) have become very popular in the machine learning community for classification problems and have recently been shown to have good optimality properties for classification purposes. Treating multicategory problems as a series of binary problems is common in the SVM paradigm. However, this approach may fail under a variety of circumstances. The multicategory support vector machine (MSVM), which extends the binary SVM to the multicategory case in a symmetric way, and has good theoretical properties, has recently been proposed. The proposed MSVM in addition provides a unifying framework when there are either equal or unequal misclassification costs, and when there is a possibly nonrepresentative training set. Illustrated herein is the potential of the MSVM as an efficient cloud detection and classification algorithm for use in Earth Observing System models, which require knowledge of whether or not a radiance profile is cloud free. If the profile is not cloud free, it is valuable to have information concerning the type of cloud, for example, ice or water. The MSVM has been applied to simulated MODIS channel data to classify the radiance profiles as coming from clear sky, water clouds, or ice clouds, and the results are promising. It can be seen in simple examples, and application to Moderate Resolution Imaging Spectroradiometer (MODIS) observations, that the method is an improvement over channel-by-channel partitioning. It is believed that the MSVM will be a very useful tool for classification problems in atmospheric sciences. © 2004 American Meteorological Society."
"7006191743;7202772927;7006191878;","A study of landscape-generated deep moist convection",1998,"10.1175/1520-0493(1998)126<0928:ASOLGD>2.0.CO;2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0031667152&doi=10.1175%2f1520-0493%281998%29126%3c0928%3aASOLGD%3e2.0.CO%3b2&partnerID=40&md5=7e30e0d61b21a506d5abb8d2345b520b","A two-dimensional version of a cloud-resolving model was used to study the generation of deep moist convection over heterogeneous landscapes. Alternating patches of dry and wet soil were simulated for various profiles of background wind. Results suggested a significant, systematic impact of patch length and background wind on moist convection. Rainfall occurred most intensely along sea-breeze-like fronts, which formed at patch boundaries. Total accumulated rainfall - as the average over simulations with the same patch size but with different background wind profiles - was largest for a patch length of 128 km. This patch length was similar in size to a local radius of deformation (ro = HN/ω). The deposition of rainfall generated a much different distribution of soil moisture after one day of model simulation. This new distribution, however, was far from equilibrium, as the landscape still consisted of a number of wet and dry soil patches. The cloud structure of moist convection was also examined using a cloud classification technique. The greatest percentage of rainfall that occurred from deep clouds (which had ""roots"" in the middle troposphere) was also obtained over patches with length similar to ro. The results suggest the need to account for the triggering of moist convection by land surface heterogeneity in regional- and global-scale atmospheric models. It is also necessary to include the impact of patch size on cloud type. Moreover, because the distribution of soil moisture patches evolves over time in response to background atmospheric conditions, further study is suggested to gain a more full understanding of local-scale feedbacks between moist convection and soil moisture."
"55249233300;55350055700;55790343700;","Analysis of oblique aerial images for land cover and point cloud classification in an Urban environment",2015,"10.1109/TGRS.2014.2337658","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84907522905&doi=10.1109%2fTGRS.2014.2337658&partnerID=40&md5=112ae741efab212c61eafb1f9a15bc7c","In addition to aerial imagery, point clouds are important remote sensing data in urban environment studies. It is essential to extract semantic information from both images and point clouds for such purposes; thus, this study aims to automatically classify 3-D point clouds generated using oblique aerial imagery (OAI)/vertical aerial imagery (VAI) into various urban object classes, such as roof, facade, road, tree, and grass. A multicamera airborne imaging system that can simultaneously acquire VAI and OAI is suggested. The acquired small-format images contain only three RGB spectral bands and are used to generate photogrammetric point clouds through a multiview-stereo dense matching technique. To assign each 3-D point cloud to a corresponding urban object class, we first analyzed the original OAI through object-based image analyses. A rule-based hierarchical semantic classification scheme that utilizes spectral information and geometry-and topology-related features was developed, in which the object height and gradient features were derived from the photogrammetric point clouds to assist in the detection of elevated objects, particularly for the roof and facade. Finally, the photogrammetric point clouds were classified into the aforementioned five classes. The classification accuracy was assessed on the image space, and four experimental results showed that the overall accuracy is between 82.47% and 91.8%. In addition, visual and consistency analyses were performed to demonstrate the proposed classification scheme's feasibility, transferability, and reliability, particularly for distinguishing elevated objects from OAI, which has a severe occlusion effect, image-scale variation, and ambiguous spectral characteristics. © 2014 IEEE."
"6604053026;","1D-VAR retrieval of temperature and humidity profiles from a ground-based microwave radiometer",2007,"10.1109/TGRS.2007.898091","https://www.scopus.com/inward/record.uri?eid=2-s2.0-34347231225&doi=10.1109%2fTGRS.2007.898091&partnerID=40&md5=9e9c731607d3c262eba400202e6d6b2f","A variational method to retrieve profiles of temperature, humidity, and cloud is described, which combines observations from a 12-channel microwave radiometer, an infrared radiometer, and surface sensors with background from shortrange numerical weather prediction (NWP) forecasts in an optimal way, accounting for their error characteristics. An analysis is presented of the error budget of the background and observations, including radiometric, modeling, and representativeness errors. Observation errors of some moisture channels are found to be dominated by representativeness, due to their sensitivity to atmospheric variability on smaller scales than the NWP model grid, whereas channels providing information on temperature in the lowest 1 km are dominated by instrument noise. Profiles of temperature and a novel total water control variable are retrieved from synthetic data using Newtonian iteration. An error analysis shows that these are expected to improve mesoscale NWP, retrieving temperature and humidity profiles up to 4 km with uncertainties of < 1 K and < 40% and 2.8 and 1.8 degrees of freedom for signal, respectively, albeit with poor vertical resolution. A cloud classification scheme is introduced to address convergence problems and better constrain the retrievals. This Bayesian retrieval method can be extended to incorporate observations from other instruments to form a basis for future integrated profiling systems. © 2007 IEEE."
"16637291100;7005311892;7202840464;","Cloud classification from satellite data using a fuzzy sets algorithm: A polar example",1989,"10.1080/01431168908904014","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0024901577&doi=10.1080%2f01431168908904014&partnerID=40&md5=5702f5950ec8e8420803446d6e8efee7","Where spatial boundaries between phenomena are diffuse, classification methods which construct mutually exclusive clusters seem inappropriate. The fuzzy c-means (FCM) algorithm assigns each observation to all clusters, with membership values as a function of distance to the cluster centre. The FCM algorithm is applied to Advanced Very High Resolution Radiometer (AVHRR) data for the purpose of classifying polar clouds and surfaces. Careful analysis of the fuzzy sets can provide information on which spectral channels are best suited to the classification of particular features and can help determine likely areas of misclassification. General agreement in the resulting classes and cloud fraction was found between the FCM algorithm, a manual classification and an unsupervised maximum likelihood classifier. © 1989 Taylor & Francis Ltd."
"57209423225;55680966400;24401175200;7004082496;6506388437;7801347466;","Contextual classification of point cloud data by exploiting individual 3D neigbourhoods",2015,"10.5194/isprsannals-II-3-W4-271-2015","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84969193116&doi=10.5194%2fisprsannals-II-3-W4-271-2015&partnerID=40&md5=8430427b18dd4f6348d5377620be257a","The fully automated analysis of 3D point clouds is of great importance in photogrammetry, remote sensing and computer vision. For reliably extracting objects such as buildings, road inventory or vegetation, many approaches rely on the results of a point cloud classification, where each 3D point is assigned a respective semantic class label. Such an assignment, in turn, typically involves statistical methods for feature extraction and machine learning. Whereas the different components in the processing workflow have extensively, but separately been investigated in recent years, the respective connection by sharing the results of crucial tasks across all components has not yet been addressed. This connection not only encapsulates the interrelated issues of neighborhood selection and feature extraction, but also the issue of how to involve spatial context in the classification step. In this paper, we present a novel and generic approach for 3D scene analysis which relies on (<i>i</i>) individually optimized 3D neighborhoods for (<i>ii</i>) the extraction of distinctive geometric features and (<i>iii</i>) the contextual classification of point cloud data. For a labeled benchmark dataset, we demonstrate the beneficial impact of involving contextual information in the classification process and that using individual 3D neighborhoods of optimal size significantly increases the quality of the results for both pointwise and contextual classification. © 2015 Copernicus GmbH. All Rights Reserved."
"6506316395;53463999600;8318179400;57203053544;","Evaluating satellite products for precipitation estimation in mountain regions: A case study for Nepal",2013,"10.3390/rs5084107","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84884781772&doi=10.3390%2frs5084107&partnerID=40&md5=0765cd3d73e8b10cdd816f87ed71ac2f","Precipitation in mountain regions is often highly variable and poorly observed, limiting abilities to manage water resource challenges. Here, we evaluate remote sensing and ground station-based gridded precipitation products over Nepal against weather station precipitation observations on a monthly timescale. We find that the Tropical Rainfall Measuring Mission (TRMM) 3B-43 precipitation product exhibits little mean bias and reasonable skill in giving precipitation over Nepal. Compared to station observations, the TRMM precipitation product showed an overall Nash-Sutcliffe efficiency of 0.49, which is similar to the skill of the gridded station-based product Asian Precipitation-Highly Resolved Observational Data Integration Towards Evaluation ofWater Resources (APHRODITE). The other satellite precipitation products considered (Global Satellite Mapping of Precipitation (GSMaP), the Climate Prediction Center Morphing technique (CMORPH), Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS)) were less skillful, as judged by Nash-Sutcliffe efficiency, and, on average, substantially underestimated precipitation compared to station observations, despite their, in some cases, higher nominal spatial resolution compared to TRMM. None of the products fully captured the dependence of mean precipitation on elevation seen in the station observations. Overall, the TRMM product is promising for use in water resources applications. © 2013 by the authors."
"6603164038;6701382162;25937337300;8719703500;7006508549;","Convective cloud identification and classification in daytime satellite imagery using standard deviation limited adaptive clustering",2008,"10.1029/2008JD010287","https://www.scopus.com/inward/record.uri?eid=2-s2.0-58149277163&doi=10.1029%2f2008JD010287&partnerID=40&md5=ffda7b42ee20204212bed86f9e9f49be","This paper describes a statistical clustering approach toward the classification of cloud types within meteorological satellite imagery, specifically, visible and infrared data. The method is based on the Standard Deviation Limited Adaptive Clustering (SDLAC) procedure, which has been used to classify a variety of features within both polar orbiting and geostationary imagery, including land cover, volcanic ash, dust, and clouds of various types. In this study, the focus is on classifying cumulus clouds of various types (e.g., ""fair weather, ""towering, and newly glaciated cumulus, in addition to cumulonimbus). The SDLAC algorithm is demonstrated by showing examples using Geostationary Operational Environmental Satellite (GOES) 12, Meteosat Second Generation's (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI), and the Moderate Resolution Infrared Spectrometer (MODIS). Results indicate that the method performs well, classifying cumulus similarly between MODIS, SEVIRI, and GOES, despite the obvious channel and resolution differences between these three sensors. The SDLAC methodology has been used in several research activities related to convective weather forecasting, which offers some proof of concept for its value. Copyright 2008 by the American Geophysical Union."
"55599166100;6602731047;57210434487;55598883700;14122837900;55210833000;55598498300;","Cloud detection, classification and motion estimation using geostationary satellite imagery for cloud cover forecast",2013,"10.1016/j.energy.2013.01.054","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84878656933&doi=10.1016%2fj.energy.2013.01.054&partnerID=40&md5=a54170e22bf68f83a95fca9252d111aa","Considering that clouds are the greatest causes to solar radiation blocking, short term cloud forecasting can help power plant operation and therefore improve benefits. Cloud detection, classification and motion vector determination are key to forecasting sun obstruction by clouds. Geostationary satellites provide cloud information covering wide areas, allowing cloud forecast to be performed for several hours in advance. Herein, the methodology developed and tested in this study is based on multispectral tests and binary cross correlations followed by coherence and quality control tests over resulting motion vectors. Monthly synthetic surface albedo image and a method to reject erroneous correlation vectors were developed. Cloud classification in terms of opacity and height of cloud top is also performed. A whole-sky camera has been used for validation, showing over 85% of agreement between the camera and the satellite derived cloud cover, whereas error in motion vectors is below 15%. © 2013 Elsevier Ltd."
"57204392506;12753475500;56031375100;56421283800;55567239000;","Improving Fmask cloud and cloud shadow detection in mountainous area for Landsats 4–8 images",2017,"10.1016/j.rse.2017.07.002","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85022219751&doi=10.1016%2fj.rse.2017.07.002&partnerID=40&md5=8ea04eb1d9c7c98cf937f1d96fa2be8f","We developed a new algorithm called MFmask (Mountainous Fmask) for automated cloud and cloud shadow detection for Landsats 4–8 images acquired in mountainous areas. The MFmask algorithm, built upon the success of the Fmask algorithm (Zhu and Woodcock, 2012; Zhu et al., 2015), is designed for cloud and cloud shadow detection in mountainous areas, where the Fmask algorithm is not performing well. The inputs of the MFmask algorithm include Landsat Top of Atmosphere (TOA) reflectance, Brightness Temperature (BT), and Digital Elevation Models (DEMs). Compared to Fmask, MFmask can separate water and land pixels better in mountainous areas with the aid of DEMs. Moreover, MFmask produces better cloud detection results than Fmask in mountainous areas after BT is linearly normalized by DEMs. To provide more accurate cloud shadow detection in mountainous areas, MFmask uses a double-projection approach to better predict cloud shadow shape on slope side. Additionally, MFmask applies a topographic correction to remove terrain shadows and estimates cloud base height with neighboring clouds. Both will reduce the possibility of cloud and cloud shadow mismatch and increase cloud shadow detection accuracy for places with large topographic gradient. To test the performance of the proposed MFmask algorithm, a total of 67 Landsat images acquired in mountainous areas from different parts of the world were selected for assessing the accuracy of cloud detection, in which 15 of them were used for assessing the accuracy of cloud shadow detection. Compared with Fmask, MFmask can provide substantial improvements in cloud and cloud shadow detection accuracies for places with large topographic gradient and also work well for relatively flat terrain. © 2017 Elsevier Inc."
"56267759500;55495868700;55619290298;24833754500;21933438300;","Cloud classification based on structure features of infrared images",2011,"10.1175/2010JTECHA1385.1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-79955038905&doi=10.1175%2f2010JTECHA1385.1&partnerID=40&md5=0fa31430821d3a4a74c888166e599d76","Some cloud structure features that can be extracted from infrared images of the sky are suggested for cloud classification. Both the features and the classifier are developed over zenithal images taken by the whole-sky infrared cloud-measuring system (WSIRCMS), which is placed in Nanjing, China. Before feature extraction, the original infrared image was smoothed to suppress noise. Then, the image was enhanced using top-hat transformation and a high-pass filtering. Edges are detected from the enhanced image after adaptive optimization threshold segmentation and morphological edge detection. Several structural features are extracted from the segment image and edge image, such as cloud gray mean value (ME), cloud fraction (ECF), edge sharpness (ES), and cloud mass and gap distribution parameters, including very small-sized cloud mass and gaps (SMG), middle-sized cloud gaps (MG), medium-small-sized cloud gaps (MSG), and main cloud mass (MM). It is found that these features are useful for distinguishing cirriform, cumuliform, and waveform clouds. A simple but efficient supervised classifier called the rectangle method is used to do cloud classification. The performance of the classifier is assessed with an a priori classification carried out by visual inspection of 277 images. The index of agreement is 90.97%. © 2011 American Meteorological Society."
"55509658400;57207222073;56263595100;57203397955;","CloudNet: Ground-Based Cloud Classification With Deep Convolutional Neural Network",2018,"10.1029/2018GL077787","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85053182365&doi=10.1029%2f2018GL077787&partnerID=40&md5=fe2dc5a34c57e468c88b67539ac6a72c","Clouds have an enormous influence on the Earth's energy balance, climate, and weather. Cloud types have different cloud radiative effects, which is an essential indicator of the cloud effect on radiation. Therefore, identifying the cloud type is important in meteorology. In this letter, we propose a new convolutional neural network model, called CloudNet, for accurate ground-based meteorological cloud classification. We build a ground-based cloud data set, called Cirrus Cumulus Stratus Nimbus, which consists of 11 categories under meteorological standards. The total number of cloud images is three times that of the previous database. In particular, it is the first time that contrails, a type of cloud generated by human activity, have been taken into account in the ground-based cloud classification, making the Cirrus Cumulus Stratus Nimbus data set more discriminative and comprehensive than existing ground-based cloud databases. The evaluation of a large number of experiments demonstrates that the proposed CloudNet model could achieve good performance in meteorological cloud classification. ©2018. American Geophysical Union. All Rights Reserved."
"7101984634;7101677832;7005741798;7003725697;35411782100;35547214900;6602407753;7202108879;","An improvement to the high-spectral-resolution CO2-slicing cloud-top altitude retrieval",2006,"10.1175/JTECH1877.1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-33745026993&doi=10.1175%2fJTECH1877.1&partnerID=40&md5=1d8d3c8a862be7e73510fe1165f0ae0e","An improvement to high-spectral-resolution infrared cloud-top altitude retrievals is compared to existing retrieval methods and cloud lidar measurements. The new method, CO2 sorting, determines optimal channel pairs to which the CO2 slicing retrieval will be applied. The new retrieval is applied to aircraft Scanning High-Resolution Interferometer Sounder (S-HIS) measurements. The results are compared to existing passive retrieval methods and coincident Cloud Physics Lidar (CPL) measurements. It is demonstrated that when CO2 sorting is used to select channel pairs for CO2 slicing there is an improvement in the retrieved cloud heights when compared to the CPL for the optically thin clouds (total optical depths less than 1.0). For geometrically thick but tenuous clouds, the infrared retrieved cloud tops underestimated the cloud height, when compared to those of the CPL, by greater than 2.5 km. For these cases the cloud heights retrieved by the S-HIS correlated closely with the level at which the CPL-integrated cloud optical depth was approximately 1.0. © 2006 American Meteorological Society."
"56430230500;57202366208;55582167500;56729520500;","Robust point cloud classification based on multi-level semantic relationships for urban scenes",2017,"10.1016/j.isprsjprs.2017.04.022","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85018767207&doi=10.1016%2fj.isprsjprs.2017.04.022&partnerID=40&md5=7fa309f3896732b1756231ad9b1fccf2","The semantic classification of point clouds is a fundamental part of three-dimensional urban reconstruction. For datasets with high spatial resolution but significantly more noises, a general trend is to exploit more contexture information to surmount the decrease of discrimination of features for classification. However, previous works on adoption of contexture information are either too restrictive or only in a small region and in this paper, we propose a point cloud classification method based on multi-level semantic relationships, including point–homogeneity, supervoxel–adjacency and class–knowledge constraints, which is more versatile and incrementally propagate the classification cues from individual points to the object level and formulate them as a graphical model. The point–homogeneity constraint clusters points with similar geometric and radiometric properties into regular-shaped supervoxels that correspond to the vertices in the graphical model. The supervoxel–adjacency constraint contributes to the pairwise interactions by providing explicit adjacent relationships between supervoxels. The class–knowledge constraint operates at the object level based on semantic rules, guaranteeing the classification correctness of supervoxel clusters at that level. International Society of Photogrammetry and Remote Sensing (ISPRS) benchmark tests have shown that the proposed method achieves state-of-the-art performance with an average per-area completeness and correctness of 93.88% and 95.78%, respectively. The evaluation of classification of photogrammetric point clouds and DSM generated from aerial imagery confirms the method's reliability in several challenging urban scenes. © 2017 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS)"
"6603081424;7004540083;","The cloud radiative effects of International Satellite Cloud Climatology Project weather states",2011,"10.1029/2010JD015472","https://www.scopus.com/inward/record.uri?eid=2-s2.0-79959638009&doi=10.1029%2f2010JD015472&partnerID=40&md5=60e25d096b996e39322a6f4c07443bed","The salient features of the daytime cloud radiative effect (CRE, also known as cloud radiative forcing) corresponding to various cloud regimes or weather states are examined. The analysis is based on a 24 year long data set from the International Satellite Cloud Climatology Project (ISCCP) for three distinct geographical zones covering most of the Earth's surface area. Conditional sampling and averaging of the ISCCP cloud fraction and CRE in 2.5 grid cells is performed for each weather state, and the state's radiative importance expressed as the relative contribution to the total CRE of its geographical zone is explained in terms of dominant cloud type, cloud fraction, and frequency of occurrence. Similarities and differences within and between geographical zones in the cloud fraction and CRE characteristics of the various weather states are identified and highlighted. By providing an exposition of the radiative energy characteristics of different cloud type mixtures, we facilitate the meteorological situation-dependent evaluation of radiation budget effects due to clouds in climate models. Copyright 2011 by the American Geophysical Union."
"7201472576;","A 10 year cloud climatology over Scandinavia derived from NOAA Advanced Very High Resolution Radiometer imagery",2003,"10.1002/joc.916","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0042974064&doi=10.1002%2fjoc.916&partnerID=40&md5=3512889763413565f9ab88bc4ff48658","Results from a satellite-based method to compile regional cloud climatologies covering the Scandinavian region are presented. Systematic processing of multispectral image data from the NOAA Advanced Very High Resolution Radiometer (AVHRR) instrument has been utilized to provide monthly cloud climatologies covering the period 1991-2000. Considerable local-scale variation of cloud amounts was found in the region. The inland Baltic Sea and adjacent land areas exhibited a large-amplitude annual cycle in cloudiness (high cloud amounts in winter, low cloud amounts in summer) whereas a weak-amplitude reversed annual cycle (high cloud amounts with a weak maximum in summer) was found for the Scandinavian mountain range. As a contrast, conditions over the Norwegian Sea showed high and almost unchanged cloud amounts during the course of the year. Some interesting exceptions to these patterns were also seen locally. The quality of the satellite-derived cloud climatology was examined through comparisons with climatologies derived from surface cloud observations, from the International Satellite Cloud Climatology Project (ISCCP) and from the European Centre for Medium-range Weather Forecasts ERA-40 data set. In general, cloud amount deviations from surface observations were smaller than 10% except for some individual winter months, when the separability between clouds and snow-covered cold land surfaces is often poor. The ISCCP data set showed a weaker annual cycle in cloudiness, generally caused by higher summer-time cloud amounts in the region. Very good agreement was found with the ERA-40 data set, especially for the summer season. However, ERA-40 showed higher cloud amounts than SCANDIA and ISCCP during the winter season. The derived cloud climatology is affected by errors due to temporal AVHRR sensor degradation, but they appear to be small for this particular study. The data set is proposed as a valuable data set for validation of cloud description in numerical weather prediction and regional climate simulation models. © 2003 Royal Meteorological Society."
"7201826462;","Satellite remote sensing of multiple cloud layers",1995,"10.1175/1520-0469(1995)052<4210:SRSOMC>2.0.CO;2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0029535563&doi=10.1175%2f1520-0469%281995%29052%3c4210%3aSRSOMC%3e2.0.CO%3b2&partnerID=40&md5=ee6443e98d81eace9c71602725f58cde","The goals of the current study are threefold: 1) to present a multispectral, multiresolution (MSMR) methodology for analysis of scenes containing multiple cloud layers; 2) to apply the MSMR method to two multilevel cloud scenes and 3) to validate the cloud-top height results from the case study analyses through comparison with lidar, radar, aircraft, and rawinsonde data. A ""fuzzy logic' classification system is developed to determine whether a 32 × 32 array of AVHRR data contains clear sky, low-level cloud, midlevel cloud, high-level cloud, or multiple cloud layers. With the addition of the fuzzy logic cloud classification system, it is possible for the first time to find evidence of more than one cloud layer within each HIRS field of view. Low cloud heights are determined through application of the spatial coherence method to the AVHRR data, while mid- to high-level cloud hights are calculated from the HIRS/2 15-μm CO2 band radiometric data that are collocated with the AVHRR data. Cirrus cloud heights retrieved from HIRS 15-μm CO2 band data are improved for optically thin cirrus through the use of the upper-tropospheric humidity profile. -from Authors"
"26632168400;56246453200;24722339600;","Climatology of stratocumulus cloud morphologies: Microphysical properties and radiative effects",2014,"10.5194/acp-14-6695-2014","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84903790842&doi=10.5194%2facp-14-6695-2014&partnerID=40&md5=528f92f2c26951a47b1b7dbe0c903513","An artificial neural network cloud classification scheme is combined with A-train observations to characterize the physical properties and radiative effects of marine low clouds based on their morphology and type of mesoscale cellular convection (MCC) on a global scale. The cloud morphological categories are (i) organized closed MCC, (ii) organized open MCC and (iii) cellular but disorganized MCC. Global distributions of the frequency of occurrence of MCC types show clear regional signatures. Organized closed and open MCCs are most frequently found in subtropical regions and in midlatitude storm tracks of both hemispheres. Cellular but disorganized MCC are the predominant type of marine low clouds in regions with warmer sea surface temperature such as in the tropics and trade wind zones. All MCC types exhibit a pronounced seasonal cycle. The physical properties of MCCs such as cloud fraction, radar reflectivity, drizzle rates and cloud top heights as well as the radiative effects of MCCs are found highly variable and a function of the type of MCC. On a global scale, the cloud fraction is largest for closed MCC with mean cloud fractions of about 90%, whereas cloud fractions of open and cellular but disorganized MCC are only about 51% and 40%, respectively. Probability density functions (PDFs) of cloud fractions are heavily skewed and exhibit modest regional variability. PDFs of column maximum radar reflectivities and inferred cloud base drizzle rates indicate fundamental differences in the cloud and precipitation characteristics of different MCC types. Similarly, the radiative effects of MCCs differ substantially from each other in terms of shortwave reflectance and transmissivity. These differences highlight the importance of low-cloud morphologies and their associated cloudiness on the shortwave cloud forcing. © Author(s) 2014."
"56135196400;7403872687;7401526171;7005052907;6507378331;","A deep neural network modeling framework to reduce bias in satellite precipitation products",2016,"10.1175/JHM-D-15-0075.1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84961392473&doi=10.1175%2fJHM-D-15-0075.1&partnerID=40&md5=3fe822f55a1058a64c9d4d6448cc4ad6","Despite the advantage of global coverage at high spatiotemporal resolutions, satellite remotely sensed precipitation estimates still suffer from insufficient accuracy that needs to be improved for weather, climate, and hydrologic applications. This paper presents a framework of a deep neural network (DNN) that improves the accuracy of satellite precipitation products, focusing on reducing the bias and false alarms. The state-of-the-art deep learning techniques developed in the area of machine learning specialize in extracting structural information from a massive amount of image data, which fits nicely into the task of retrieving precipitation data from satellite cloud images. Stacked denoising autoencoder (SDAE), a widely used DNN, is applied to perform bias correction of satellite precipitation products. A case study is conducted on the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks Cloud Classification System (PERSIANN-CCS) with spatial resolution of 0.08° × 0.08° over the central United States, where SDAE is used to process satellite cloud imagery to extract information over a window of 15 × 15 pixels. In the study, the summer of 2012 (June-August) and the winter of 2012/13 (December-February) serve as the training periods, while the same seasons of the following year (summer of 2013 and winter of 2013/14) are used for validation purposes. To demonstrate the effectiveness of the methodology outside the study area, three more regions are selected for additional validation. Significant improvements are achieved in both rain/no-rain (R/NR) detection and precipitation rate quantification: the results make 33% and 43% corrections on false alarm pixels and 98% and 78% bias reductions in precipitation rates over the validation periods of the summer and winter seasons, respectively. © 2016 American Meteorological Society."
"7004384155;7003597653;55869652000;7202746102;","Clouds as seen by satellite sounders (3I) and imagers (ISCCP). Part II: A new approach for cloud parameter determination in the 3I algorithms",1999,"10.1175/1520-0442(1999)012<2214:casbss>2.0.co;2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0033172548&doi=10.1175%2f1520-0442%281999%29012%3c2214%3acasbss%3e2.0.co%3b2&partnerID=40&md5=41ebd8ca26e2952aec68cab9ed392708","First comparisons of improved initialization inversion (3I) cloud parameters determined from TIROS-N Operational Vertical Sounder observations with time-space-collocated clouds from the recently reprocessed International Satellite Cloud Climatology Project (ISCCP) dataset have shown a reasonable agreement between all cloud types, with exception of the stratocumulus regions off the western coasts. Here, 3I clouds were found systematically thinner and higher than ISCCP clouds. These results have initiated a careful investigation of the methods used to convert measurements from IR sounders into cloud parameters. All existing methods get very sensitive to the chosen temperature profile toward lower cloud heights, due to a denominator approaching zero. This leads to a bias like the one seen in the comparison with ISCCP. Therefore, a new 3I cloud scheme has been developed, based on a weighted-γ2 method, which calculates the effective cloud amount from the CO2-band radiances, but weighted differently according to the effect of the brightness temperature uncertainty within an air mass on these radiances at the various cloud levels. This physically much more correct method led to unbiased 3I cloud parameters for homogeneous cloud types. The ISCCP comparison agrees much better now, especially in the stratocumulus regions where the cloud type matching improved from about 50% to 75%. In 1°grid boxes covered uniformly with the same ISCCP cloud type, the matching reaches even 87%. Remaining discrepancies in cloud classification can be explained by partly cloudy fields and differences in temperature profiles and cloud detection. The weighted-γ2 method can be used in other IR sounder inversion algorithms, if the empirical weights, taking care of the effect of temperature profile uncertainties on the difference between clear sky and cloudy radiances for different cloud levels and spectral channels, have been reevaluated so that they can be calculated automatically by the corresponding inversion algorithm."
"7007175473;","Analysis of polar clouds from satellite imagery using pattern recognition and a statistical cloud analysis scheme",1989,"10.1175/1520-0450(1989)028<0382:AOPCFS>2.0.CO;2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0024830476&doi=10.1175%2f1520-0450%281989%29028%3c0382%3aAOPCFS%3e2.0.CO%3b2&partnerID=40&md5=32b9838852f519380b51b43657c7f68c","Pattern recognition has proven to be a useful tool in detecting and identifying several cloud types over snow and ice. Here a pattern recognition algorithm is combined with a hybrid histogram-spatial coherence (HHSC) scheme to derive cloud classification and fractional coverage, surface and cloud visible albedos and infrared brightness temperatures from multispectral AVHRR satellite imagery. The accuracy of the cloud fraction estimates were between 0.05 and 0.26, based on the mean absolute difference between the automated and manual nephanalyses of nearly 1000 training samples. The algorithm is demonstrated for a set of AVHRR imagery from the summertime Arctic. The automated classification and analysis are in good agreement with manual interpretation of the satellite imagery and with surface observations. -from Author"
"35099841200;7004260140;","The use of airborne and mobile laser scanning for modeling railway environments in 3D",2014,"10.3390/rs6043075","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84898063275&doi=10.3390%2frs6043075&partnerID=40&md5=f976683fba5d5a51eb939a7163aa2cd8","This paper presents methods for 3D modeling of railway environments from airborne laser scanning (ALS) and mobile laser scanning (MLS). Conventionally, aerial data such as ALS and aerial images were utilized for 3D model reconstruction. However, 3D model reconstruction only from aerial-view datasets can not meet the requirement of advanced visualization (e.g., walk-through visualization). In this paper, objects in a railway environment such as the ground, railroads, buildings, high voltage powerlines, pylons and so on were reconstructed and visualized in real-life experiments in Kokemaki, Finland. Because of the complex terrain and scenes in railway environments, 3D modeling is challenging, especially for high resolution walk-through visualizations. However, MLS has flexible platforms and provides the possibility of acquiring data in a complex environment in high detail by combining with ALS data to produce complete 3D scene modeling. A procedure from point cloud classification to 3D reconstruction and 3D visualization is introduced, and new solutions are proposed for object extraction, 3D reconstruction, model simplification and final model 3D visualization. Image processing technology is used for the classification, 3D randomized Hough transformations (RHT) are used for the planar detection, and a quadtree approach is used for the ground model simplification. The results are visually analyzed by a comparison with an orthophoto at a 20 cm ground resolution. © 2014 by the authors; licensee MDPI, Basel, Switzerland."
"55832671000;23017945100;7102128820;57196671387;6701754792;7006577245;","From CloudSat-CALIPSO to EarthCare: Evolution of the DARDAR cloud classification and its comparison to airborne radar-lidar observations",2013,"10.1002/jgrd.50579","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84882739651&doi=10.1002%2fjgrd.50579&partnerID=40&md5=9032a228aaf3f686b7edb4760d215eaa","This paper presents the implementation of a new version of the DARDAR (radar lidar) classification derived from CloudSat and CALIPSO data. The resulting target classification called DARDAR v2 is compared to the first version called DARDAR v1. Overall DARDAR v1 reports more cloud or rain pixels than DARDAR v2. In the low troposphere this is because v1 detects too many liquid cloud pixels, and in the higher troposphere this is because v2 is more restrictive in lidar detection than v1. Nevertheless, the spatial distribution of different types of hydrometeors show similar patterns in both classifications. The French airborne Radar-Lidar (RALI) platform carries a CloudSat/CALIPSO instrument configuration (lidar at a wavelength of 532nm and a 95GHz cloud radar) as well as an EarthCare instrument configuration (high spectral resolution lidar at 355nm and a 95GHz Doppler cloud radar). It therefore represents an ideal go-between for A-Train and EarthCare. The DARDAR v2 classification algorithm is adapted to RALI data for A-Train overpasses during dedicated airborne field experiments using the lidar at 532nm and the radar Doppler measurements. The results from the RALI classification are compared with the DARDAR v2 classification to identify where the classification should still be interpreted with caution. Finally, the RALI classification algorithm with lidar at 532nm is adapted to RALI with high spectral resolution lidar data at 355nm in preparation for EarthCare. Key Points A new DARDAR classification is developed from CloudSat and CALIOP profiles The new version is compared to the first one and it shows improvements The method is adapted to airborne data for validation and preparation to E-Care ©2013. American Geophysical Union. All Rights Reserved."
"34881780600;7401796996;8629713500;7005877775;36570526700;7403508241;7006783796;","Life cycle of midlatitude deep convective systems in a Lagrangian framework",2012,"10.1029/2012JD018362","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84870592379&doi=10.1029%2f2012JD018362&partnerID=40&md5=aa2f760a08ea7ae64c31ed99085bc4b5","Deep convective systems (DCSs) consist of intense convective cores (CC), large stratiform rain (SR) regions, and extensive nonprecipitating anvil clouds (AC). This study focuses on the evolution of these three components and the factors that affect system lifetime and AC production. An automated satellite tracking method is used in conjunction with a recently developed multisensor hybrid classification to analyze the evolution of DCS structure in a Lagrangian framework over the central United States. Composite analysis from 4221 tracked DCSs during two warm seasons (May-August, 2010-2011) shows that maximum system size correlates with lifetime, and longer-lived DCSs have more extensive SR and AC. For short to medium systems (lifetimes <6 h), the lifetime is mainly attributed to the intensity of the initial convection. Systems that last longer than 6 h are associated with up to 50% higher midtropospheric relative humidity and up to 40% stronger middle to upper tropospheric wind shear. Such environments allow continuous growth of detrained hydrometeors by deposition, supporting further development of the SR and AC region, as indicated by the increased staggered timing between stratiform clouds and peak convective intensity, thus prolonging the system lifetime beyond 6 h. Regression analysis shows that the areal coverage of thick AC is strongly correlated with the size of CC, updraft strength, and SR area. Ambient upper tropospheric wind speed and wind shear also play an important role for convective AC production, where for systems with large AC (radius >120 km) they are 24% and 20% higher, respectively, than those with small AC (radius = 20 km)."
"35305025100;6604000335;6603892183;6701599239;6701607011;","Large-scale analysis of cirrus clouds from AVHRR data: Assessment of both a microphysical index and the cloud-top temperature",1997,"10.1175/1520-0450-36.6.664","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0031402529&doi=10.1175%2f1520-0450-36.6.664&partnerID=40&md5=0911ffc8251ac2f6d599ce40a0918ec2","An algorithm that allows an automatic analysis of cirrus properties from Advanced Very High Resolution Radiometer (AVHRR) observations is presented. Further investigations of the information content and physical meaning of the brightness temperature differences (BTD) between channels 4(11 μm) and 5 (12 μm) of the radiometer have led to the development of an automatic procedure to provide global estimates both of the cirrus cloud temperature and of the ratio of the equivalent absorption coefficients in the two channels, accounting for scattering effects. The ratio is useful since its variations are related to differences in microphysical properties. Assuming that cirrus clouds are composed of ice spheres, the effective diameter of the particle size distribution can be deduced from this microphysical index. The automatic procedure includes first, a cloud classification and a selection of the pixels corresponding to the envelope of the BTD diagram observed at a scale of typically 100 × 100 pixels. The classification, which uses dynamic cluster analysis, takes into account spectral and spatial properties of the AVHRR pixels. The selection is made through a series of tests, which also guarantees that the BTD diagram contains the necessary information, such as the presence of both cirrus-free pixels and pixels totally covered by opaque cirrus in the same area. Finally, the cloud temperature and the equivalent absorption coefficient ratio are found by fitting the envelope of the BTD diagram with a theoretical curve. Note that the method leads to the retrieval of the maximum value of the equivalent absorption coefficient ratio in the scene under consideration. This, in turn, corresponds to the minimum value of the effective diameter of the size distribution of equivalent Mie particles. The automatic analysis has been applied to a series of 21 AVHRR images acquired during the International Cirrus Experiment (ICE'89). Although the dataset is obviously much too limited to draw any conclusion at the global scale, it is large enough to permit derivation of cirrus properties that are statistically representative of the cirrus systems contained therein. The authors found that on average, the maximum equivalent absorption coefficient ratio increases with the cloud-top temperature with a jump between 235 and 240 K. More precisely, for cloud temperatures warmer than 235 K, the retrieved equivalent absorption coefficient ratio sometimes corresponds to very small equivalent spheres (diameter smaller than 20 μm). This is never observed for lower cloud temperatures. This change in cirrus microphysical properties points out that ice crystal habits may vary from one temperature regime to another. It may be attributed to a modification of the size and/or shape of the particles."
"14054277800;35785760400;56165340200;7501952105;","Eigen-feature analysis of weighted covariance matrices for LiDAR point cloud classification",2014,"10.1016/j.isprsjprs.2014.04.016","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84900799935&doi=10.1016%2fj.isprsjprs.2014.04.016&partnerID=40&md5=46ecda47c0257d80fa01316755ed5d60","The features used in the separation of different objects are important for successful point cloud classification. Eigen-features from a covariance matrix of a point set with the sample mean are commonly used geometric features that can describe the local geometric characteristics of a point cloud and indicate whether the local geometry is linear, planar, or spherical. However, eigen-features calculated by the principal component analysis of a covariance matrix are sensitive to LiDAR data with inherent noise and incomplete shapes because of the non-robust statistical analysis. To obtain reliable eigen-features from LiDAR data and to improve classification accuracy, we introduce a method of analyzing local geometric characteristics of a point cloud by using a weighted covariance matrix with a geometric median. Each point is assigned a weight to represent its spatial contribution in the weighted principal component analysis and to estimate the geometric median which can be regarded as a localized center of a shape. In the experiments, qualitative and quantitative analyses on airborne LiDAR data and simulated point clouds show a clear improvement of the proposed method compared with the standard eigen-features. The classification accuracy is improved by 1.6-4.5% using a supervised classifier. © 2014 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS)."
"7005742190;12141333900;7102759403;","Cloud detection in the tropics - A suitable tool for climate-ecological studies in the high mountains of Ecuador",2004,"10.1080/01431160410001709967","https://www.scopus.com/inward/record.uri?eid=2-s2.0-8744278258&doi=10.1080%2f01431160410001709967&partnerID=40&md5=ef7697fd2939bbc1e242891cfd33dd19","The detection of clouds and the analysis of cloud frequency play an important role for operational weather prediction as well as for climate-ecological studies. A threshold technique for cloud detection in the tropical mountainous area of Ecuador is presented which is based on National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA-AVHRR) data. Cloud classification is performed for both day and night overpasses by applying several threshold tests which also include ancillary terrain information. From a set of 155 images, maps of relative cloud frequency are calculated for Ecuador and adjacent areas as well as the central study area of an ecological project in southern Ecuador (Loja). Results show a clear relation between topography, main airflow and cloudiness due to barrage and lee-effects. The spatial extension of high mountain ecosystems such as the Ecuadorian Páramo has been proven to be significantly influenced by the spatial pattern of cloud frequency. © 2004 Taylor & Francis Ltd."
"55339081600;6602600408;","Evaluation of clouds and precipitation in the ECHAM5 general circulation model using CALIPSO and cloudsat satellite data",2012,"10.1175/JCLI-D-11-00347.1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84864242359&doi=10.1175%2fJCLI-D-11-00347.1&partnerID=40&md5=b20004d4197177ed5327d0610d69d582","Observations from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and CloudSat satellites are used to evaluate clouds and precipitation in the ECHAM5 general circulation model. Active lidar and radar instruments on board CALIPSO and CloudSat allow the vertical distribution of clouds and their optical properties to be studied on a global scale. To evaluate the clouds modeled by ECHAM5with CALIPSO and CloudSat, the lidar and radar satellite simulators of the Cloud Feedback Model Intercomparison Project's Observation Simulator Package are used. Comparison of ECHAM5 with CALIPSO and CloudSat found large-scale features resolved by the model, such as the Hadley circulation, are captured well. The lidar simulator demonstrated ECHAM5 overestimates the amount of high-level clouds, particularly optically thin clouds. High-altitude clouds in ECHAM5 consistently produced greater lidar scattering ratios compared with CALIPSO. Consequently, the lidar signal in ECHAM5 frequently attenuated high in the atmosphere. The large scattering ratios were due to an underestimation of effective ice crystal radii in ECHAM5. Doubling the effective ice crystal radii improved the scattering ratios and frequency of attenuation. Additionally, doubling the effective ice crystal radii improved the detection of ECHAM5's highest-level clouds by the radar simulator, in better agreement with CloudSat. ECHAM5 was also shown to significantly underestimate midlevel clouds and (sub)tropical low-level clouds. The low-level clouds produced were consistently perceived by the lidar simulator as too optically thick. The radar simulator demonstrated ECHAM5 overestimates the frequency of precipitation, yet underestimates its intensity compared with CloudSat observations. These findings imply compensating mechanisms inECHAM5 balance out the radiative imbalance caused by incorrect optical properties of clouds and consistently large hydrometeors in the atmosphere. © 2012 American Meteorological Society."
"7101959253;7005626683;6602136577;","An assessment of Multiangle Imaging Spectroradiometer (MISR) stereo-derived cloud top heights and cloud top winds using ground-based radar, lidar, and microwave radiometers",2007,"10.1029/2006JD007091","https://www.scopus.com/inward/record.uri?eid=2-s2.0-34249677754&doi=10.1029%2f2006JD007091&partnerID=40&md5=783597749f9137bec0cbe143cd9542ee","In this article stereoscopically derived cloud top heights and cloud winds estimated from the Multiangle Imaging Spectroradiometer (MISR) are assessed. MISR is one of five instruments on board the NASA Terra satellite. The cloud top height assessment is based on a comparison of more than 4 years of MISR retrievals with that derived from ground-based radar and lidar systems operated by the U.S. Department of Energy Atmospheric Radiation Measurement program. The assessment includes a comparison of the MISR cloud top heights and ground-based data sets as a function of cloud optical depth and a simple cloud classification. Overall, we find that the MISR retrieval is working well with little bias for most cloud types, when the cloud is sufficiently optically thick to be detected. The detection limit is found to be around optical depth 0.3 to 0.5, except over snow and ice surfaces where it is larger. The standard deviation across all clouds is less than about 1000 m for the MISR best winds retrievals at all ARM sites, and the standard deviation for the MISR without winds retrieval varied between about 1000 to 1300 m, depending on the site. The performance for various cloud types is explored. Copyright 2007 by the American Geophysical Union."
"6504058972;7201472576;6603043158;","NWCSAF AVHRR cloud detection and analysis using dynamic thresholds and radiative transfer modeling. Part II: Tuning and validation",2005,"10.1175/JAM-2189.1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-14644394958&doi=10.1175%2fJAM-2189.1&partnerID=40&md5=9adb9589186836d7818e57a32f4958e0","Algorithms for cloud detection (cloud mask) and classification (cloud type) at high and midlatitudes using data from the Advanced Very High Resolution Radiometer (AVHRR) on board the current NOAA satellites and future polar Meteorological and Operational Weather Satellites (METOP) of the European Organisation for the Exploitation of Meteorological Satellites have been extensively validated over northern Europe and the adjacent seas. The algorithms have been described in detail in Part I and are based on a multispectral grouped threshold approach, making use of cloud-free radiative transfer model simulations. The thresholds applied in the algorithms have been validated and tuned using a database interactively built up over more than 1 yr of data from NOAA-12, -14, and -15 by experienced nephanalysts. The database contains almost 4000 rectangular (in the image data)-sized targets (typically with sides around 10 pixels), with satellite data collocated in time and space with atmospheric data from a short-range NWP forecast model, land cover characterization, elevation data, and a label identifying the given cloud or surface type as interpreted by the nephanalyst. For independent and objective validation, a large dataset of nearly 3 yr of collocated surface synoptic observation (Synop) reports, AVHRR data, and NWP model output over northern and central Europe have been collected. Furthermore, weather radar data were used to check the consistency of the cloud type. The cloud mask performs best over daytime sea and worst at twilight and night over land. As compared with Synop, the cloud cover is overestimated during night (except for completely overcast situations) and is underestimated at twilight. The algorithms have been compared with the more empirically based Swedish Meteorological and Hydrological Institute (SMHI) Cloud Analysis Model Using Digital AVHRR Data (SCANDIA), operationally run at SMHI since 1989, and results show that performance has improved significantly. © 2005 American Meteorological Society."
"7005181100;7004189729;","Cloud classification using support vector machines",2000,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-0034545562&partnerID=40&md5=77457a57e5cfef232be060bdf9c24b5c","Cloud classification from GOES 8 (Geostationary Operational Environmental Satellite) imagery data is performed using the infrared (IR) channel only. For each block of the image, first and second order statistics are extracted and used to train and test a classifier. In this paper, cloud classification is performed using a support vector machine (SVM) classifier. This scheme, which is typically used to solve two-class problems, has been extended to classify ten different cloud and no-cloud areas. Preliminary results indicate the promise of this method for meteorological applications."
"57206029166;13406672500;","Lagrangian approach for deriving cloud characteristics from satellite observations and its implications to cloud parameterization",1997,"10.1029/97jd00930","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0031413306&doi=10.1029%2f97jd00930&partnerID=40&md5=de1ce28353840f38d110ad325031ef7d","A Lagrangian view is adopted for establishing the spatiotemporal cloud statistics and the scale dependent radiative properties using satellite data. Individual clouds are identified using a newly developed scheme. We sort all clouds by cloud type, cloud area, and number of clouds in each area bin, as well as their radiative properties. For seven different cloud types our analyses provide radiative properties, such as albedo and cloud top temperature, as a function of the cloud spatial scale. All clouds are marked by local time, and large clouds are tracked over time. These analyses provide diurnal variability, lifetimes, and evolution of cloud systems as a function of their spatial scales. These scale dependent cloud properties can be objectively used in guiding the development and evaluation of cloud parameterization in global climate models (GCMs). Particularly, we show how our Lagrangian approach can be used to establish the relative importance of resolvable and fully parameterized clouds to the total cloudy area and to the total amount of reflected visible irradiance. Focus in this 1 month satellite study is on the convective-stratiform cloud systems over the western and central tropical Pacific Ocean, including the so-called warm pool. We adopt the hourly Japanese geostationary satellite (GMS) window channel radiances in the visible and IR window region for cloud classification and characterization. To study the radiative contributions of different clouds in the area, we computed the bidirectional model (BDM) for the Visible and Infrared Spin Scan Radiometer instrument aboard GMS, which we show to agree well with the BDM of the Earth Radiation Budget instrument aboard the Nimbus 7 satellite. An iterative two-stage cloud detection scheme was developed to identify individual clouds. Furthermore, a tracking algorithm was developed to study the time evolution of mesoscale convective systems (MCS). It operates on area and orientationally equivalent ellipsoidal representations of these MCS. We show that the temporal statistics of these convective anvil clouds show good agreement with those reported in the literature. Our data indicate that for the convective-stratiform systems in the tropical Pacific, 95% of the radiatively important clouds (containing a core with an effective brightness temperature <219 K) are of scales resolvable by a GCM of about 50 km × 50 km. On the other hand, a GCM of 250 km × 250 km will only be able to resolve 50% of the radiatively important clouds. This, however, does not mean that the processes responsible for the formation and maintenance of these systems are also resolvable. The low clouds that are unattached to convective-stratiform systems are mostly unresolvable by available GCMs."
"7403648844;15828981100;39763050000;55955073200;7401526171;7005052907;","Watershed rainfall forecasting using neuro-fuzzy networks with the assimilation of multi-sensor information",2014,"10.1016/j.jhydrol.2013.11.011","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84889679815&doi=10.1016%2fj.jhydrol.2013.11.011&partnerID=40&md5=c9d74f0b2d26ec9e28988012bf557de0","The complex temporal heterogeneity of rainfall coupled with mountainous physiographic context makes a great challenge in the development of accurate short-term rainfall forecasts. This study aims to explore the effectiveness of multiple rainfall sources (gauge measurement, and radar and satellite products) for assimilation-based multi-sensor precipitation estimates and make multi-step-ahead rainfall forecasts based on the assimilated precipitation. Bias correction procedures for both radar and satellite precipitation products were first built, and the radar and satellite precipitation products were generated through the Quantitative Precipitation Estimation and Segregation Using Multiple Sensors (QPESUMS) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS), respectively. Next, the synthesized assimilated precipitation was obtained by merging three precipitation sources (gauges, radars and satellites) according to their individual weighting factors optimized by nonlinear search methods. Finally, the multi-step-ahead rainfall forecasting was carried out by using the adaptive network-based fuzzy inference system (ANFIS). The Shihmen Reservoir watershed in northern Taiwan was the study area, where 641 hourly data sets of thirteen historical typhoon events were collected. Results revealed that the bias adjustments in QPESUMS and PERSIANN-CCS products did improve the accuracy of these precipitation products (in particular, 30-60% improvement rates for the QPESUMS, in terms of RMSE), and the adjusted PERSIANN-CCS and QPESUMS individually provided about 10% and 24% contribution accordingly to the assimilated precipitation. As far as rainfall forecasting is concerned, the results demonstrated that the ANFIS fed with the assimilated precipitation provided reliable and stable forecasts with the correlation coefficients higher than 0.85 and 0.72 for one- and two-hour-ahead rainfall forecasting, respectively. The obtained forecasting results are very valuable information for the flood warning in the study watershed during typhoon periods. © 2013 Elsevier B.V."
"56584089300;7005146506;7801436487;","Unsupervised segmentation of low clouds from infrared METEOSAT images based on a contextual spatio-temporal labeling approach",2002,"10.1109/36.981353","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0036193701&doi=10.1109%2f36.981353&partnerID=40&md5=e755fb6eb42330624517669490e9e09c","The early and accurate segmentation of low clouds during the night-time is an important task for nowcasting. It requires that observations can be acquired at a sufficient time rate as provided by the geostationary METEOSAT satellite over Europe. However, the information supplied by the single infrared METEOSAT channel available by night is not sufficient to discriminate between low clouds and ground during night from a single image. To tackle this issue, we consider several sources of information extracted from an infrared image sequence. Indeed, we exploit both relevant local motion-based measurements, intensity images and thermal parameters estimated over blocks, along with local contextual information. A statistical contextual labeling process in two classes, involving ""low clouds"" and ""clear sky,"" is performed on the warmer pixels. It is formulated within a Bayesian estimation framework associated with Markov random field (MRF) models. This comes to minimize a global energy function comprising three terms: two data-driven terms (thermal and motion-based ones) and a regularization term expressing a priori knowledge on the label field (expected spatial contextual properties). We propose a progressive minimization procedure of this energy function starting from initial reliably labeled pixels and involving only local computation. Thermal parameters associated to each class are estimated according to an unsupervised learning scheme enabling the handling of spatiotemporal nonstationarities. Our method produces segmentation maps displaying temporal coherency along the image sequence. Experimental results on representative meteorological situations are reported and favorably compared with NOAA/AVHRR cloud classifications which serve as reference results. They demonstrate the accuracy and efficiency of the proposed approach."
"7101714152;","A comparative study of cloud classification techniques",1977,"10.1016/0034-4257(77)90007-4","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0017426925&doi=10.1016%2f0034-4257%2877%2990007-4&partnerID=40&md5=cff62d4d08988b93feb5c9ae36e22114","The purpose of this study was to determine the importance of infrared vs. visual features, textural vs. spectral features, hierarchical vs. single-stage decision logic, and quadratic vs. linear discriminant functions for classification of NOAA-1 visible and infrared tropical cloud data. Both a four-class problem, in which cloud types were grouped into (1) ""low"", (2) ""mix"", (3) ""cirrus"", and (4) ""cumulonimbus"" classes, and a three-class problem, in which the ""mix"" class was excluded, were analyzed. The addition of at least one visual spectral feature to infrared spectral features improved the ability of the classifier to recognize all cloud classes except ""low"". Combining textural features with spectral features did not significantly improve classification results achieved using only spectral features. For the four-class problem, a classification accuracy of 91% was obtained by using a two-stage variation of a single-stage, maximum likelihood classifier. For the three-class problem, classification accuracies of 98% were obtained using either single-stage or hierarchical decision logic and either quadratic or linear discriminant functions. © 1977."
"26642240800;54790498500;56452028600;","Cloud classification of ground-based images using texture-structure features",2014,"10.1175/JTECH-D-13-00048.1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84892459412&doi=10.1175%2fJTECH-D-13-00048.1&partnerID=40&md5=abe36e486711dd4705290df840e83afe","Cloud classification of ground-based images is a challenging task. Recent research has focused on extracting discriminative image features, which are mainly divided into two categories: 1) choosing appropriate texture features and 2) constructing structure features. However, simply using texture or structure features separately may not produce a high performance for cloud classification. In this paper, an algorithm is proposed that can capture both texture and structure information from a color sky image. The algorithm comprises three main stages. First, a preprocessing color census transform (CCT) is applied. The CCT contains two steps: converting red, green, and blue (RGB) values to opponent color space and applying census transform to each component. The CCT can capture texture and local structure information. Second, a novel automatic block assignment method is proposed that can capture global rough structure information. A histogram and image statistics are computed in every block and are concatenated to form a feature vector. Third, the feature vector is fed into a trained support vectormachine (SVM) classifier to obtain the cloud type. The results show that this approach outperforms other existing cloud classificationmethods. In addition, several different color spaces were tested and the results show that the opponent color space is most suitable for cloud classification. Another comparison experiment on classifiers shows that the SVM classifier is more accurate than the k-nearest neighbor (k-NN) and neural networks classifiers. © 2014 American Meteorological Society."
"6602176524;6701781257;6701706067;6603624776;6701751100;","Mesoscale model cloud scheme assessment using satellite observations",2002,"10.1029/2001JD000714","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0010415056&doi=10.1029%2f2001JD000714&partnerID=40&md5=62002e8d941e4fd3b8d2a8e02bb62158","An exploratory evaluation of the explicit cloud scheme of the mesoscale nonhydrostatic (Meso-NH) model has been conducted by comparing synthetic METEOSAT brightness temperatures (BT) to the observed ones. Three different meteorological situations are examined to illustrate the expected degree of accuracy in simulating realistic synthetic BTs in the midlatitude and in the subtropics, with a horizontal grid length ranging from 75 to 12 km. It is shown that the model to satellite approach, which combines the output from a bulk explicit cloud scheme routinely used in mesoscale simulations with a detailed radiative transfer code, offers the possibility of tuning a critical parameter. For instance, tests made with three different values of an ice to snow autoconversion threshold reveal a profound impact on the synthetic BT maps which results in unbiased differences with satellite observations when the appropriate value is selected. The main discrepancies that remain are partly due to errors in the vertical or horizontal placement of the cloud layer or in the amount of condensates, but also due to the lack of subgrid-scale cloudiness in the model. A similar test conducted on the ice water and the liquid water paths confirms the fairly good agreement with retrievals from microwave observations. The paper concludes by discussing the need not only to extend the model to satellite approach to other well-documented cases but also to derive diagnostics from deep convection scheme characteristics in order to include the radiative effect of the convective towers in the generation of synthetic BT maps. Copyright 2002 by the American Geophysical Union."
"6604083018;7006802750;7005992621;","An AVHRR multiple cloud-type classification package",2000,"10.1175/1520-0450(2000)039<0125:AAMCTC>2.0.CO;2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0034005565&doi=10.1175%2f1520-0450%282000%29039%3c0125%3aAAMCTC%3e2.0.CO%3b2&partnerID=40&md5=c8bbeed9c8947df3c309d01e0009d29f","Using imagery from NOAA's Advanced Very High Resolution Radiometer (AVHRR) orbiting sensor, one of the authors (RLB) earlier developed a probabilistic neural network cloud classifier valid over the world's maritime regions. Since then, the authors have created a database of nearly 8000 16 × 16 pixel cloud samples (from 13 Northern Hemispheric land regions) independently classified by three experts. From these samples, 1605 were of sufficient quality to represent 11 conventional cloud types (including clear). This database serves as the training and testing samples for developing a classifier valid over land. Approximately 200 features, calculated from a visible and an infrared channel, form the basis for the computer vision analysis. Using a 1-nearest neighbor classifier, meshed with a feature selection method using backward sequential selection, the authors select the fewest features that maximize classification accuracy. In a leave-one-out test, overall classification accuracies range from 86% to 78% for the water and land classifiers, with accuracies at 88% or greater for general height-dependent groupings. Details of the databases, feature selection method, and classifiers, as well as example simulations, are presented."
"55802031900;6603126554;15726427000;7102171439;7005528388;","Validation of MODIS cloud mask and multilayer flag using CloudSat-CALIPSO cloud profiles and a cross-reference of their cloud classifications",2016,"10.1002/2016JD025239","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84990181984&doi=10.1002%2f2016JD025239&partnerID=40&md5=dce8864355418b191bbd4f00d735aab3","Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 cloud observations (MYD06) at 1 km are collocated with daytime CloudSat-Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) (C-C) cloud vertical structures (2B-CLDCLASS-LIDAR). For 2007-2010, over 267 million C-C cloud profiles are used to (1) validate MODIS cloud mask and cloud multilayer flag and (2) cross-reference between C-C cloud types and MODIS cloud regimes defined by joint histograms of cloud top pressure (CTP) and cloud optical depth (τ). Globally, of total observations, C-C reports 27.1%clear and 72.9%cloudy, whereasMODIS reports 30.0% confidently clear and 58.7% confidently cloudy, with the rest 7.1% as probably clear and 4.2% as probably cloudy. Agreement between MODIS and C-C is 77.8%, with 20.9% showing both clear and 56.9% showing both cloudy. The 9.1%of observations are clear inMODIS but cloudy in C-C, indicating cloudsmissed by MODIS; 1.8% of observations are cloudy inMODIS but clear in C-C, likely due to aerosol/dust or surface snow layers misidentified by MODIS. C-C reports 47.4/25.5% single-layer/multilayer clouds, while MODIS reports 26.7/14.0%. For C-C single-layer clouds, ~90% of tropical MODIS high (CTP<440 hPa) and optically thin (τ<3.6) clouds are identified as cirrus and ~60% of high and optically thick (τ>23) clouds are recognized as deep convective in C-C. Approximately 70% of MODIS low-level (CTP>680 hPa) clouds are classified as stratocumulus in C-C regardless of region and optical thickness. No systematic relationship exists between MODIS middle-level (680<CTP<440 hPa) clouds and C-C cloud types, largely due to different definitions adopted. © 2016. American Geophysical Union. All Rights Reserved."
"57202803751;6603562731;7005473082;24538154100;57208765879;7102063963;","Detection of multi-layer and vertically-extended clouds using A-train sensors",2010,"10.5194/amt-3-233-2010","https://www.scopus.com/inward/record.uri?eid=2-s2.0-77956199396&doi=10.5194%2famt-3-233-2010&partnerID=40&md5=fff7ab52a0eca5db132ef30b2b832569","The detection of multiple cloud layers using satellite observations is important for retrieval algorithms as well as climate applications. In this paper, we describe a relatively simple algorithm to detect multiple cloud layers and distinguish them from vertically-extended clouds. The algorithm can be applied to coincident passive sensors that derive both cloud-top pressure from the thermal infrared observations and an estimate of solar photon pathlength from UV, visible, or near-IR measurements. Here, we use data from the A-train afternoon constellation of satellites: cloud-top pressure, cloud optical thickness, the multi-layer flag from the Aqua MODerate-resolution Imaging Spectroradiometer (MODIS) and the optical centroid cloud pressure from the Aura Ozone Monitoring Instrument (OMI). For the first time, we use data from the CloudSat radar to evaluate the results of a multi-layer cloud detection scheme. The cloud classification algorithms applied with different passive sensor configurations compare well with each other as well as with data from CloudSat.<br> <br> We compute monthly mean fractions of pixels containing multi-layer and vertically-extended clouds for January and July 2007 at the OMI spatial resolution (12 km×24 km at nadir) and at the 5 km×5 km MODIS resolution used for infrared cloud retrievals. There are seasonal variations in the spatial distribution of the different cloud types. The fraction of cloudy pixels containing distinct multi-layer cloud is a strong function of the pixel size. Globally averaged, these fractions are approximately 20% and 10% for OMI and MODIS, respectively. These fractions may be significantly higher or lower depending upon location. There is a much smaller resolution dependence for fractions of pixels containing vertically-extended clouds (∼20% for OMI and slightly less for MODIS globally), suggesting larger spatial scales for these clouds. We also find higher fractions of vertically-extended clouds over land as compared with ocean, particularly in the tropics and summer hemisphere."
"26031036300;6701416377;7202667312;7201950609;","Remote sensing of precipitation over Indian land and oceanic regions by synergistic use of multisatellite sensors",2010,"10.1029/2009JD012157","https://www.scopus.com/inward/record.uri?eid=2-s2.0-77951803492&doi=10.1029%2f2009JD012157&partnerID=40&md5=06984bad65b3a53bfe33718c56bf77c8","In the present study, an attempt was made to estimate rainfall by synergistically analyzing collocated thermal infrared (TIR) brightness temperatures from Meteosat along with rainfall estimates from active microwave precipitation radar (PR) on the Tropical Rainfall Measuring Mission (TRMM) over Indian land and oceanic regions. In this study, we used broad and frequent TIR measurements from a geostationary satellite for rainfall estimation, calibrating them with sparse but more accurate PR rain rates. To make the algorithm robust, we used a two-step procedure. First, a cloud classification scheme was applied to TIR measurements using the 6.7 mm water vapor channel and TIR radiances to delineate the rain-bearing clouds. Next, the concurrent TIR and PR observations were used to establish a regression relation between them. The relationship thus established was used to estimate rainfall from TIR measurements by applying it to rain-producing systems during southwest and northeast monsoons and tropical cyclones. Comparisons were made with TRMM-merged (3B42 V6) data, Global Precipitation Climatology Project (GPCP) monthly rain rate data, ground-based rain gauge observations from automatic weather stations, and Doppler weather radar over India. The results from combined infrared and microwave sensors were in very good agreement with the ground-based measurements, TRMM-3B42 V6, as well as GPCP. Copyright 2010 by the American Geophysical Union."
"56201438000;7201707538;","Satellite cloud classification and rain-rate estimation using multispectral radiances and measures of spatial texture",1996,"10.1175/1520-0450(1996)035<0839:SCCARR>2.0.CO;2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0030431084&doi=10.1175%2f1520-0450%281996%29035%3c0839%3aSCCARR%3e2.0.CO%3b2&partnerID=40&md5=538a5244ffdcf57fa7053fc01dde400f","Twelve months of Southern Hemisphere (maritime) midlatitudes Advanced Very High Resolution Radiometer local area coverage data at full radiometric and spatial resolution have been collocated with rain-rate data from three Doppler weather radars. Using an interactive computing environment, large independent samples of cloudy-altocumulus, cumulonimbus, cirrostratus, cumulus, nimbostratus, stratocumulus, stratus-and cloud-free scenes have been identified (labeled) in the collocated data. Accurate labeling was ensured by providing a supervising-analyst access to appropriate diagnostics, including difference and ratio channels, 3.7-μm reflected and emissive components, spectral histograms, Coakley-Bretherton spatial coherence plots, mean, standard deviation, and gray-level difference (GLD) statistics. This analysis yielded 4323 cloud and no-cloud samples at a spatial resolution of 8 × 8 instantaneous fields of view (IFOV), from 257 NOAA-11 and NOAA-12 orbits. Bayesian cloud discriminant functions calculated from the labeled samples and utilizing feature vectors including radiometric and GLD spatial characteristics successfully classified scenes into one of the seven cloud and no-cloud classes with significant skill (Kuipers' performance index 0.63). Utilizing the posterior probability of the classified samples enabled some clouds that were classified erroneously to be identified (and discarded), improving the skill of the discriminant functions by an additional 10% or so. Removing the GLD statistics from the feature vector reduced the skill of the cloud discrimination by about 20% (relative to the nondiscarding discriminant function), while increasing the misclassification of midlevel clouds. However, some cloud classes can only be discriminated from their multispectral signatures. Day and night discriminant functions show similar skill. Within raining cloud classes, rain rate has been related to the spatial and radiometric characteristics of the cloud. The skill of the rain-rate estimates is dependent on the cloud type. For nimbostratus and altocumulus classes 20%-25% of the rain-rate variation can be explained by predictors that measure the temperature, spatial texture, and degree of isotropy in the sampled clouds. Raining and nonraining samples of altocumulus, cumulus, cirrostratus, and nimbostratus can be delineated with at least 60% accuracy. This approach, whereby cloud classes are identified then rain rates estimated as a function of cloud type, would seem to resolve some of the usual problems associated with rain-rate analyses from midlatitudes infrared and visible satellite data. It also extends rain-rate diagnosis to nonconvective (frontal) cloud systems."
"35262555900;36605450500;7005523706;7004011998;8632797000;35741822600;","Investigating the applicability of error correction ensembles of satellite rainfall products in river flow simulations",2013,"10.1175/JHM-D-12-074.1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84884184498&doi=10.1175%2fJHM-D-12-074.1&partnerID=40&md5=e9e4549133a217b1a706077552624c0b","This study uses a stochastic ensemble-based representation of satellite rainfall error to predict the propagation in flood simulation of three quasi-global-scale satellite rainfall products across a range of basin scales. The study is conducted on the Tar-Pamlico River basin in the southeastern United States based on 2 years of data (2004 and 2006). The NWS Multisensor Precipitation Estimator (MPE) dataset is used as the reference for evaluating three satellite rainfall products: the Tropical Rainfall Measuring Mission (TRMM) real-time 3B42 product (3B42RT), the Climate Prediction Center morphing technique (CMORPH), and the Precipitation Estimation from Remotely Sensed Imagery Using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS). Both ground-measured runoff and streamflow simulations, derived from the NWS Research Distributed Hydrologic Model forced with the MPE dataset, are used as benchmarks to evaluate ensemble streamflow simulations obtained by forcing the model with satellite rainfall corrected using stochastic error simulations from a two-dimensional satellite rainfall error model (SREM2D). The ability of the SREM2D ensemble error corrections to improve satellite rainfall-driven runoff simulations and to characterize the error variability of those simulations is evaluated. It is shown that by applying the SREM2D error ensemble to satellite rainfall, the simulated runoff ensemble is able to envelope both the reference runoff simulation and observed streamflow. The best (uncorrected) product is 3B42RT, but after applying SREM2D, CMORPH becomes the most accurate of the three products in the prediction of runoff variability. The impact of spatial resolution on the rainfall-to-runoff error propagation is also evaluated for a cascade of basin scales (500-5000km2). Results show a doubling in the bias from rainfall to runoff at all basin scales. Significant dependency to catchment area is exhibited for the random error propagation component © 2013 American Meteorological Society."
"24315205000;7003548068;7102953444;","Increasing cloud cover in the 20th century: Review and new findings in Spain",2012,"10.5194/cp-8-1199-2012","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84869012017&doi=10.5194%2fcp-8-1199-2012&partnerID=40&md5=1c35b0f741f34afecedaea1be41bf84c","Visual observations of clouds have been performed since the establishment of meteorological observatories during the early instrumental period, and have become more systematic and reliable after the mid-19th century due to the establishment of the first national weather services. During the last decades a large number of studies have documented the trends of the total cloud cover (TCC) and cloudy types; most of these studies focus on the trends since the second half of the 20th century. Due to the lower reliability of former observations, and the fact that most of this data is not accessible in digital format, there is a lack of studies focusing on the trends of cloudiness since the mid-19th century. In the first part, this work attempts to review previous studies analyzing TCC changes with information covering at least the first half of the 20th century. Then, the study analyses a database of cloudiness observations in Southern Europe (Spain) since the second half of the 19th century. Specifically, monthly TCC series were reconstructed since 1866 by means of a so-called parameter of cloudiness, calculated from the number of cloudless and overcast days. These estimated TCC series show a high interannual and decadal correlation with the observed TCC series originally measured in oktas. After assessing the temporal homogeneity of the estimated TCC series, the mean annual and seasonal series for the whole of Spain and several subregions were calculated. The mean annual TCC shows a general tendency to increase from the beginning of the series until the 1960s; at this point, the trend becomes negative. The linear trend for the annual mean series, estimated over the 1866-2010 period, is a highly remarkable (and statistically significant) increase of +0.44% per decade, which implies an overall increase of more than +6% during the analyzed period. These results are in line with the majority of the trends observed in many areas of the world in previous studies, especially for the records before the 1950s when a widespread increase of TCC can been considered as a common feature. © Author(s) 2012."
"8265648400;26325493600;8545284000;14123588900;9246472600;","Demonstration of a virtual active hyperspectral lidar in automated point cloud classification",2011,"10.1016/j.isprsjprs.2011.04.002","https://www.scopus.com/inward/record.uri?eid=2-s2.0-80052815265&doi=10.1016%2fj.isprsjprs.2011.04.002&partnerID=40&md5=88155f80c310d6e3db8aea0e0e608f90","In this paper, a measurement system for the acquisition of a virtual hyperspectral LiDAR dataset is presented. As commercial hyperspectral LiDARs are not yet available, the system provides a novel type of data for the testing and developing of future hyperspectral LiDAR algorithms. The measurement system consists of two parts: first, backscattered reflectance spectra are collected using a spectrometer and a cutting-edge technology, white-light supercontinuum laser source; second, a commercial monochromatic LiDAR system is used for ranging. A virtual hyperspectral LiDAR dataset is produced by data fusion. Such a dataset was collected on a Norway spruce (Picea abies) sample. The performance of classification was tested using an experimental hyperspectral algorithm based on a novel combination of the Spectral Correlation Mapper and a region growing algorithm. The classifier was able to automatically distinguish between needles, branches and background, in other words, perform a difficult task using only traditional TLS data. © 2011 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS)."
"55953510100;35561327100;7202105404;","A fuzzy rule based approach to cloud cover estimation",2006,"10.1016/j.rse.2005.11.005","https://www.scopus.com/inward/record.uri?eid=2-s2.0-32244439895&doi=10.1016%2fj.rse.2005.11.005&partnerID=40&md5=7f1c022cec4496d21bb8420dfcc0d5c0","A fuzzy rule based cloud classification scheme is proposed to estimate the cloud cover from satellite imagery. METEOSAT-5 images are classified into three classes: cloudy, partially cloudy, and clear sky. Five features, which measure the temporal and spatial properties of visible (VIS) and infrared (IR) images of METEOSAT-5, are used for this. The proposed classifier finds out a few human understandable rules (fuzzy rules) using exploratory data analysis. A novel attribute of the system is that it analyzes the behavior of misclassifications during training (i.e., typical mistakes) to extract a few more rules which are augmented to the initial rule base to improve its performance. The scheme is tested on images other than the training image(s) and the performance is found to be quite satisfactory. A post-processing scheme is also developed, which utilizes experts' knowledge to generate additional rules to account for coastal region, sunglint areas, and snow-covered Himalayan region. This improves the performance of the system further. Finally, the classification results are compared with multispectral threshold tests, surface synoptic observations, and total cloud cover (tcdc) of reanalysis data produced by National Centers for Environmental Prediction (NCEP) and National Center for Atmospheric Research (NCAR). The high accuracy achieved by the proposed method may be attributed to (1) better design philosophy of classifiers; (2) good choice for the feature vectors; (3) accurate labeling of training data; and (4) exploitation of experts' knowledge. © 2005 Elsevier Inc. All rights reserved."
"55331455800;56136768200;7005052907;7401526171;30267501800;","Flood forecasting and inundation mapping using HiResFlood-UCI and near-real-time satellite precipitation data: The 2008 Iowa flood",2015,"10.1175/JHM-D-14-0212.1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84941283467&doi=10.1175%2fJHM-D-14-0212.1&partnerID=40&md5=03076786f1e96e6a74ede7518d299727","Floods are among the most devastating natural hazards in society. Flood forecasting is crucially important in order to provide warnings in time to protect people and properties fromsuch disasters. This research applied the high-resolution coupled hydrologic-hydraulic model from the University of California, Irvine, named HiResFlood-UCI, to simulate the historical 2008 Iowa flood. HiResFlood-UCI was forced with the near-realtime Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS) and NEXRAD Stage 2 precipitation data. The model was run using the a priori hydrologic parameters and hydraulicManning n values from lookup tables. The model results were evaluated in two aspects: point comparison using USGS streamflow and areal validation of inundation maps usingUSDA's flood extentmaps derived fromAdvancedWide Field Sensor (AWiFS) 56-mresolution imagery. The results show that the PERSIANN-CCS simulation tends to capture the observed hydrograph shape better than Stage 2 (minimum correlation of 0.86 for PERSIANN-CCS and 0.72 for Stage 2); however, at most of the stream gauges, Stage 2 simulation provides more accurate estimates of flood peaks compared to PERSIANNCCS (49%-90%bias reduction from PERSIANN-CCS to Stage 2). The simulation in both cases shows a good agreement (0.67 and 0.73 critical success index for Stage 2 and PERSIANN-CCS simulations, respectively) with the AWiFS flood extent. Since the PERSIANN-CCS simulation slightly underestimated the discharge, the probability of detection (0.93) is slightly lower than that of the Stage 2 simulation (0.97).As a trade-off, the false alarm rate for the PERSIANN-CCS simulation (0.23) is better than that of the Stage 2 simulation (0.31). © American Meteorological Society."
"55502994400;6602463657;6602390932;55056533200;8525148200;56175387100;37078009200;","Cloud detection and classification based on MAX-DOAS observations",2014,"10.5194/amt-7-1289-2014","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84901245279&doi=10.5194%2famt-7-1289-2014&partnerID=40&md5=1d7108bd3b48c4adfdaf1c9b96378bb5","Multi-axis differential optical absorption spectroscopy (MAX-DOAS) observations of aerosols and trace gases can be strongly influenced by clouds. Thus, it is important to identify clouds and characterise their properties. In this study we investigate the effects of clouds on several quantities which can be derived from MAX-DOAS observations, like radiance, the colour index (radiance ratio at two selected wavelengths), the absorption of the oxygen dimer O4 and the fraction of inelastically scattered light (Ring effect). To identify clouds, these quantities can be either compared to their corresponding clear-sky reference values, or their dependencies on time or viewing direction can be analysed. From the investigation of the temporal variability the influence of clouds can be identified even for individual measurements. Based on our investigations we developed a cloud classification scheme, which can be applied in a flexible way to MAX-DOAS or zenith DOAS observations: in its simplest version, zenith observations of the colour index are used to identify the presence of clouds (or high aerosol load). In more sophisticated versions, other quantities and viewing directions are also considered, which allows subclassifications like, e.g., thin or thick clouds, or fog. We applied our cloud classification scheme to MAX-DOAS observations during the Cabauw intercomparison campaign of Nitrogen Dioxide measuring instruments (CINDI) campaign in the Netherlands in summer 2009 and found very good agreement with sky images taken from the ground and backscatter profiles from a lidar. © Author(s) 2014."
"36195159400;6602981892;","Concepts and techniques for integration, analysis and visualization of massive 3D point clouds",2014,"10.1016/j.compenvurbsys.2013.07.004","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84897024128&doi=10.1016%2fj.compenvurbsys.2013.07.004&partnerID=40&md5=64623ce9979326e062fe529ffbf8231e","Remote sensing methods, such as LiDAR and image-based photogrammetry, are established approaches for capturing the physical world. Professional and low-cost scanning devices are capable of generating dense 3D point clouds. Typically, these 3D point clouds are preprocessed by GIS and are then used as input data in a variety of applications such as urban planning, environmental monitoring, disaster management, and simulation. The availability of area-wide 3D point clouds will drastically increase in the future due to the availability of novel capturing methods (e.g., driver assistance systems) and low-cost scanning devices. Applications, systems, and workflows will therefore face large collections of redundant, up-to-date 3D point clouds and have to cope with massive amounts of data. Hence, approaches are required that will efficiently integrate, update, manage, analyze, and visualize 3D point clouds. In this paper, we define requirements for a system infrastructure that enables the integration of 3D point clouds from heterogeneous capturing devices and different timestamps. Change detection and update strategies for 3D point clouds are presented that reduce storage requirements and offer new insights for analysis purposes. We also present an approach that attributes 3D point clouds with semantic information (e.g., object class category information), which enables more effective data processing, analysis, and visualization. Out-of-core real-time rendering techniques then allow for an interactive exploration of the entire 3D point cloud and the corresponding analysis results. Web-based visualization services are utilized to make 3D point clouds available to a large community. The proposed concepts and techniques are designed to establish 3D point clouds as base datasets, as well as rendering primitives for analysis and visualization tasks, which allow operations to be performed directly on the point data. Finally, we evaluate the presented system, report on its applications, and discuss further research challenges. © 2013 Elsevier Ltd."
"6504118722;8629285200;55889853300;7003529616;6603278694;","Cloud classification using the textural features of Meteosat images",2004,"10.1080/01431160410001735120","https://www.scopus.com/inward/record.uri?eid=2-s2.0-8744232119&doi=10.1080%2f01431160410001735120&partnerID=40&md5=ad91c125ff89a774c44747d464dbea74","The sum and difference histogram approach is applied to the assessment of the textural features of Meteosat images and the resulting textural parameters are used to classify the clouds appearing over North Africa. The images under consideration were taken by Meteosat in the visible and infrared bands during the month of December 1994. They cover North Africa, the Mediterranean Sea, Europe and the Atlantic Ocean. The visible images are first processed following four directions, namely 0°, 45°, 90° and 135°. For each direction, the textural parameters are calculated from the sum and difference histograms of grey levels. To take into account the effect of the texture anisotropy in the classification, the Karhunen-Loeve transformation (KLT) is then used. The minimum number of components representing these parameters is obtained with the slightest loss of information. Finally, after averaging the textural parameters over the four directions, each image of the database is divided into homogeneous classes by using the K-means algorithm. The approach thus described, tested on one of the images of the Brodatz album, shows that the classification ratio is greater than 96%. The segmentation of the Meteosat images is performed using both the textural parameters of visible images and the brightness of infrared images. It is then found that the different ground and cloud types are classified with proper accuracy. The implementation of this method to satellite images advantageously reduces the classification time, which is found to be three times smaller than that required by classical techniques of image processing. © 2004 Taylor & Francis Ltd."
"26659897600;55471597600;7003601758;","Comparison of a split-window and a multi-spectral cloud classification for MODIS observations",2003,"10.2151/jmsj.81.623","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0242583942&doi=10.2151%2fjmsj.81.623&partnerID=40&md5=0577246e92a3b934c4aa04535418609a","Results of the split-window cloud retrieval method and the new Meteosat Second Generation cloud analysis method (MSG/CLA), have been compared for MODIS data over the west Atlantic Ocean. Very good agreement is obtained for the classification of optically thick ice and water clouds. Differences are found for thin cirrus, thin water clouds and at cloud edges. These differences are explained by the fact that MSG/CLA also uses spectral channels of 3.9, 6.2, and 8.7 μm in addition to the split-window, which provides information over and above the split-window observations. Some of the disagreement at cloud edges is interpreted as inter-channel miss-alignment. The analysis in this study also confirms that an optically thin water cloud can be correctly classified by the MSG/CLA method."
"57213258640;35598959400;","An automated neural network cloud classifier for use over land and ocean surfaces",1997,"10.1175/1520-0450(1997)036<1346:AANNCC>2.0.CO;2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0031401868&doi=10.1175%2f1520-0450%281997%29036%3c1346%3aAANNCC%3e2.0.CO%3b2&partnerID=40&md5=cad7424112eea2cfbaab3c114b145ad7","An automated neural network cloud classifier that functions over both land and ocean backgrounds is presented. Motivated by the development of a combined visible, infrared, and microwave rain-rate retrieval algorithm for use with data from the 1997 Tropical Rainfall Measuring Mission (TRMM), an automated cloud classification technique is sought to discern different types of clouds and, hence, different types of precipitating systems from Advanced Very High Resolution Radiometer (AVHRR) type imagery. When this technique is applied to TRMM visible-infrared imagery, it will allow the choice of a passive microwave rain-rate algorithm, which performs well for the observed precipitation type, theoretically increasing accuracy at the instantaneous level when compared with the use of any single microwave algorithm. A neural network classifier, selected because of the strengths of neural networks with respect to within-class variability and nonnormal cluster distributions, is developed, trained, and tested on AVHRR data received from three different polar-orbiting satellites and spanning the continental United States and adjacent waters, as well as portions of the Tropics from the Tropical Ocean and Global Atmosphere Coupled Ocean-Atmosphere Response Experiment (TOGA COARE). The results are analyzed and suggestions are made for future work on this technique. The network selected the correct class for 96% of the training samples and 82% of the test samples, indicating that this type of approach to automated cloud classification holds considerable promise and is worthy of additional research and refinement."
"35331137500;7003283811;7005729142;6506385754;7501447027;7004198777;56332567800;","A study of cirrus ice particle size distribution using TC4 observations",2010,"10.1175/2009JAS3114.1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-77953234205&doi=10.1175%2f2009JAS3114.1&partnerID=40&md5=a0df922caa6f584249c2776dc587eb08","An analysis of two days of in situ observations of ice particle size spectra, in convectively generated cirrus, obtained during NASA's Tropical Composition, Cloud, and Climate Coupling (TC4) mission is presented. The observed spectra are examined for their fit to the exponential, gamma, and lognormal function distributions. Characteristic particle size and concentration density scales are determined using two (for the exponential) or three (for the gamma and lognormal functions) moments of the spectra. It is shown that transformed exponential, gamma, and lognormal distributions should collapse onto standard curves. An examination of the transformed spectra, and of deviations of the transformed spectra from the standard curves, shows that the lognormal function provides a better fit to the observed spectra. © 2010 American Meteorological Society."
"57089599900;54790498500;56452028600;","DeepCloud: Ground-Based Cloud Image Categorization Using Deep Convolutional Features",2017,"10.1109/TGRS.2017.2712809","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85022045459&doi=10.1109%2fTGRS.2017.2712809&partnerID=40&md5=eca56af45aca5c3e478e74d037131efe","Accurate ground-based cloud image categorization is a critical but challenging task that has not been well addressed. One of the essential issues that affect the performance is to extract the representative visual features. Nearly all of the existing methods rely on the hand-crafted descriptors (e.g., local binary patterns, CENsus TRsansform hISTogram, and scale-invariant feature transform). Their limited discriminative power indeed leads to the unsatisfactory performance. To alleviate this, we propose ""DeepCloud"" as a novel cloud image feature extraction approach by resorting to the deep convolutional visual features. In the recent years, the deep convolutional neural network (CNN) has achieved the promising results in lots of computer vision and image understanding fields. Nevertheless, it has not been applied to cloud image classification yet. Thus, we actually pay the first effort to fill this blank. Since cloud image classification can be attributed to a multi-instance learning problem, simply employing the convolutional features within CNN cannot achieve the promising result. To address this, Fisher vector encoding is applied to executing the spatial feature aggregation and high-dimensional feature mapping on the raw deep convolutional features. Moreover, the hierarchical convolutional layers are used simultaneously to capture the fine textural characteristics and high-level semantic information in the unified manner. To further leverage the performance, a cloud pattern mining and selection method are also proposed. It targets at finding the discriminative local patterns to better distinguish the different kinds of clouds. The experiments on a challenging ground-based cloud image data set demonstrate the superiority of the proposition over the state-of-the-art methods. © 2017 IEEE."
"26538406800;7003495004;56521441300;57214286389;55232388000;8918197800;36124109400;57206531303;7006497723;6701618694;","Cloud observations in Switzerland using hemispherical sky cameras",2015,"10.1002/2014JD022643","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84923098815&doi=10.1002%2f2014JD022643&partnerID=40&md5=6595ebd7c66ef5752ecfc3bc0d97c9c6","We present observations of total cloud cover and cloud type classification results from a sky camera network comprising four stations in Switzerland. In a comprehensive intercomparison study, records of total cloud cover from the sky camera, long-wave radiation observations, Meteosat, ceilometer, and visual observations were compared. Total cloud cover from the sky camera was in 65-85% of cases within ±1 okta with respect to the other methods. The sky camera overestimates cloudiness with respect to the other automatic techniques on average by up to 1.1 ± 2.8 oktas but underestimates it by 0.8 ± 1.9 oktas compared to the human observer. However, the bias depends on the cloudiness and therefore needs to be considered when records from various observational techniques are being homogenized. Cloud type classification was conducted using the k-Nearest Neighbor classifier in combination with a set of color and textural features. In addition, a radiative feature was introduced which improved the discrimination by up to 10%. The performance of the algorithm mainly depends on the atmospheric conditions, site-specific characteristics, the randomness of the selected images, and possible visual misclassifications: The mean success rate was 80-90% when the image only contained a single cloud class but dropped to 50-70% if the test images were completely randomly selected and multiple cloud classes occurred in the images. © 2015. The Authors."
"57212000722;36704804900;6701416377;7102063144;","Estimation of Indian summer monsoon rainfall using Kalpana-1 VHRR data and its validation using rain gauge and GPCP data",2010,"10.1007/s00703-010-0106-8","https://www.scopus.com/inward/record.uri?eid=2-s2.0-78650020932&doi=10.1007%2fs00703-010-0106-8&partnerID=40&md5=612c2a9593d64a5ef512e96b7f4a52b3","In the present study, an attempt has been made to estimate and validate the daily and monthly rainfall during the Indian summer monsoon seasons of 2008 and 2009 using INSAT (Indian National Satellite System) Multispectral Rainfall Algorithm (IMSRA) technique utilizing Kalpana-1 very high resolution radiometer (VHRR) measurements. In contrary to infrared (IR), microwave (MW) rain rates are based on measurements that sense precipitation in clouds and do not rely merely on cloud top temperature. Geostationary satellites provide broad coverage and frequent refresh measurements but microwave measurements are accurate but sparse. IMSRA technique is the combination of the infrared and microwave measurements which make use of the best features of both IR- and MW-based rainfall estimates. The development of this algorithm included two major steps: (a) classification of rain-bearing clouds using proper cloud classification scheme utilizing Kalpana-1 IR and water vapor (WV) brightness temperatures (Tb) and (b) collocation of Kalpana-1 IR brightness temperature with Tropical Rainfall Measuring Mission (TRMM)-Precipitation Radar (PR) surface rain rate and establishment of a regression relation between them. In this paper, the capability of IMSRA as an operational algorithm has been tested for the two monsoon seasons 2008 and 2009. For this, IMSRA has been used to estimate daily and monthly rainfall and has been intercompared on daily and monthly scales with TRMM Multisatellite Precipitation Analysis (TMPA)-3B42 V6 product and Global Precipitation Climatology Project (GPCP) rain product during these two monsoon years. The daily and monthly IMSRA rainfall has also been validated against ground-based observations from Automatic Weather Station (AWS) Rain Gauge and Buoy data. The algorithm proved to be in good correlation with AWS data over land up to 0.70 for daily rain estimates except orographic regions like North-East and South-West India and 0.72 for monthly rain estimates. The validation with Buoys gives the reasonable correlation of 0.49 for daily rain estimates and 0.66 for monthly rain estimates over Tropical Indian Ocean. © 2010 Springer-Verlag."
"6602883239;6603505225;6603860837;56999946500;57218944645;57215016417;","Bayesian algorithm for microwave-based precipitation retrieval: Description and application to TMI measurements over ocean",2005,"10.1109/TGRS.2005.844726","https://www.scopus.com/inward/record.uri?eid=2-s2.0-16444369908&doi=10.1109%2fTGRS.2005.844726&partnerID=40&md5=44e73b736ff8f6fd91f0d4c4f0f14062","A physically oriented inversion algorithm to retrieve precipitation from satellite-based passive microwave measurements named the Bayesian algorithm for microwave-based precipitation retrieval (BAMPR) is proposed. First, we illustrate the procedure that BAMPR follows to produce precipitation estimates from observed multichannel brightness temperatures. Retrieval products are the surface rain rates, columnar equivalent water contents, and hydrometeor content profiles, together with the associated estimation uncertainties. Numerical tests performed on simulated measurements show that retrieval errors are reduced when a rain type and pattern classification procedure is employed, and that estimates are quite sensitive to the adopted error model. Finally, for different tropical storms that were observed by the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), we compare the rain retrieved from BAMPR relative to those retrieved from the Goddard Profiling (Gprof) algorithm and the Precipitation Radar-adjusted TMI estimation of rainfall (PATER) algorithm. Despite a similar inversion approach, the algorithms exhibit different performances that can be mainly related to different training databases and retrieval constraints such as cloud classification. © 2005 IEEE."
"26026749200;12042086300;7404016992;8665263500;35431772700;7005140533;18633549200;","Satellite-based precipitation estimation and its application for streamflow prediction over mountainous western U.S. basins",2014,"10.1175/JAMC-D-14-0056.1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84928314792&doi=10.1175%2fJAMC-D-14-0056.1&partnerID=40&md5=96aa811a63db371a46ad3f20bdf74b43","Recognizing the importance and challenges inherent to the remote sensing of precipitation in mountainous areas, this study investigates the performance of the commonly used satellite-based high-resolution precipitation products (HRPPs) over several basins in the mountainous western United States. Five HRPPs [Tropical Rainfall Measuring Mission 3B42 and 3B42-RT algorithms, the Climate Prediction Center morphing technique (CMORPH), Precipitation Estimation from Remotely Sensed Imagery Using Artificial Neural Networks (PERSIANN), and the PERSIANN Cloud Classification System (PERSIANN-CCS)] are analyzed in the present work using ground gauge, gauge-adjusted radar, and CloudSat precipitation products. Using ground observation of precipitation and streamflow, the skill of HRPPs and the resulting streamflow simulations from the Variable Infiltration Capacity hydrological model are cross-compared. HRPPs often capture major precipitation events but seldom capture the observed magnitude of precipitation over the studied region and period (2003-09). Bias adjustment is found to be effective in enhancing the HRPPs and resulting streamflow simulations. However, if not bias adjusted using gauges, errors are typically large as in the lower-level precipitation inputs to HRPPs. The results using collocated Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) and CloudSat precipitation data show that missing data, often over frozen land, and limitations in retrieving precipitation from systems that lack frozen hydrometeors contribute to the observed microwave-based precipitation errors transferred to HRPPs. Over frozen land, precipitation retrievals from infrared sensors and microwave sounders show some skill in capturing the observed precipitation climatology maps. However, infrared techniques often show poor detection skill, and microwave sounding in dry atmosphere remains challenging. By recognizing the sources of precipitation error and in light of the operation of the Global Precipitation Measurement mission, further opportunity for enhancing the current status of precipitation retrievals and the hydrology of cold and mountainous regions becomes available. © 2014 American Meteorological Society."
"26026749200;6602886081;7401526171;7005052907;6603504366;7101600167;","REFAME: Rain estimation using forward-adjusted advection of microwave estimates",2010,"10.1175/2010JHM1248.1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-78651420049&doi=10.1175%2f2010JHM1248.1&partnerID=40&md5=b7c401d931b2e8412b19ff368358760b","A new multiplatform multisensor satellite rainfall estimation technique is proposed in which sequences of Geostationary Earth Orbit infrared (GEO-IR) images are used to advect microwave (MW)-derived precipitation estimates along cloud motion streamlines and to further adjust the rainfall rates using local cloud classification. The main objective of the Rain Estimation using Forward-Adjusted advection of Microwave Estimates (REFAME) is to investigate whether inclusion of GEO-IR information can help to improve the advected MW precipitation rate as it gets farther in time from the previous MW overpass. The technique comprises three steps. The first step incorporates a 2D cloud tracking algorithm to capture cloud motion streamlines through successive IR images. The second step classifies cloudy pixels to a number of predefined clusters using brightness temperature (Tb) gradients between successive IR images along the cloud motion streamlines in combination with IR cloud-top brightness temperatures and textural features. A mean precipitation rate for each cluster is calculated using available MW-derived precipitation estimates. In the third step, the mean cluster precipitation rates are used to adjust MW precipitation intensities advected between available MW overpasses along cloud motion streamlines. REFAME is a flexible technique, potentially capable of incorporating diverse precipitation-relevant information, such as multispectral data. Evaluated over a range of spatial and temporal scales over the conterminous United States, the performance of the full REFAMEalgorithm compared favorably with products incorporating either no cloud tracking or no intensity adjustment. The observed improvements in root-mean-square error and especially in correlation coefficient between REFAME outputs and ground radar observations demonstrate that the new approach is effective in reducing the uncertainties and capturing the variation of precipitation intensity along cloud advection streamlines between MW sensor overpasses. An extended REFAME algorithm combines the adjusted advected MW rainfall rates with infrared-derived precipitation rates in an attempt to capture precipitation events initiating and decaying during the interval between two consecutive MW overpasses. Evaluation statistics indicate that the extended algorithm is effective to capture the life cycle of the convective precipitation, particularly for the interval between microwave overpasses in which precipitation starts or ends. © 2010 American Meteorological Society."
"6506416572;6701754792;23017945100;7006577245;6507679962;6506846397;7003406400;7410084319;8206969400;6602137606;7006146719;7102128820;7007114756;7102410621;6507128092;","Using continuous ground-based radar and lidar measurements for evaluating the representation of clouds in four operational models",2010,"10.1175/2010JAMC2333.1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-77957739358&doi=10.1175%2f2010JAMC2333.1&partnerID=40&md5=85dc46f3e35d106e670a03e4cad1a5ed","The ability of four operational weather forecast models [ECMWF, Action de Recherche Petite Echelle Grande Echelle model (ARPEGE), Regional Atmospheric Climate Model (RACMO), and Met Office] to generate a cloud at the right location and time (the cloud frequency of occurrence) is assessed in the present paper using a two-year time series of observations collected by profiling ground-based active remote sensors (cloud radar and lidar) located at three different sites in western Europe (Cabauw, Netherlands; Chilbolton, United Kingdom; and Palaiseau, France). Particular attention is given to potential biases that may arise from instrumentation differences (especially sensitivity) from one site to another and intermittent sampling. In a second step the statistical properties of the cloud variables involved in most advanced cloud schemes of numerical weather forecast models (ice water content and cloud fraction) are characterized and compared with their counterparts in the models. The two years of observations are first considered as a whole in order to evaluate the accuracy of the statistical representation of the cloud variables in each model. It is shown that all models tend to produce too many high-level clouds, with too-high cloud fraction and ice water content. The midlevel and low-level cloud occurrence is also generally overestimated, with too-low cloud fraction but a correct ice water content. The dataset is then divided into seasons to evaluate the potential of the models to generate different cloud situations in response to different large-scale forcings. Strong variations in cloud occurrence are found in the observations from one season to the same season the following year as well as in the seasonal cycle. Overall, the model biases observed using the whole dataset are still found at seasonal scale, but the models generally manage to well reproduce the observed seasonal variations in cloud occurrence. Overall, models do not generate the same cloud fraction distributions and these distributions do not agree with the observations. Another general conclusion is that the use of continuous ground-based radar and lidar observations is definitely a powerful tool for evaluating model cloud schemes and for a responsive assessment of the benefit achieved by changing or tuning a model cloud parameterization. © 2010 American Meteorological Society."
"6603896143;6508070993;7102084129;","Radar analysis of cloud systems and their rainfall yield in Israel",2004,"10.1560/G68K-30MN-D5V0-KUHU","https://www.scopus.com/inward/record.uri?eid=2-s2.0-3242656637&doi=10.1560%2fG68K-30MN-D5V0-KUHU&partnerID=40&md5=bbe16ae9d417bf6b12db498bfaef16af","This study documents the climatological occurrence of rainfall from different types of rain cloud systems over Israel and the adjacent seas. The rain cloud types are: (a) cold front; (b) cloud systems that develop in the cold sectors of cyclones; and (c) cloudiness of the cyclone center (vortex). The cloud systems within the cold sector include: (a) convection lines; (b) open Benard cells and unorganized cells; (c) the coastal front; and (d) cloud streets. While the warm front rain yield is negligible, the Red Sea trough adds about 5% to the total precipitation in the Mediterranean climate region of the country. The main rainfall yield contribution in the north of the country comes from cold fronts and vortices, while the southern part is dominated by less dynamically and more air-mass-convection-controlled rain cloud systems, such as Benard cells and coastal fronts. Analysis of the cloud pattern on a monthly basis reveals that the cold front is more active during November-December, with the climax of the coastal front in December, and the main vortex activity starts in January. We checked rainy/dry year variations and found that during rainy years, the vortex rain contribution is larger than that of the cold front. The coastal front also contributes more rain during wet years and tends to follow a more southerly route. In dry years, the proportion of vortex/cold front is reversed, and the coastal front rain yield is meager and has a more northerly route. © 2004 Science From Israel/LPPLtd."
"55471597600;7101677832;","Radiative effects of various cloud types as classified by the split window technique over the Eastern Sub-tropical Pacific derived from collocated ERBE and AVHRR data",2002,"10.2151/jmsj.80.1383","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0036968853&doi=10.2151%2fjmsj.80.1383&partnerID=40&md5=d14d2a1b3dda23a3fcad01f6363abdbd","The radiative effects of several cloud types as classified by the split window (11 and 12 μm) technique were studied using coincident and collocated Earth Radiation Budget Experiment (ERBE) S-8 data and Advanced Very High Resolution Radiometer (AVHRR) data from NOAA-9. The parameter investigated was cloud radiative forcing (CRF), the difference between clear and cloudy shortwave flux (SW) and longwave flux (OLR) at the top of the atmosphere. In computing the CRF, the accuracy of clear SW and OLR is essential. Clear scene IDs in the ERBE dataset were evaluated using coincident and collocated AVHRR image data. The mean visible reflectance and SW for clear footprints defined by the ERBE are reasonably small and are 2.7% and 89.0 Wm-2, respectively. However, the values computed using our technique are smaller, 2.7% and 83.9 Wm-2, respectively. The use of collocated AVHRR image data improves clear footprint definition and implies that care should be taken when computing CRF from ERBE data alone. The CRF from several cloud types classified by the split window were compared. Cumulonimbus clouds show the largest impact on top of the atmosphere radiation for both SW and OLR. Cirrus and low-level cumulus clouds have similar effects on OLR, but large differences between them are seen for SW. The impact of low-level cumulus clouds on SW is much larger than that of cirrus clouds. Some optically thin cirrus clouds show positive cloud radiative forcing (warming effect). The relationships between OLR and cloud types (including cloud-free) as classified by the split window technique were investigated. By using brightness temperature differences between the split window channels, OLR estimation is improved for cloud-free and low-level cumulus clouds when compared with OLR estimated by the National Oceanic and Atmospheric Administration (NOAA) operational algorithm."
"7005354212;36931519000;","Application of 1·38 μm imagery for thin cirrus detection in daytime imagery collected over land surfaces",1996,"10.1080/01431169608949154","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0030292276&doi=10.1080%2f01431169608949154&partnerID=40&md5=b4be71ac0f7668781d1ec797df45ea9e","While considerable effort has been expended on research into the analysis of optically thin cirrus clouds, the global detection and accurate identification of these clouds remains inadequate, especially in daytime meteorological satellite imagery collected over land surfaces. Recently, 1·38 μm imagery was recommended for the improved detection of these thin cirrus clouds. Since this channel is centred on a strong water vapour absorption band and water vapour is concentrated in the lower atmosphere, incident solar energy in the 1·38 μm spectral band is strongly attenuated once prior to reaching the Earth's surface and a second time after being reflected back toward space under normal atmospheric conditions. Thus, it has been postulated that any energy measured by an airborne (or space-borne) radiometer operating in this spectral band would originate from scattering off of mid-level water and high-level ice clouds, making even thin cirrus readily detectable. While initial results have been encouraging, more quantitative analyses are needed to assess the value of 1·38 μm imagery as a candidate for the next generation of polar-orbiting meteorological satellites. Thus, this project investigates the potential for improved thin cirrus detection in daytime imagery collected over land surfaces using scenes of nearly coincident Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensor data, which collects 1·38 μm imagery, and Advanced Very High Resolution Radiometer (AVHRR) imagery collected by National Oceanic and Atmospheric Administration (NOAA) operational meteorological satellites. Results show that the addition of 1·38 μm imagery significantly improves the detection of optically thin cirrus clouds in these daytime data. However, the 1·38 μm imagery does not mask all energy reflected by all surfaces as suggested previously. Thus, the accurate use of 1·38 μm data in an automated cloud classification algorithm requires the development of a detection threshold that varies with surface albedo in the 1·38 μm band and atmospheric water vapour concentration. Meanwhile, it is concluded that imagery collected in the 1·38 μm band significantly improves the detection of optically thin cirrus clouds in daytime imagery collected over land surfaces and would be a valuable addition to future meteorological satellite sensors. © 1996 Taylor & Francis Ltd."
"57218130381;7005126327;6601922531;7006813055;","Polar stratospheric clouds and volcanic aerosol during spring 1992 over McMurdo Station, Antarctica: lidar and particle counter comparisons",1995,"10.1029/95jd02029","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0029502039&doi=10.1029%2f95jd02029&partnerID=40&md5=5447f053dba0c7f947d8c68e84fd802b","Coordinated observations with lidar and balloon-borne particle counters were used to characterize polar stratospheric clouds and to estimate a particle index of refraction. The index of refraction was estimated from comparisons of calculated and measured scattering ratios at a wavelength of 532 nm. The clouds, measured from McMurdo Station, Antarctica (78°S), were observed above 11 km at temperatures below 198 K and were divided into three classes based on their scattering properties and particle size. -from Authors"
"8658386900;37018824600;57198616562;35478813200;34881780600;6701754792;7406215388;57204253860;7102866124;","Convective cloud vertical velocity and mass-flux characteristics from radar wind profiler observations during GoAmazon2014/5",2016,"10.1002/2016JD025303","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84999836812&doi=10.1002%2f2016JD025303&partnerID=40&md5=8d475b4889098a27a6a57bf4404b13e8","A radar wind profiler data set collected during the 2 year Department of Energy Atmospheric Radiation Measurement Observations and Modeling of the Green Ocean Amazon (GoAmazon2014/5) campaign is used to estimate convective cloud vertical velocity, area fraction, and mass flux profiles. Vertical velocity observations are presented using cumulative frequency histograms and weighted mean profiles to provide insights in a manner suitable for global climate model scale comparisons (spatial domains from 20 km to 60 km). Convective profile sensitivity to changes in environmental conditions and seasonal regime controls is also considered. Aggregate and ensemble average vertical velocity, convective area fraction, and mass flux profiles, as well as magnitudes and relative profile behaviors, are found consistent with previous studies. Updrafts and downdrafts increase in magnitude with height to midlevels (6 to 10 km), with updraft area also increasing with height. Updraft mass flux profiles similarly increase with height, showing a peak in magnitude near 8 km. Downdrafts are observed to be most frequent below the freezing level, with downdraft area monotonically decreasing with height. Updraft and downdraft profile behaviors are further stratified according to environmental controls. These results indicate stronger vertical velocity profile behaviors under higher convective available potential energy and lower low-level moisture conditions. Sharp contrasts in convective area fraction and mass flux profiles are most pronounced when retrievals are segregated according to Amazonian wet and dry season conditions. During this deployment, wet season regimes favored higher domain mass flux profiles, attributed to more frequent convection that offsets weaker average convective cell vertical velocities. © 2016. American Geophysical Union. All Rights Reserved."
"55746159100;35509639400;","Influence of low-cloud radiative effects on tropical circulation and precipitation",2015,"10.1002/2013MS000288","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84937042731&doi=10.1002%2f2013MS000288&partnerID=40&md5=583f64411756f8aecd29ad91065e4221","Low-level clouds, which constitute the most prevalent cloud type over tropical oceans, exert a radiative cooling within the planetary boundary layer. By using an atmospheric general circulation model, we investigate the role that this cloud radiative cooling plays in the present-day climate. Low-cloud radiative effects are found to increase the tropics-wide precipitation, to strengthen the winds at the surface of the tropical oceans, and to amplify the atmospheric overturning circulation. An analysis of the water and energy budgets of the atmosphere reveals that most of these effects arises from the strong coupling of cloud-radiative cooling with turbulent fluxes at the ocean surface. The impact of cloud-radiative effects on atmospheric dynamics and precipitation is shown to occur on very short time scales (a few days). Therefore, short-term atmospheric forecasts constitute a valuable framework for evaluating the interactions between cloud processes and atmospheric dynamics, and for assessing their dependence on model physics. © 2014. The Authors."
"26026749200;7401526171;6602886081;7005052907;","Daytime precipitation estimation using bispectral cloud classification system",2010,"10.1175/2009JAMC2291.1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-77955562846&doi=10.1175%2f2009JAMC2291.1&partnerID=40&md5=765a31ca7af213b3c7d9b85c07dd2547","Two previously developed Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) algorithms that incorporate cloud classification system (PERSIANN-CCS) and multispectral analysis (PERSIANN-MSA) are integrated and employed to analyze the role of cloud albedo from Geostationary Operational Environmental Satellite-12 (GOES-12) visible (0.65 μm) channel in supplementing infrared (10.7 mm) data. The integrated technique derives finescale (0.04° × 0.04° latitudelongitude every 30 min) rain rate for each grid box through four major steps: 1) segmenting clouds into a number of cloud patches using infrared or albedo images; 2) classification of cloud patches into a number of cloud types using radiative, geometrical, and textural features for each individual cloud patch; 3) classification of each cloud type into a number of subclasses and assigning rain rates to each subclass using a multidimensional histogram matching method; and 4) associating satellite gridbox information to the appropriate corresponding cloud type and subclass to estimate rain rate in grid scale. The technique was applied over a study region that includes the U.S. landmass east of 115°W. One reference infrared-only and three different bis-pectral (visible and infrared) rain estimation scenarios were compared to investigate the technique's ability to address two major drawbacks of infrared-only methods: 1) underestimating warm rainfall and 2) the inability to screen out no-rain thin cirrus clouds. Radar estimates were used to evaluate the scenarios at a range of temporal (3 and 6 hourly) and spatial (0.04°, 0.08°, 0.12°, and 0.24° latitude-longitude) scales. Overall, the results using daytime data during June-August 2006 indicate that significant gain over infrared-only technique is obtained once albedo is used for cloud segmentation followed by bispectral cloud classification and rainfall estimation. At 3-h, 0.04° resolution, the observed improvement using bispectral information was about 66% for equitable threat score and 26% for the correlation coefficient. At coarser 0.24° resolution, the gains were 34% and 32% for the two performance measures, respectively. © 2010 American Meteorological Society."
"8076920100;7005719900;57215223917;6602621536;7003484308;7003532404;","Cloud cover classification through simultaneous ground-based measurements of solar and infrared radiation",2002,"10.1016/S0169-8095(02)00003-0","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0036092460&doi=10.1016%2fS0169-8095%2802%2900003-0&partnerID=40&md5=239abeee54914af623ea28b6bec5a1f3","Simultaneous measurements of downwelling short-wave solar irradiance and incoming total radiation flux were performed at the Reeves Nevè glacier station (1200 m MSL) in Antarctica on 41 days from late November 1994 to early January 1995, employing the upward sensors of an albedometer and a pyrradiometer. The downwelling short-wave radiation measurements were analysed following the Duchon and O'Malley [J. Appl. Meteorol. 38 (1999) 132] procedure for classifying clouds, using the 50-min running mean values of standard deviation and the ratio of scaled observed to scaled clear-sky irradiance. Comparing these measurements with the Duchon and O'Malley rectangular boundaries and the local human observations of clouds collected on 17 days of the campaign, we found that the Duchon and O'Malley classification method obtained a success rate of 93% for cirrus and only 25% for cumulus. New decision criteria were established for some polar cloud classes providing success rates of 94% for cirrus, 67% for cirrostratus and altostratus, and 33% for cumulus and altocumulus. The ratios of the downwelling short-wave irradiance measured for cloudy-sky conditions to that calculated for clear-sky conditions were analysed in terms of the Kasten and Czeplak [Sol. Energy 24 (1980) 177] formula together with simultaneous human observations of cloudiness, to determine the empirical relationship curves providing reliable estimates of cloudiness for each of the three above-mentioned cloud classes. Using these cloudiness estimates, the downwelling long-wave radiation measurements (obtained as differences between the downward fluxes of total and short-wave radiation) were examined to evaluate the downwelling long-wave radiation flux normalised to totally overcast sky conditions. Calculations of the long-wave radiation flux were performed with the MODTRAN 3.7 code [Kneizys, F.X., Abreu, L.W., Anderson, G.P., Chetwynd, J.H., Shettle, E.P., Berk, A., Bernstein, L.S., Robertson, D.C., Acharya, P., Rothman, L.S., Selby, J.E.A., Gallery, W.O., Clough, S.A., 1996. In: Abreu, L.W., Anderson, G.P. (Eds.), The MODTRAN 2/3 Report and LOWTRAN 7 MODEL. Contract F19628-91-C.0132, Phillips Laboratory, Geophysics Directorate, PL/GPOS, Hanscom AFB, MA, 261 pp.] for both clear-sky and cloudy-sky conditions, considering various cloud types characterised by different cloud base altitudes and vertical thicknesses. From these evaluations, best-fi: curves of the downwelling long-wave radiation flux were defined as a function of the cloud base height for the three polar cloud classes. Using these relationship curves, average estimates of the cloud base height were obtained from the three corresponding sub-sets of long-wave radiation measurements. The relative frequency histograms of the cloud base height defined by examining these three sub-sets were found to present median values of 4.7, 1.7 and 3.6 km for cirrus, cirrostratus/altostratus and cumulus/altocumulus, respectively, while median values of 6.5, 1.8 and 2.9 km were correspondingly determined by analysing only the measurements taken together with simultaneous cloud observations. © 2002 Elsevier Science B.V. All rights reserved."
"7401876682;57210742979;7202554609;57212761650;","Microphysical observations of warm cumulus clouds in Ceara, Brazil",2000,"10.1016/S0169-8095(00)00045-4","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0033942603&doi=10.1016%2fS0169-8095%2800%2900045-4&partnerID=40&md5=2938fd072366912604afb5d255be1571","Microphysical properties of shallow, warm cumulus clouds, such as droplet concentration, shape of the spectra, etc., may vary due to several factors, from the large-scale environment to microphysical processes on very small scales. Microphysical characteristics of clouds present a significant variability due to different CCN sources. For instance, it is well known that there are crucial differences between maritime and continental clouds regarding their microstructure. In this paper, we analyze microphysical data obtained inside shallow cumuli with an instrumented aircraft in Ceara State, Northeast Brazil, during a field campaign carried out during the first half of 1994. A brief description of the field campaign is presented and a cloud classification is established. Significant differences regarding droplet concentration and spectrum shape were observed among four different cloud regimes: maritime, coastal, continental and 'urban' clouds. Different functions were examined (exponential, gamma, lognormal and Weibull) in order to determine how appropriate are bulk parameterizations of droplet spectra in the representation of the microphysical properties of shallow cumulus clouds. The exponential distribution was shown to be unsuitable for most of the observed spectra. The gamma and lognormal distributions were better with the Weibull distribution providing the best fit. However, a significant variability of the width (or shape) parameter was verified for the three distributions, regarding different cloud regimes (maritime, coastal, continental and urban), from cloud to cloud, and in association with different regions of a cloud. Such variability imposes important limitations to bulk-microphysical modeling using distributions with prescribed width/shape parameters. A brief discussion is presented on how physical processes in a cloud can alter the shape of the droplet spectra, focusing on idealized distributions. (C) 2000 Elsevier Science B.V. All rights reserved."
"6602692238;6701607011;","Significant changes between the ISCCP C and D cloud climatologies",1998,"10.1029/1998GL900081","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0032534158&doi=10.1029%2f1998GL900081&partnerID=40&md5=f9db59b8e5ce18250ec746d8620b6d6f","We analyse one year of cloud data from the ISCCP C and D datasets. The two datasets differ by their retrieval algorithms and their definitions of the cloud types defined from the cloud top pressure and cloud optical depth. The differences between the two datasets are first described in terms of the total cloud cover, as well as its repartition in low, middle, and high level cloudiness. We also project the ISCCP C cloud classes into the ISCCP D cloud types to circumvent the problem of different cloud type definitions in the two datasets. The differences between the two datasets are then also investigated in terms of the most frequent cloud type."
"7006186929;7003758326;","Remote sensing of integrated cloud liquid water: Development of algorithms and quality control",1998,"10.1029/97RS02745","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0032027612&doi=10.1029%2f97RS02745&partnerID=40&md5=beac9231a57cee59f1817001cc05efa6","Algorithms are developed to infer integrated cloud liquid water path (LWP) over the oceans from spaceborne and ground-based passive microwave measurements. These algorithms are built from simulated observations, which are calculated with a radiative transfer model applied to a set of about 10,000 atmospheric profiles obtained from the European Centre for Medium-Range Weather Forecasts forecast model. In this model the liquid water content is computed from a prognostic cloud scheme. A multilinear regression is applied to functions of simulated brightness temperatures (log linear form) and LWP to derive the algorithm coefficients. The retrieval accuracy based on the regression analysis including instrumental noise is 0.0257 and 0.0345 kg m-2 for the DMSP special sensor microwave imager (SSM/I) and the ERS1 along-track scanning radiometer/microwave (ATSR/M), respectively, and 0.0308 kg m-2 for the ground-based radiometer. It is shown that the log linear form is adequate to transform the nonlinear problem into a quasi-linear problem for LWP below 0.8 kg m-2. The coherence of the global approach is verified through the validation of total precipitable water (TPW) algorithms developed in a way similar to LWP algorithms. The LWP retrievals from the algorithm for the ground-based radiometer are in good agreement with retrievals from airborne measurements performed in the vicinity of the radiometer. A coherence test is performed for ATSR/M, benefiting from the coincident infrared images obtained from an infrared radiometer (ATSR/IR) aboard the same platform to select clear-air areas. Regardless of the slight mean bias of the inferred LWP due to inaccurate calibration, there is no anomalous dependency upon latitude, i.e., upon high water vapor contents in the tropics and strong winds in the high latitudes. The results of the algorithm for SSM/I are compared with a Meteosat cloud classification. When the classification detects the ocean surface, the algorithm systematically retrieves contents close to zero. The retrievals for other classes (i.e., low stratiform clouds, medium clouds) are consistent with the Meteosat data; retrievals in the presence of low stratiform clouds appear more realistic than values provided by some already published algorithms. It is also shown that up to 0.8 kg m-2 the log linear regression approach has a quality of the same order as a variational method, which requires much more computation time."
"24069904000;55358263500;7003396214;6603397172;","A first assessment of the Sentinel-2 Level 1-C cloud mask product to support informed surface analyses",2018,"10.1016/j.rse.2018.08.009","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85052905424&doi=10.1016%2fj.rse.2018.08.009&partnerID=40&md5=982443c51d35dad91766c7f5628649a1","Cloud detection in optical remote sensing images is a crucial problem because undetected clouds can produce misleading results in the analyses of surface and atmospheric parameters. Sentinel-2 provides high spatial resolution satellite data distributed with associated cloud masks. In this paper, we evaluate the ability of Sentinel-2 Level-1C cloud mask products to discriminate clouds over a variety of biogeographic scenarios and in different cloudiness conditions. Reference cloud masks for the identification of misdetection were generated by applying a local thresholding method that analyses Sentinel-2 Band 2 (0.490 μm) and Band 10 (1.375 μm) separately; histogram-based thresholds were locally tuned by checking the single bands and the natural color composite (B4B3B2); in doubtful cases, NDVI and DEM were also analyzed to refine the masks; the B2B11B12 composite was used to separate snow. The analysis of the cloud classification errors obtained for our test sites allowed us to get important inferences of general value. The L1C cloud mask generally underestimated the presence of clouds (average Omission Error, OE, 37.4%); this error increased (OE &gt; 50%) for imagery containing opaque clouds with a large transitional zone (between the cloud core and clear areas) and cirrus clouds, fragmentation emerged as a major source of omission errors (R2 0.73). Overestimation was prevalently found in the presence of holes inside the main cloud bodies. Two extreme environments were particularly critical for the L1C cloud mask product. Detection over Amazonian rainforests was highly inefficient (OE &gt; 70%) due to the presence of complex cloudiness and high water vapor content. On the other hand, Alpine orography under dry atmosphere created false cirrus clouds. Altogether, cirrus detection was the most inefficient. According to our results, Sentinel-2 L1C users should take some simple precautions while waiting for ESA improved cloud detection products. © 2018 The Authors"
"57189490787;36064917000;55500134600;55020400300;55986579100;55292994700;57069455200;57216168491;57189002132;57189502828;7005906188;","A Three-Step Approach for TLS Point Cloud Classification",2016,"10.1109/TGRS.2016.2564501","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84971441900&doi=10.1109%2fTGRS.2016.2564501&partnerID=40&md5=f998a761ed271fb4c5f47f7650a5b461","The ability to classify urban objects in large urban scenes from point clouds efficiently and accurately still remains a challenging task today. A new methodology for the effective and accurate classification of terrestrial laser scanning (TLS) point clouds is presented in this paper. First, in order to efficiently obtain the complementary characteristics of each 3-D point, a set of point-based descriptors for recognizing urban point clouds is constructed. This includes the 3-D geometry captured using the spin-image descriptor computed on three different scales, the mean RGB colors of the point in the camera images, the LAB values of that mean RGB, and the normal at each 3-D point. The initial 3-D labeling of the categories in urban environments is generated by utilizing a linear support vector machine classifier on the descriptors. These initial classification results are then first globally optimized by the multilabel graph-cut approach. These results are further refined automatically by a local optimization approach based upon the object-oriented decision tree that uses weak priors among urban categories which significantly improves the final classification accuracy. The proposed method has been validated on three urban TLS point clouds, and the experimental results demonstrate that it outperforms the state-of-the-art method in classification accuracy for buildings, trees, pedestrians, and cars. © 2016 IEEE."
"7405431519;7401526171;7005052907;55644003021;35975568000;56148670500;","Bias adjustment of satellite-based precipitation estimation using gauge observations: A case study in Chile",2016,"10.1002/2015JD024540","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84963955071&doi=10.1002%2f2015JD024540&partnerID=40&md5=e82f98978362f606a920859068cbad11","Satellite-based precipitation estimates (SPEs) are promising alternative precipitation data for climatic and hydrological applications, especially for regions where ground-based observations are limited. However, existing satellite-based rainfall estimations are subject to systematic biases. This study aims to adjust the biases in the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Cloud Classification System (PERSIANN-CCS) rainfall data over Chile, using gauge observations as reference. A novel bias adjustment framework, termed QM-GW, is proposed based on the nonparametric quantile mapping approach and a Gaussian weighting interpolation scheme. The PERSIANN-CCS precipitation estimates (daily, 0.04°×0.04°) over Chile are adjusted for the period of 2009–2014. The historical data (satellite and gauge) for 2009–2013 are used to calibrate the methodology; nonparametric cumulative distribution functions of satellite and gauge observations are estimated at every 1°×1° box region. One year (2014) of gauge data was used for validation. The results show that the biases of the PERSIANN-CCS precipitation data are effectively reduced. The spatial patterns of adjusted satellite rainfall show high consistency to the gauge observations, with reduced root-mean-square errors and mean biases. The systematic biases of the PERSIANN-CCS precipitation time series, at both monthly and daily scales, are removed. The extended validation also verifies that the proposed approach can be applied to adjust SPEs into the future, without further need for ground-based measurements. This study serves as a valuable reference for the bias adjustment of existing SPEs using gauge observations worldwide. © 2016. American Geophysical Union. All Rights Reserved."
"7004327151;7004547261;57203025969;","Cloud-base height estimates using a combination of meteorological satellite imagery and surface reports",2000,"10.1175/1520-0450(2000)039<2336:cbheua>2.0.co;2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0034464587&doi=10.1175%2f1520-0450%282000%29039%3c2336%3acbheua%3e2.0.co%3b2&partnerID=40&md5=46575aa0fc6adebda41fe6ef55bcf4c8","This paper describes how the combination of a satellite-derived cloud classification with surface observations can improve analysis of cloud-base height. A cloud-base retrieval that combines a cloud classification derived from visible and infrared satellite data with surface reports of cloud base is investigated. A method using the satellite classification to interpret the surface data is compared with a more traditional distance-weighted approach of interpolating the surface data. Cloud-height observations from the U.S. surface synoptic network were merged with a cloud classification of GOES-8 imager data for 235 test images from June 1996. Surface cloud-base height reports were withheld on a revolving basis and used as truth for the cloud-base height predictions from the satellite-based method. The comparison was limited to cloud-base heights of less than 10 000 feet because of biases in cloud-base height reporting at higher altitudes. Results indicate that fusion of the satellite cloud classification with surface cloud-base height reports yields a superior estimate of cloud-base height versus an estimate using only interpolated surface data. This is true even though the surface-only method was given the advantage of always being spatially closer to the control site. Performance improvement is more significant for broken and overcast conditions. In addition, the use of a simple textural measure, derived from the satellite cloud classification, causes the satellite-assisted method to outperform the surface-only method by an even wider margin."
"56192613000;36990982800;56192015600;56422220400;15034793900;","Facet Segmentation-Based Line Segment Extraction for Large-Scale Point Clouds",2017,"10.1109/TGRS.2016.2639025","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020712144&doi=10.1109%2fTGRS.2016.2639025&partnerID=40&md5=eab8c2f3465d0e19a7c38f8b0d66ef16","As one of the most common features in the man-made environments, straight lines play an important role in many applications. In this paper, we present a new framework to extract line segments from large-scale point clouds. The proposed method is fast to produce results, easy for implementation and understanding, and suitable for various point cloud data. The key idea is to segment the input point cloud into a collection of facets efficiently. These facets provide sufficient information for determining linear features in the local planar region and make line segment extraction become relatively convenient. Moreover, we introduce the concept 'number of false alarms' into 3-D point cloud context to filter the false positive line segment detections. We test our approach on various types of point clouds acquired from different ways. We also compared the proposed method with several other methods and provide both quantitative and visual comparison results. The experimental results show that our algorithm is efficient and effective, and produce more accurate and complete line segments than the comparative methods. To further verify the accuracy of the line segments extracted by the proposed method, we also present a line-based registration framework, which employs these line segments on point clouds registration. © 2017 IEEE."
"56188627800;55948466000;14625770800;56068624000;36610940400;","Salient local binary pattern for ground-based cloud classification",2013,"10.1007/s13351-013-0206-8","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84882749579&doi=10.1007%2fs13351-013-0206-8&partnerID=40&md5=ccc352df2ea6f2c4e2717d39c8e0a868","Ground-based cloud classification is challenging due to extreme variations in the appearance of clouds under different atmospheric conditions. Texture classification techniques have recently been introduced to deal with this issue. A novel texture descriptor, the salient local binary pattern (SLBP), is proposed for ground-based cloud classification. The SLBP takes advantage of the most frequently occurring patterns (the salient patterns) to capture descriptive information. This feature makes the SLBP robust to noise. Experimental results using ground-based cloud images demonstrate that the proposed method can achieve better results than current state-of-the-art methods. © 2013 The Chinese Meteorological Society and Springer-Verlag Berlin Heidelberg."
"7003668116;7005135473;7402094372;","Cloud liquid water path comparisons from passive microwave and solar reflectance satellite measurements: Assessment of sub-field-of-view cloud effects in microwave retrievals",1997,"10.1029/97jd01257","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0000007190&doi=10.1029%2f97jd01257&partnerID=40&md5=c8772b3777ef5447ea74d51682373e8e","Satellite observations of the cloud liquid water path (LWP) are compared from special sensor microwave imager (SSM/I) measurements and GOES 8 imager solar reflectance (SR) measurements to ascertain the impact of sub-field-of-view (FOV) cloud effects on SSM/I 37 GHz retrievals. The SR retrievals also incorporate estimates of the cloud droplet effective radius derived from the GOES 8 3.9-μm channel. The comparisons consist of simultaneous collocated and full-resolution measurements and are limited to nonprecipitating marine stratocumulus in the eastern Pacific for two days in October 1995. The retrievals from these independent methods are consistent for overcast SSM/I FOVs. with RMS differences as low as 0.030 kg m-2, although biases exist for clouds with more open spatial structure, where the RMS differences increase to 0.039 kg m-2. For broken cloudiness within the SSM/I FOV the average beam-filling error (BFE) in the microwave retrievals is found to be about 22% (average cloud amount of 73%). This systematic error is comparable with the average random errors in the microwave retrievals. However, even larger BFEs can be expected for individual FOVs and for regions with less cloudiness. By scaling the microwave retrievals by the cloud amount within the FOV, the systematic BFE can be significantly reduced but with increased RMS differences of 0.046-0.058 kg m-2 when compared to the SR retrievals. The beam-filling effects reported here are significant and are expected to impact directly upon studies that use instantaneous SSM/I measurements of cloud LWP, such as cloud classification studies and validation studies involving surface-based or in situ data."
"7005528388;6603126554;7102171439;15726427000;24367209100;21742642500;35265576100;","Cloud-state-dependent sampling in airs observations based on cloudsat cloud classification",2013,"10.1175/JCLI-D-13-00065.1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84886239379&doi=10.1175%2fJCLI-D-13-00065.1&partnerID=40&md5=d6c5356644db9eff6f6eb89acf0813e6","The precision, accuracy, and potential sampling biases of temperature T and water vapor q vertical profiles obtained by satellite infrared sounding instruments are highly cloud-state dependent and poorly quantified. The authors describe progress toward a comprehensive T and q climatology derived from the Atmospheric Infrared Sounder (AIRS) suite that is a function of cloud state based on collocated CloudSat observations. The AIRS sampling rates, biases, and center root-mean-square differences (CRMSD) are determined through comparisons of pixel-scale collocated ECMWF model analysis data. The results show that AIRS provides a realistic representation of most meteorological regimes in most geographical regions, including those dominated by high thin cirrus and shallow boundary layer clouds. The mean AIRS observational biases relative to the ECMWF analysis between the surface and 200 hPa are within±1K in T and from -1 to +0.5 g kg-1 in q. Biases because of cloud-state-dependent sampling dominate the total biases in the AIRS data and are largest in the presence of deep convective (DC) and nimbostratus (Ns) clouds. Systematic cold and dry biases are found throughout the free troposphere for DC and Ns. Somewhat larger biases are found over land and in the midlatitudes than over the oceans and in the tropics, respectively. Tropical and oceanic regions generally have a smaller CRMSD than the midlatitudes and over land, suggesting agreement of T and q variability between AIRS andECMWFin these regions. The magnitude ofCRMSDis also strongly dependent on cloud type. © 2013 American Meteorological Society."
"6602611139;","Cloudiness changes in Cracow in the 20th century",2003,"10.1002/joc.887","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0038603845&doi=10.1002%2fjoc.887&partnerID=40&md5=5dcba1be3ff45dde21621068dd9389c8","This paper presents the long-term, annual and diurnal cloud amount values for Cracow (Poland) on the basis of 95-year records. Cloud data were taken from climatological observations made at the Department of Climatology, Jagiellonian University, in the years 1906-2000. On the basis of traditional statistical methods, the following values were calculated: average cloudiness (cloud amount), number of cloudless sky occurences for three climatological terms, daily and yearly averages, and sums of cloudless days. The cloudiness was also analysed from the point of view of its quality. For that purpose, ten specific genera of clouds adopted in the international classification were examined, taking into account the frequency of their occurence within every month, separately for each of the climatological periods. Copyright © 2003 Royal Meteorological Society."
"55247565600;55948466000;55969140000;14625770800;","Deep Convolutional Activations-Based Features for Ground-Based Cloud Classification",2017,"10.1109/LGRS.2017.2681658","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85017153626&doi=10.1109%2fLGRS.2017.2681658&partnerID=40&md5=1622a14896cdcaf450ef10cc2a73a358","Ground-based cloud classification is crucial for meteorological research and has received great concern in recent years. However, it is very challenging due to the extreme appearance variations under different atmospheric conditions. Although the convolutional neural networks have achieved remarkable performance in image classification, no one has evaluated their suitability for cloud classification. In this letter, we propose to use the deep convolutional activations-based features (DCAFs) for ground-based cloud classification. Considering the unique characteristic of cloud, we believe the local rich texture information might be more important than the global layout information and, thus, give a comprehensive evaluation of using both shallow convolutional layers-based features and DCAFs. Experimental results on two challenging public data sets demonstrate that although the realization of DCAF is quite straightforward without any use-dependent tricks, it outperforms conventional hand-crafted features considerably. © 2017 IEEE."
"24070152900;6603384387;6603287639;7004276118;","Pure rotational-Raman channels of the Esrange lidar for temperature and particle extinction measurements in the troposphere and lower stratosphere",2013,"10.5194/amt-6-91-2013","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84880658209&doi=10.5194%2famt-6-91-2013&partnerID=40&md5=b2ea6a218d8572d15df02ebb448b9498","The Department of Meteorology at Stockholm University operates the Esrange Rayleigh/Raman lidar at Es-range (68° N, 21° E) near the Swedish city of Kiruna. This paper describes the design and first measurements of the new pure rotational-Raman channel of the Esrange lidar. The Es-range lidar uses a pulsed Nd:YAG solid-state laser operating at 532 nm as light source with a repetition rate of 20 Hz and a pulse energy of 350 mJ. The minimum vertical resolution is 150 m and the integration time for one profile is 5000 shots. The newly implemented channel allows for measurements of atmospheric temperature at altitudes below 35 km and is currently optimized for temperature measurements between 180 and 200 K. This corresponds to conditions in the lower Arctic stratosphere during winter. In addition to the temperature measurements, the aerosol extinction coefficient and the aerosol backscatter coefficient at 532 nm can be measured independently. Our filter-based design minimizes the systematic error in the obtained temperature profile to less than 0.51 K. By combining rotational-Raman measurements (5-35 km height) and the integration technique (30-80 km height), the Esrange lidar is now capable of measuring atmospheric temperature profiles from the upper troposphere up to the mesosphere. With the improved setup, the system can be used to validate current lidar-based polar stratospheric cloud classification schemes. The new capability of the instrument measuring temperature and aerosol extinction furthermore enables studies of the thermal structure and variability of the upper troposphere/lower stratosphere. Although several lidars are operated at polar latitudes, there are few instruments that are capable of measuring temperature profiles in the troposphere, stratosphere, and mesosphere, as well as aerosols extinction in the troposphere and lower stratosphere with daylight capability. © Author(s) 2013."
"6507866559;7006499081;","Use of satellite images for fog detection (AVHRR) and forecast of fog dissipation (METEOSAT) over lowland thessalia, hellas",1999,"10.1080/014311699212876","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0033560478&doi=10.1080%2f014311699212876&partnerID=40&md5=e781be8802eeb433897b85c9b2c37f48","In this work, both AVHRR and METEOSAT data are used in order to detect fog areas and to predict fog dissipation. Since fog is created during the night, AVHRR data is used for fog detection at night and METEOSAT data is used for fog observation and fog dissipation forecast during the day over a specific area of Greece, lowland Thessalia. For fog detection at night only the three infrared AVHRR channels are used. The difference between channel 4 and channel 3 is used to distinguish fog or low stratus from land and sea. A cloud classification method applied in this work is based on multispectral box classification. Classification classes are fog and low clouds, sea, land, middle and high clouds. There is also a class for unclassified pixels. For fog dissipation detection, the visible channel of METEOSAT is used, normalized according to the vertical Sun viewing angle. Fog persistence after the first morning calculations is examined versus fog duration. Also, the percentage of the same area affected by fog in the following hours is examined versus fog duration. A case study is presented and the results of the method are discussed. © 1999 Taylor & Francis Ltd."
"7003750861;7202162685;55386235300;","Vertical stratification of tropical cloud properties as determined from satellite",1997,"10.1029/96jd02867","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0031445857&doi=10.1029%2f96jd02867&partnerID=40&md5=c0fcf0059acecd6323c4f91fafb435b0","A new retrieval scheme is developed to infer tropical cloud properties and vertical structure, including liquid and ice water content, cloud top and base, and cloud layering. The retrieval scheme utilizes a cloud classification scheme that uses both International Satellite Cloud Climatology Project (ISCCP) cloud top temperature and a microwave index from the special sensor microwave/imager (SSM/I). Different cloud classes have different allowed numbers of cloud layers. The retrieval scheme also incorporates findings from observational studies. When multiple liquid layers are judged to be possible, a ""cloudiness likelihood"" parameter is used to determine the priority for the presence of liquid layer at each level, based on the European Centre for Medium-Range Weather Forecasts analyzed relative humidity field. Cloud liquid water path is determined using a microwave satellite retrieval. In case of multiple liquid layers, the liquid water path is partitioned and assigned to each liquid layer in proportion to the adiabatic liquid water path in each layer. Depending on the cloud class, ice water paths are determined using one of the following methods: (1) ISCCP ice optical depth; (2) a microwave ice retrieval that uses ice scattering signals at high SSM/I frequencies; and (3) a residual method that infers ice from the difference between a ""virtual"" liquid water path derived from ISCCP total optical depth and the true liquid water path determined from SSM/I. The retrieved cloud layering is indirectly validated using cloud cooccurrence climatology from surface observations. The cloud base retrieval is compared with lidar measurements obtained during the Tropical Ocean-Global Atmosphere Coupled Ocean-Atmosphere Response Experiment."
"7404325680;56228174700;","An improved fuzzy logic segmentation of sea ice, clouds, and ocean in remotely sensed arctic imagery",1995,"10.1016/0034-4257(95)00175-1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0029511595&doi=10.1016%2f0034-4257%2895%2900175-1&partnerID=40&md5=1f132942d8894344d57099dbfd5831f0","The accurate segmentation of sea ice from cloud and from cloud-free ocean in polar AVHRR imagery is important for many scientific applications (e.g., sea ice-albedo feedback mechanisms, heat exchange between ocean and atmosphere in polar regions; studies of the stability of surface water in polar regions). Unfortunately, it is a difficult task complicated by the common visible reflectance characteristics of sea ice and cloud. Moreover, AVHRR Channel 3 data historically have been contaminated by highly variable sensor noise which generally has hampered their use in the classification of polar scenes. Likewise, polar scenes often contain pixels with mixed classes (e.g., sea ice and cloud). This article uses a combination of fuzzy logic classification methods, noise reduction in AVHRR Channel 3 data using Wiener filtering methods (Simpson and Yhann, 1994), and a physically motivated rule base which makes effective use of the Wiener filtered Channel 3 data to more accurately segment polar imagery. The new method's improved classification skill compared to more traditional methods, as well as its regional independence, is demonstrated. The algorithm is computationally efficient and hence is suitable for analyzing the large volumes of polar imagery needed in many global change studies. © 1995."
"6603377859;57026296100;","Cloud classification using METEOSAT VIS-IR imagery",1992,"10.1080/01431169208904162","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0026460853&doi=10.1080%2f01431169208904162&partnerID=40&md5=3639c75d6b3565633f01dc4de697ca28","A new method is proposed which allows for reasonably accurate cloud classification based upon Gaussian approximation of cloud emission and using a pair of visible-infrared, high resolution METEOSAT images. Its most important characteristic is the very low amount of CPU time required for a single classifica-tion. It becomes then suitable for application in very short range weather forecasting (nowcasting) by ensuring the adequate time coverage and necessary rapidity of use for the operational environment. Two classifications of summer and winter meteorological situations are presented. © 1992 Taylor & Francis Group, LLC."
"14063370300;57192604786;","A probabilistic graphical model for the classification of mobile LiDAR point clouds",2018,"10.1016/j.isprsjprs.2018.04.018","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85047263524&doi=10.1016%2fj.isprsjprs.2018.04.018&partnerID=40&md5=2e79d18fee1cfcf09a161f81c1d9e10e","Mobile Light Detection And Ranging (LiDAR) point clouds have the characteristics of complex and incomplete scenes, uneven point density and noises, which raises great challenges for automatically interpreting 3D scene. Aiming at the problem of 3D point cloud classification, we propose a probabilistic graphical model for automatic classification of mobile LiDAR point clouds in this paper. First, the super-voxels are generated as primitives based on the similar geometric and radiometric properties. Second, we construct point-based multi-scale visual features that are used to describe the texture information at various scales. Third, the topic model is used to analyze the semantic correlations among points within super-voxels to establish the semantic representation, which is finally fed into the proposed probabilistic graphical model. The proposed model combines Bayesian network and Markov random fields to obtain locally continuous and globally optimal classification results. To evaluate the effectiveness and the robustness of the proposed method, experiments were conducted using mobile LiDAR point clouds for three types of street scenes. Experimental results demonstrate that our proposed model is efficient and robust for extracting vehicles, buildings, street trees and pole-like objects, with overall accuracies of 98.17%, 97.41% and 96.81% respectively. Moreover, compared with other existing methods, our proposed model can provide higher classification correctness, specifically for small objects such as cars and pole-like objects. © 2018 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS)"
"56321122100;7006698304;6701754792;23017945100;","Characterizing observed midtopped cloud regimes associated with Southern Ocean shortwave radiation biases",2014,"10.1175/JCLI-D-14-00139.1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84905898101&doi=10.1175%2fJCLI-D-14-00139.1&partnerID=40&md5=68ab7b61ea87b193fb468f45b86e0d72","Clouds strongly affect the absorption and reflection of shortwave and longwave radiation in the atmosphere. A key bias in climate models is related to excess absorbed shortwave radiation in the high-latitude Southern Ocean. Model evaluation studies attribute these biases in part to midtopped clouds, and observations confirm significant midtopped clouds in the zone of interest. However, it is not yet clear what cloud properties can be attributed to the deficit in modeled clouds. Present approaches using observed cloud regimes do not sufficiently differentiate between potentially distinct types of midtopped clouds and their meteorological contexts. This study presents a refined set of midtopped cloud subregimes for the high-latitude Southern Ocean, which are distinct in their dynamical and thermodynamic background states. Active satellite observations from CloudSat and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) are used to study the macrophysical structure and microphysical properties of the new cloud regimes. The subgrid-scale variability of cloud structure and microphysics is quantified within the cloud regimes by identifying representative physical cloud profiles at high resolution from the radar-lidar (DARDAR) cloud classification mask. The midtopped cloud subregimes distinguish between stratiform clouds under a high inversion and moderate subsidence; an optically thin cold-air advection cloud regime occurring under weak subsidence and including altostratus over low clouds; optically thick clouds with frequent deep structures under weak ascent and warm midlevel anomalies; and a midlevel convective cloud regime associated with strong ascent and warm advection. The new midtopped cloud regimes for the high-latitude Southern Ocean will provide a refined tool for model evaluation and the attribution of shortwave radiation biases to distinct cloud processes and properties. © 2014 American Meteorological Society."
"57219547459;7401526171;7005052907;7004011998;8632797000;26026749200;","Short-term quantitative precipitation forecasting using an object-based approach",2013,"10.1016/j.jhydrol.2012.09.052","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84874317644&doi=10.1016%2fj.jhydrol.2012.09.052&partnerID=40&md5=682f4e84c6b80d1b32094720fd5371b2","Short-term Quantitative Precipitation Forecasting (SQPF) is critical for flash-flood warning, navigation safety, and many other applications. The current study proposes a new object-based method, named PERCAST (PERsiann-ForeCAST), to identify, track, and nowcast storms. PERCAST predicts the location and rate of rainfall up to 4. h using the most recent storm images to extract storm features, such as advection field and changes in storm intensity and size. PERCAST is coupled with a previously developed precipitation retrieval algorithm called PERSIANN-CCS (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System) to forecast rainfall rates. Four case studies have been presented to evaluate the performance of the models. While the first two case studies justify the model capabilities in nowcasting single storms, the third and fourth case studies evaluate the proposed model over the contiguous US during the summer of 2010. The results show that, by considering storm Growth and Decay (GD) trends for the prediction, the PERCAST-GD further improves the predictability of convection in terms of verification parameters such as Probability of Detection (POD) and False Alarm Ratio (FAR) up to 15-20%, compared to the comparison algorithms such as PERCAST. © 2012 Elsevier B.V."
"36173974000;7404570418;","A hybrid cloud detection algorithm to improve MODIS sea surface temperature data quality and coverage over the eastern Gulf of Mexico",2013,"10.1109/TGRS.2012.2223217","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84876331043&doi=10.1109%2fTGRS.2012.2223217&partnerID=40&md5=e579a910059c60c3f849b1757bdea52b","Cloud contamination can lead to significant biases in sea surface temperature (SST) as estimated from satellite measurements. The effectiveness of four cloud detection algorithms for the Moderate Resolution Imaging Spectroradiometer (MODIS) in retaining valid SST data and masking cloud-contaminated data was assessed for all 2125 daytime and nighttime images during 2010 over the eastern Gulf of Mexico and including the east coast of Florida. None of the cloud detection algorithms was found to be sufficient to reliably differentiate clouds from valid SST, particularly during anomalously cold events. The strengths and weaknesses of each algorithm were identified, and a new hybrid cloud detection algorithm was developed to maximize valid data retention while excluding cloud-contaminated pixels. The hybrid algorithm was based on a decision tree, which includes a set of rules to use existing algorithms in different ways according to time and location. Comparing with >10\,000 concurrent in situ SST measurements from buoys, images processed with the hybrid algorithm showed increases in data capture and improved accuracy statistics over most existing algorithms. In particular, while keeping the same accuracy, the hybrid algorithm resulted in nearly 20% more SST retrievals than the most accurate algorithm (Quality SST) currently being used for operational processing. The increases in both data coverage and SST range should improve MODIS data products for more reliable SST retrievals in near real time, thus enhancing the ocean observing capacity to detect anomaly events and study short-and long-term SST changes in coastal environments. © 1980-2012 IEEE."
"7401760335;7202997063;35581784000;7103083205;7006410639;6701878628;","Textural and spectral features as an aid to cloud classification",1991,"10.1080/01431169108929704","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0026359731&doi=10.1080%2f01431169108929704&partnerID=40&md5=a15e6e6348be6b53406663a5b53da237","The problem of classifying clouds seen on meteorological satellite images into different types is one which requires the use of textural as well as spectral information. Since multi-spectral features are of prime importance, textural features must be considered as augmenting, rather than replacing, spectral measures. Several textural features are studied to determine their discriminating power across a number of cloud classes including those which have previously been found difficult to separate. Although several features in the frequency domain are tested they are found to be less useful than those in the spatial domain with only one exception. The specific features recommended for use in classification depend on the type of classification to be undertaken. Specifically, different features should be used for a multi-dimensional feature space analysis than for a binary-tree rule-based classification. © 1991 Taylor & Francis Ltd."
"7201914101;6506702741;7402546593;","The effect of spatial resolution upon texture-based cloud field classifications",1989,"10.1029/jd094id12p14767","https://www.scopus.com/inward/record.uri?eid=2-s2.0-17044461216&doi=10.1029%2fjd094id12p14767&partnerID=40&md5=5b722a009d99c6f0509a6dd1887db8cd","The present study examines the loss of cloud classification accuracy as a function of spatial resolution by degrading the imagery through progressive averaging. Significant improvement in cloud classification accuracy can be obtained using 1/2-km spatial resolution data rather than the current 1-km spatial resolution data available today from AVHRR and GOES. Cirrus classification accuracy is especially compromised as the spatial resolution is degraded. However, the use of texture measures defined at the combination of pixel separations d = 1, 4 improves classification accuracies by several percent even for 1-km spatial resolution data. -from Authors"
"55576725800;57189715211;55709751800;57202043229;55937215200;53979940800;7202089880;","A novel approach for the detection of standing tree stems from plot-level terrestrial laser scanning data",2019,"10.3390/rs11020211","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85060687621&doi=10.3390%2frs11020211&partnerID=40&md5=4b306254327ff9a8b2b3f45851127752","Tree stem detection is a key step toward retrieving detailed stem attributes from terrestrial laser scanning (TLS) data. Various point-based methods have been proposed for the stem point extraction at both individual tree and plot levels. The main limitation of the point-based methods is their high computing demand when dealing with plot-level TLS data. Although segment-based methods can reduce the computational burden and uncertainties of point cloud classification, its application is largely limited to urban scenes due to the complexity of the algorithm, as well as the conditions of natural forests. Here we propose a novel and simple segment-based method for efficient stem detection at the plot level, which is based on the curvature feature of the points and connected component segmentation. We tested our method using a public TLS dataset with six forest plots that were collected for the international TLS benchmarking project in Evo, Finland. Results showed that the mean accuracies of the stem point extraction were comparable to the state-of-art methods (>95%). The accuracies of the stem mappings were also comparable to the methods tested in the international TLS benchmarking project. Additionally, our method was applicable to a wide range of stem forms. In short, the proposed method is accurate and simple; it is a sensible solution for the stem detection of standing trees using TLS data. © 2019 by the authors."
"55003524100;7003510377;","Estimation of satellite rainfall error variance using readily available geophysical features",2014,"10.1109/TGRS.2013.2238636","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84890426166&doi=10.1109%2fTGRS.2013.2238636&partnerID=40&md5=7b9627332aa4c51cf8df2622ecb09e0a","The present study addresses the estimation of error variance (mean square error, MSE) of three satellite rainfall products: i) Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) product of 3B42RT; ii) Climate Prediction Center (CPC) Morph (CMORPH); and iii) Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS). Nonlinear regression model is used to fit the response variable (satellite rainfall error variance) with explanatory variable (satellite rainfall rate) by grouping them as function of three key geophysical features: topography, climate, and season. The results of the study suggest that the error variance of a rainfall product is strongly correlated with rainfall rate and can be expressed as a power-law function. The geophysical feature based error classification analysis helps in achieving superior accuracy for prognostic error variance quantification in the absence of ground truth data. The multiple correlation coefficients between the estimated and observed error variance over an independent validation region (Upper Mississippi River basin) and time period (2007-2010) are found to be 0.75, 0.86, and 0.87 for 3B42RT, CMORPH, and PERSIANN-CCS products, respectively. In another validation region (Arkansas-Red River basin), the correlation coefficients are 0.59, 0.89, and 0.92 for the same products, respectively. Results of the assessment of error variance models reveal that the type of error component present in a satellite rainfall product directly impacts the accuracy of estimated error variance. The model estimates the error variance more accurately when the precipitation error components are mostly hit bias or false precipitation, while for a product with extensive missed precipitation, the accuracy of estimated error variance is significantly compromised. The study clearly demonstrates the feasibility of quantifying the error variance of satellite rainfall products in a spatially and temporally varying manner using readily available geophysical features and rainfall rate. The study is a path finder to a globally applicable and operationally feasible methodology for error variance estimation at high spatial and temporal scales for advancing satellite rainfall applications in ungauged basins. © 1980-2012 IEEE."
"7403282069;8977001000;55745955800;","Cloud-Resolving Simulation of Low-Cloud Feedback to an Increase in Sea Surface Temperature",2010,"10.1175/2009JAS3239.1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-77953347773&doi=10.1175%2f2009JAS3239.1&partnerID=40&md5=5e3a24729f0c7c4072326e89f5614f69","This study investigates the physical mechanisms of the low cloud feedback through cloud-resolving simulations of cloud-radiative equilibrium response to an increase in sea surface temperature (SST). Six pairs of perturbed and control simulations are performed to represent different regimes of low clouds in the subtropical region by specifying SST differences (ΔSST) in the range of 4 and 14 K between the warm tropical and cool subtropical regions. The SST is uniformly increased by 2 Kin the perturbed set of simulations.Equilibriumstates are characterized by cumulus and stratocumulus cloud regimes with variable thicknesses and vertical extents for the range of specied ΔSSTs, with the perturbed set of simulations having higher cloud bases and tops and larger geometric thicknesses. The cloud feedback effect is negative for this ΔSST range (-0.68 to -5.22 W m-2 K-1) while the clear-sky feedback effect is mostly negative (-1.45 to 0.35 W m-2 K-1). The clear-sky feedback effect contributes greatly to the climate sensitivity parameter for the cumulus cloud regime whereas the cloud feedback effect dominates for the stratocumulus regime. The increase of liquid water path (LWP) and cloud optical depth is related to the increase of cloud thickness and liquid water content with SST. The rates of change in surface latent heat flux are much higher than those of saturation water vapor pressure in the cumulus simulations. The increase in surface latent heat flux is the pri marymechanism for the large change of cloud physical properties with +2 K SST, which leads to the negative cloud feedback effects. The changes in cloud fraction also contribute to the negative cloud feedback effects in the cumulus regime. Comparison of these results with prior modeling studies is also discussed. © 2010 American Meteorological Society."
"6602182223;","Simulated Doppler radar observations of inhomogeneous clouds: Application to the EarthCARE space mission",2008,"10.1175/2007JTECHA956.1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-38949169561&doi=10.1175%2f2007JTECHA956.1&partnerID=40&md5=488a997d46f814322f38ab8aaaca4873","A new simulation technique for spaceborne Doppler radar observations that was developed specifically for inhomogeneous targets is presented. Cloud inhomogeneity affects Doppler observations in two ways. First, line-of-sight velocities within the instantaneous field of view are unequally weighted. As the large forward motion of a spaceborne radar contributes to these line-of-sight velocities this causes biases in observed Doppler speeds. Second, receiver voltages now have time-varying stochastical properties, increasing the inaccuracy of Doppler observations. The new technique predicts larger inaccuracies of observed Doppler speeds than the traditional random signal simulations based on the inverse Fourier transform. The accuracy of Doppler speed observations by a spaceborne 95-GHz radar [as part of the proposed European Space Agency (ESA)/Japan Aerospace Exploration Agency (JAXA)/National Institute for Information and Communications Technology (NICT) EarthCARE mission] is assessed through simulations for realistic cloud scenes based on observations made by ground-based cloud-profiling radars. Close to lateral cloud boundary biases as large as several meters per second occur. For half of the cloud scenes investigated, the distribution of the in-cloud bias has an rms of 0.5 m s-1, implying that a bias in excess of 0.5 m s-1 will not be uncommon. An algorithm to correct the bias in observed Doppler observations, based on the observed gradient of reflectivity along track, is suggested and shown to be effective; that is, the aforementioned rms bias reduces to 0.14 m s-1. © 2008 American Meteorological Society."
"55790781000;26643530600;7102063963;7004697990;7101677832;","Comparison between current and future environmental satellite imagers on cloud classification using MODIS",2007,"10.1016/j.rse.2006.11.023","https://www.scopus.com/inward/record.uri?eid=2-s2.0-34247571402&doi=10.1016%2fj.rse.2006.11.023&partnerID=40&md5=cb0185221c8e63e928077443bc9aae4e","Future Satellite Imagers are expected to improve current ones on environmental and meteorological applications. In this study, an automatic classification scheme using radiance measurements with a clustering method is applied in an attempt to compare the capability on cloud classification by different sensors: AVHRR/3, the current GOES-12 Imager, SEVIRI, VIIRS, and ABI. The MODIS cloud mask is used as the initial classification. The results are analyzed with the help of true color and RGB composite images as well as other information about surface and cloud types. Results indicate that the future sensors (ABI and VIIRS) provide much better overall cloud classification capabilities than their corresponding current sensors (the current GOES-12 Imager and AVHRR/3) from the two chosen demonstration cases. However, for a specific class, it is not always true that more spectral bands result in better classification. In order to optimally use the spectral information, it is necessary to determine which bands are more sensitive for a specific class. Spatial resolution and the signal-to-noise ratio (SNR) of satellite sensors can significantly affect the classification. The 2.13 μm band could be useful for thin low cloud detection and the 3.7 μm band is useful for fresh snow detection. © 2006 Elsevier Inc. All rights reserved."
"15031035100;7202162685;35264351500;7102389805;","Transition between suppressed and active phases of intraseasonal oscillations in the Indo-Pacific warm pool",2006,"10.1175/JCLI3924.1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-33751429242&doi=10.1175%2fJCLI3924.1&partnerID=40&md5=b03488cf7c5a18753c9f854d2fd52f2d","Intraseasonal oscillations (ISOs) are important large-amplitude and large-scale elements of the tropical Indo-Pacific climate with time scales in the 20-60-day period range, during which time they modulate higher-frequency tropical weather. Despite their importance, the ISO is poorly simulated and predicted by numerical models. A joint diagnostic and modeling study of the ISO is conducted, concentrating on the period between the suppressed and active (referred to as the ""transition"") period that is hypothesized to be the defining stage for the development of the intraseasonal mode and the component that is most poorly simulated. The diagnostic study uses data from the Tropical Ocean Global Atmosphere Coupled Ocean-Atmosphere Response Experiment (TOGA COARE). It is found that during the transition period, the ocean and the atmosphere undergo gradual but large-scale and high-amplitude changes, especially the moistening of the lower troposphere caused jointly by the anomalously warm sea surface temperature arising from minimal cloud and low winds during the suppressed phase and the large-scale subsidence that inhibits the formation of locally deep convection. Using a cloud classification scheme based on microwave and infrared satellite data, it is observed that midtop (cloud with a top in the middle troposphere) non-precipitating clouds are a direct response of the low-level moisture buildup. To investigate the sensitivity of ISO simulations to the transitional phase, the European Centre for Medium-Range Weather Forecasts (ECMWF) coupled ocean-atmosphere climate model is used. The ECMWF was run serially in predictive ensemble mode (five members) for 30-day periods starting from 1 December 1992 to 30 January 1993, encompassing the ISO occurring in late December. Predictability of the active convective period of the ISO is poor when initialized before the transitional phases of the ISO. However, when initialized with the correct lower-tropospheric moisture field, predictability increases substantially, although the model convective parameterization appears to trigger convection too quickly without allowing an adequate buildup of convective available potential energy during the transition period. © 2006 American Meteorological Society."
"7005956183;7005421048;6603892183;7003309358;7005067383;7102661133;15124069000;56152167900;","Cloud-radiation studies during the European cloud and radiation experiment (EUCREX)",1998,"10.1023/A:1006544220339","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0031845101&doi=10.1023%2fA%3a1006544220339&partnerID=40&md5=f5700f22c8c1504fe0616a053126cf6c","The dominant role of clouds in modulating and interacting with radiative energy transports within the atmosphere, in providing precipitation, transporting water and influencing air-chemical processes is still not understood well enough to be accurately represented within atmospheric circulation and climate models over all regions of the globe. Also the extraction of real-world cloud properties from satellite measurements still contains uncertainties. Therefore, various projects have been developed within the Global Energy and Water Cycle Experiment (GEWEX), to achieve more accurate solutions for this problem by direct measurements within cloud fields and other complementary studies. They are based on the hypothesis, that most relevant properties of cloud fields can be parametrized on the basis of the prognostic field variables of atmospheric circulation models, and that the cloud microphysical properties can directly be related - with additional parameters on the particle shapes etc.-to the radiative transfer properties. One of these projects has been the European Cloud and Radiation Experiment (EUCREX) with its predecessor ICE (International Cirrus Experiment). The EUCREX and ICE provided a common platform for research groups from France, Germany, Sweden and the United Kingdom to concentrate their efforts primarily on high, cold cirrus. They showed, with data from satellites, that this cloud species enhances the atmospheric greenhouse-effect. Numerical mesoscale models were used in sensitivity studies on cloud developments. In-situ measurements of cloud properties were made during more than 30 aircraft missions, where also inflight comparisons of various instruments were made to ensure the quality of data sets measured from different aircraft. The particle sampling probes, used for in-cloud measurements, showed a disagreement in total number density in all ranges between about 20-50%, while all other instruments agreed quite satisfactorily. A few measured holographic data provided information on typical ice-crystal shapes, which were used in numerical simulations of their absorption and scattering properties. Several new instruments for both in-situ and remote measurement, such as a polar nephelometer, a chopped pyrgeometer and an imaging multispectral polarimeter (POLDER) for cloud and radiation measurements were tested and improved. New algorithms were developed for cloud classifications in multispectral satellite images and also for simulations of the scattering of radiation by non-spherical particles. This paper primarily summarizes the EUCREX results obtained between 1989 and 1996, and provides examples of the many results which have been obtained so far. It is not a complete review of the world-wide state in this field, but it tries to place the EUCREX results into the world-wide development. Therefore many references are made to the results of other groups, which in turn influenced the work within EUCREX."
"6508369265;57196396429;","Satellite estimation of the tropical precipitation using the METEOSTAT and SSM/I data",1994,"10.1016/0169-8095(94)90097-3","https://www.scopus.com/inward/record.uri?eid=2-s2.0-33749360281&doi=10.1016%2f0169-8095%2894%2990097-3&partnerID=40&md5=acd525c7c83e26b0f2e8a0816e1ac7b8","The purpose of this study is to test a satellite method for estimating precipitation using infrared and microwave data. The method is validated by comparing the rain estimates to the ground precipitation measurements, over continental tropical regions (West Africa). The rain estimation method used is based on an automatic classification algorithm combining infrared and microwave satellite data. The method takes advantage of both, the good time and space resolution of infrared satellite images and the rain related information retrieved from microwave images. This method of rain and cloud classification (RACC) gives homogeneous classes characterising the different types of clouds or rain-rates. A set of microwave images from the 86 GHz channel of the passive microwave radiometer SSM/I and of the coincident infrared images from Meteosat is used in the learning phase of the classification process, while the full set of half-hourly infrared images is needed for the application phase. The areal rain estimates are computed with the microwave-infrared combined RACC method for time periods ranging from one to twelve hours and for different areas up to 120 km × 120 km. The correlation with the ground rainfall data given by the raingauge network of a validation site in Niger are estimated and compared to the correlations obtained for the rain estimated derived from a method based on a single infrared threshold and using the Meteosat images only. The improvement brought by the RACC method is discussed. © 1994."
"57212075803;","A pattern recognition technique for retrieving humidity profiles from Meteosat or GOES imagery",1993,"10.1175/1520-0450(1993)032<1592:APRTFR>2.0.CO;2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0027804935&doi=10.1175%2f1520-0450%281993%29032%3c1592%3aAPRTFR%3e2.0.CO%3b2&partnerID=40&md5=07ecbf9c3622e2171bb1b416d7566aa2","A retrieval technique based on cloud classification is designed to derive humidity profiles from Meteosat visible (VIS), infrared window (IR), and water vapour (WV) channels, or equivalent sensors available on other satellites. Dewpoint depression (DPD) is the variable retrieved at six standard levels: 1000, 850, 700, 500, 400, and 300 mb. It is found that a classification in seven (IR only) or nine (VIS-IR) classes contains the essential information on cloud type for the application sought. The WV channel proved very useful in improving DPDs at higher levels while the VIS channel improved inferences of low-level humidity in classes associated with precipitation. The main advantages of the technique are its applicability to cloudy atmospheres, its robustness, and the fact that it can efficiently provide retrievals from 60°S to 60°N every half-hour. -Author"
"9637547100;","Land cover classification of cloud-contaminated multitemporal high-resolution images",2011,"10.1109/TGRS.2010.2052464","https://www.scopus.com/inward/record.uri?eid=2-s2.0-78650911205&doi=10.1109%2fTGRS.2010.2052464&partnerID=40&md5=bc122605a69e04b6ac6d3f37e2507f22","We show how methods proposed in the statistical community dealing with missing data may be applied for land cover classification, where optical observations are missing due to clouds and snow. The proposed method is divided into two stages: 1) cloud/snow classification and 2) training and land cover classification. The purpose of the cloud/snow classification stage is to determine which pixels are missing due to clouds and snow. All pixels in each optical image are classified into the classes cloud, snow, water, and vegetation using a suitable classifier. The pixels classified as cloud or snow are labeled as missing, and this information is used in the subsequent training and classification stage, which deals with classification of the pixels into various land cover classes. For land cover classification, we apply the maximum-likelihood (assuming normal distributions), κ-nearest neighbor, and Parzen classifiers, all modified to handle missing features. The classifiers are evaluated on Landsat (both Thematic Mapper and Enhanced Thematic Mapper Plus) images covering a scene at about 900 m a.s.l. in the Hardangervidda mountain plateau in Southern Norway, where 4869 in situ samples of the land cover classes water, ridge, leeside, snowbed, mire, forest, and rock are obtained. The results show that proper modeling of the missing pixels improves the classification rate by 5%10%, and by using multiple images, we increase the chance of observing the land cover type substantially. The nonparametric classifiers handle nonignorable missing-data mechanisms and are therefore particularly suitable for remote sensing applications where the pixels covered by snow and cloud may depend on the land cover type. © 2006 IEEE."
"57192387537;57190963447;56203143700;23036019000;23006934800;7005742190;35611187000;7004609788;","Fast cloud segmentation using convolutional neural networks",2018,"10.3390/rs10111782","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85057086755&doi=10.3390%2frs10111782&partnerID=40&md5=7d2ca03a9cf9c3a7863586d39e356613","Information about clouds is important for observing and predicting weather and climate as well as for generating and distributing solar power. Most existing approaches extract cloud information from satellite data by classifying individual pixels instead of using closely integrated spatial information, ignoring the fact that clouds are highly dynamic, spatially continuous entities. This paper proposes a novel cloud classification method based on deep learning. Relying on a Convolutional Neural Network (CNN) architecture for image segmentation, the presented Cloud Segmentation CNN (CS-CNN), classifies all pixels of a scene simultaneously rather than individually. We show that CS-CNN can successfully process multispectral satellite data to classify continuous phenomena such as highly dynamic clouds. The proposed approach produces excellent results on Meteosat Second Generation (MSG) satellite data in terms of quality, robustness, and runtime compared to other machine learning methods such as random forests. In particular, comparing CS-CNN with the CLAAS-2 cloud mask derived from MSG data shows high accuracy (0.94) and Heidke Skill Score (0.90) values. In contrast to a random forest, CS-CNN produces robust results and is insensitive to challenges created by coast lines and bright (sand) surface areas. Using GPU acceleration, CS-CNN requires only 25 ms of computation time for classification of images of Europe with 508 × 508 pixels. © 2018 by the authors."
"56135196400;7403872687;6507378331;7005052907;7401526171;","Precipitation identification with bispectral satellite information using deep learning approaches",2017,"10.1175/JHM-D-16-0176.1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85019030320&doi=10.1175%2fJHM-D-16-0176.1&partnerID=40&md5=558e83622cfe32be2e28350847eda415","In the development of a satellite-based precipitation product, two important aspects are sufficient precipitation information in the satellite-input data and proper methodologies, which are used to extract such information and connect it to precipitation estimates. In this study, the effectiveness of the state-of-the-art deep learning (DL) approaches to extract useful features from bispectral satellite information, infrared (IR), and water vapor (WV) channels, and to produce rain/no-rain (R/NR) detection is explored. To verify the methodologies, two models are designed and evaluated: the first model, referred to as the DL-IR only method, applies deep learning approaches to the IR data only; the second model, referred to as the DL-IR+WV method, incorporates WV data to further improve the precipitation identification performance. The radar stage IV data are the reference data used as ground observation. The operational product, Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS), serves as a baseline model with which to compare the performances. The experiments show significant improvement for both models in R/NR detection. The overall performance gains in the critical success index (CSI) are 21.60% and 43.66% over the verification periods for the DL-IR only model and the DL-IR+WV model compared to PERSIANN-CCS, respectively. In particular, the performance gains in CSI are as high as 46.51% and 94.57% for the models for the winter season. Moreover, specific case studies show that the deep learning techniques and the WV channel information effectively help recover a large number of missing precipitation pixels under warm clouds while reducing false alarms under cold clouds. © 2017 American Meteorological Society."
"57069455200;36064917000;55500134600;55972035800;55292994700;57189502828;","Discriminative-Dictionary-Learning-Based Multilevel Point-Cluster Features for ALS Point-Cloud Classification",2016,"10.1109/TGRS.2016.2599163","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85027405929&doi=10.1109%2fTGRS.2016.2599163&partnerID=40&md5=36584dd7e642cc8a128538dd41f72da3","Efficient presentation and recognition of on-ground objects from airborne laser scanning (ALS) point clouds are a challenging task. In this paper, we propose an approach that combines a discriminative-dictionary-learning-based sparse coding and latent Dirichlet allocation (LDA) to generate multilevel point-cluster features for ALS point-cloud classification. Our method takes advantage of the labels of training data and each dictionary item to enforce discriminability in sparse coding during the dictionary learning process and more accurately further represent point-cluster features. The multipath AdaBoost classifiers with the hierarchical point-cluster features are trained, and we apply them to the classification of unknown points by the heritance of the recognition results under different paths. Experiments are performed on different ALS point clouds; the experimental results have shown that the extracted point-cluster features combined with the multipath classifiers can significantly enhance the classification accuracy, and they have demonstrated the superior performance of our method over other techniques in point-cloud classification. © 2016 IEEE."
"7006802750;8605057200;55476786400;15841242400;","Comparison of GOES cloud classification algorithms employing explicit and implicit physics",2009,"10.1175/2009JAMC2103.1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-70349678789&doi=10.1175%2f2009JAMC2103.1&partnerID=40&md5=42e42582831fc5dfe6f9a36ce19e2509","Cloud-type classification based on multispectral satellite imagery data has been widely researched and demonstrated to be useful for distinguishing a variety of classes using a wide range of methods. The research described here is a comparison of the classifier output from two very different algorithms applied to Geostationary Operational Environmental Satellite (GOES) data over the course of one year. The first algorithm employs spectral channel thresholding and additional physically based tests. The second algorithm was developed through a supervised learning method with characteristic features of expertly labeled image samples used as training data for a 1-nearest-neighbor classification. The latter's ability to identify classes is also based in physics, but those relationships are embedded implicitly within the algorithm. A pixel-to-pixel comparison analysis was done for hourly daytime scenes within a region in the northeastern Pacific Ocean. Considerable agreement was found in this analysis, with many of the mismatches or disagreements providing insight to the strengths and limitations of each classifier. Depending upon user needs, a rule-based or other postprocessing system that combines the output from the two algorithms could provide the most reliable cloud-type classification."
"55898333500;","Effects of an assimilation of radar and satellite data on a very-short range forecast of heavy convective rainfalls",2009,"10.1016/j.atmosres.2008.11.001","https://www.scopus.com/inward/record.uri?eid=2-s2.0-67349280717&doi=10.1016%2fj.atmosres.2008.11.001&partnerID=40&md5=4240fd1573ff1e052dc054a05c3bd798","An assimilation method of radar reflectivity and satellite data into a NWP model COSMO with a horizontal resolution of 2.8 km and its impact on a very short range forecast of precipitation are presented. The assimilation method consists in a correction of model water vapour mixing ratio using the nudging technique. The correction depends on: (i) the difference between model and observed precipitation, which is derived from radar reflectivity, (ii) whether observed clouds are classified as precipitating. The cloud classification utilises two channels (10.8 and 6.2 μm) from the Meteosat Second Generation (MSG), which are also used to estimate precipitation. Two types of corrections are examined with respect to places at which the correction is applied. First approach performs the correction at the same grid point where the difference is calculated. Second approach performs the correction at a grid point advected upwind from the point where the difference is calculated. Forecasts of three convective events with heavy local precipitation were evaluated subjectively (by eye) and by applying the fraction skill score. The results show that the assimilation of radar information or combined radar and MSG data significantly improves precipitation forecasts for at least three forecast hours. Despite uncertainties in the relationship between MSG data and precipitation, the assimilation method that combines radar and MSG data shows comparable or better precipitation forecasts than when only radar reflectivity is used. The advantage of the approach transforming MSG measurements into precipitation is that the same assimilation technique can be applied to both radar reflectivity and satellite data. © 2008 Elsevier B.V. All rights reserved."
"8832722300;6604000335;8680433600;7005067383;6603518408;","Case study of inhomogeneous cloud parameter retrieval from MODIS data",2005,"10.1029/2005GL022791","https://www.scopus.com/inward/record.uri?eid=2-s2.0-24944454952&doi=10.1029%2f2005GL022791&partnerID=40&md5=e2cd90cda58ca25589b2178206807501","Cloud parameter retrieval of inhomogeneous and fractional clouds is performed for a stratocumulus scene observed by MODIS at a solar zenith angle near 60°. The method is based on the use of neural network technique with multispectral and multiscale information. It allows to retrieve six cloud parameters, i.e. pixel means and standard deviations of optical thickness and effective radius, fractional cloud cover, and cloud top temperature. Retrieved cloud optical thickness and effective radius are compared to those retrieved with a classical method based on the homogeneous cloud assumption. Subpixel fractional cloud cover and optical thickness inhomogeneity are compared with their estimates obtained from 250m pixel observations; this comparison shows a fairly good agreement. The cloud top temperature appears also retrieved quite suitably. Copyright 2005 by the American Geophysical Union."
"57189047428;23480942700;7005627747;55944549200;","Deriving comprehensive forest structure information from mobile laser scanning observations using automated point cloud classification",2016,"10.1016/j.envsoft.2016.04.025","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84964815284&doi=10.1016%2fj.envsoft.2016.04.025&partnerID=40&md5=b9241deb2e8fafb0017c70fd21398b5b","The advent of mobile laser scanning has enabled time efficient and cost effective collection of forest structure information. To make use of this technology in calibrating or evaluating models of forest and landscape dynamics, there is a need to systematically and reproducibly automate the processing of LiDAR point clouds into quantities of forest structural components. Here we propose a method to classify vegetation structural components of an open-understorey eucalyptus forest, scanned with a 'Zebedee' mobile laser scanner. It detected 98% of the tree stems (N = 50) and 80% of the elevated understorey components (N = 15). Automatically derived DBH values agreed with manual field measurements with r2 = 0.72, RMSE = 3.8 cm, (N = 27), and total basal area agreed within 1.5%. Though this methodological study was restricted to one ecosystem, the results are promising for use in applications such as fuel load, habitat structure, and biomass estimations. © 2016 Elsevier Ltd."
"56204562000;25637650700;9234066900;6602390932;55056533200;56175387100;55484648400;55502994400;","Cloud and aerosol classification for 2.5 years of MAX-DOAS observations in Wuxi (China) and comparison to independent data sets",2015,"10.5194/amt-8-5133-2015","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84949921121&doi=10.5194%2famt-8-5133-2015&partnerID=40&md5=6755555af760084304500d26f0eef250","Multi-axis differential optical absorption spectroscopy (MAX-DOAS) observations of trace gases can be strongly influenced by clouds and aerosols. Thus it is important to identify clouds and characterize their properties. In a recent study Wagner et al. (2014) developed a cloud classification scheme based on the MAX-DOAS measurements themselves with which different ""sky conditions"" (e.g., clear sky, continuous clouds, broken clouds) can be distinguished. Here we apply this scheme to long-term MAX-DOAS measurements from 2011 to 2013 in Wuxi, China (31.57° N, 120.31° E). The original algorithm has been adapted to the characteristics of the Wuxi instrument, and extended towards smaller solar zenith angles (SZA). Moreover, a method for the determination and correction of instrumental degradation is developed to avoid artificial trends of the cloud classification results. We compared the results of the MAX-DOAS cloud classification scheme to several independent measurements: aerosol optical depth from a nearby Aerosol Robotic Network (AERONET) station and from two Moderate Resolution Imaging Spectroradiometer (MODIS) instruments, visibility derived from a visibility meter and various cloud parameters from different satellite instruments (MODIS, the Ozone Monitoring Instrument (OMI) and the Global Ozone Monitoring Experiment (GOME-2)). Here it should be noted that no quantitative comparison between the MAX-DOAS results and the independent data sets is possible, because (a) not exactly the same quantities are measured, and (b) the spatial and temporal sampling is quite different. Thus our comparison is performed in a semi-quantitative way: the MAX-DOAS cloud classification results are studied as a function of the external quantities. The most important findings from these comparisons are as follows: (1) most cases characterized as clear sky with low or high aerosol load were associated with the respective aerosol optical depth (AOD) ranges obtained by AERONET and MODIS; (2) the observed dependences of MAX-DOAS results on cloud optical thickness and effective cloud fraction from satellite confirm that the MAX-DOAS cloud classification scheme is sensitive to cloud (optical) properties; (3) the separation of cloudy scenes by cloud pressure shows that the MAX-DOAS cloud classification scheme is also capable of detecting high clouds; (4) for some cloud-free conditions, especially with high aerosol load, the coincident satellite observations indicated optically thin and low clouds. This finding indicates that the satellite cloud products contain valuable information on aerosols. © Author(s) 2015."
"7404284987;57212025640;","Block-based cloud classification with statistical features and distribution of local texture features",2015,"10.5194/amt-8-1173-2015","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84924353331&doi=10.5194%2famt-8-1173-2015&partnerID=40&md5=3dd27596494b62e160284c69f9a5f913","This work performs cloud classification on all-sky images. To deal with mixed cloud types in one image, we propose performing block division and block-based classification. In addition to classical statistical texture features, the proposed method incorporates local binary pattern, which extracts local texture features in the feature vector. The combined feature can effectively preserve global information as well as more discriminating local texture features of different cloud types. The experimental results have shown that applying the combined feature results in higher classification accuracy compared to using classical statistical texture features. In our experiments, it is also validated that using block-based classification outperforms classification on the entire images. Moreover, we report the classification accuracy using different classifiers including the k-nearest neighbor classifier, Bayesian classifier, and support vector machine. © Author(s) 2015."
"23983423100;7003995144;35095461100;57203217480;35863893500;","A statistical approach for rain intensity differentiation using Meteosat Second Generation-Spinning Enhanced Visible and InfraRed Imager observations",2014,"10.5194/hess-18-2559-2014","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84904335033&doi=10.5194%2fhess-18-2559-2014&partnerID=40&md5=97ec575a593229fdc9841e7e2aad0e6c","This study exploits the Meteosat Second Generation (MSG)-Spinning Enhanced Visible and Infrared Imager (SEVIRI) observations to evaluate the rain class at high spatial and temporal resolutions and, to this aim, proposes the Rain Class Evaluation from Infrared and Visible observation (RainCEIV) technique. RainCEIV is composed of two modules: a cloud classification algorithm which individuates and characterizes the cloudy pixels, and a supervised classifier that delineates the rainy areas according to the three rainfall intensity classes, the non-rainy (rain rate value &lt; 0.5 mm h-1) class, the light-to-moderate rainy class (0.5 mm hg-1 ≤ rain rate value &lt; 4 mm h-1), and the heavy-to-very-heavy-rainy class (rain rate value ≥ 4 mm h-1). The second module considers as input the spectral and textural features of the infrared and visible SEVIRI observations for the cloudy pixels detected by the first module. It also takes the temporal differences of the brightness temperatures linked to the SEVIRI water vapour channels as indicative of the atmospheric instability strongly related to the occurrence of rainfall events. The rainfall rates used in the training phase are obtained through the Precipitation Estimation at Microwave frequencies, PEMW (an algorithm for rain rate retrievals based on Atmospheric Microwave Sounder Unit (AMSU)-B observations). RainCEIV's principal aim is that of supplying preliminary qualitative information on the rainy areas within the Mediterranean Basin where there is no radar network coverage. The results of RainCEIV have been validated against radar-derived rainfall measurements from the Italian Operational Weather Radar Network for some case studies limited to the Mediterranean area. The dichotomous assessment related to daytime (nighttime) validation shows that RainCEIV is able to detect rainy/non-rainy areas with an accuracy of about 97% (96%), and when all the rainy classes are considered, it shows a Heidke skill score of 67% (62%), a bias score of 1.36 (1.58), and a probability of detection of rainy areas of 81% (81%). © Author(s) 2014."
"37461808200;55200342600;56094444900;56095482200;6602438071;","Probabilistic approach to cloud and snow detection on Advanced Very High Resolution Radiometer (AVHRR) imagery",2014,"10.5194/amt-7-799-2014","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84897395427&doi=10.5194%2famt-7-799-2014&partnerID=40&md5=c813facc8c47f86381d8fc3c95506061","Derivation of probability estimates complementary to geophysical data sets has gained special attention over the last years. Information about a confidence level of provided physical quantities is required to construct an error budget of higher-level products and to correctly interpret final results of a particular analysis. Regarding the generation of products based on satellite data a common input consists of a cloud mask which allows discrimination between surface and cloud signals. Further the surface information is divided between snow and snow-free components. At any step of this discrimination process a misclassification in a cloud/snow mask propagates to higher-level products and may alter their usability.Within this scope a novel probabilistic cloud mask (PCM) algorithm suited for the 1 km1 km Advanced Very High Resolution Radiometer (AVHRR) data is proposed which provides three types of probability estimates between: cloudy/clear-sky, cloudy/snow and clearsky/ snow conditions. As opposed to th majority of available techniques which are usually based on the decision-tree approach in the PCM algorithm all spectral, angular and ancillary information is used in a single step to retrieve probability estimates from the precomputed look-up tables (LUTs). Moreover, the issue of derivation of a single threshold value for a spectral test was overcome by the concept of multidimensional information space which is divided into small bins by an extensive set of intervals. The discrimination between snow and ice clouds and detection of broken, thin clouds was enhanced by means of the invariant coordinate system (ICS) transformation. The study area covers a wide range of environmental conditions spanning from Iceland through central Europe to northern parts of Africa which exhibit diverse difficulties for cloud/snow masking algorithms. The retrieved PCM cloud classification was compared to the Polar Platform System (PPS) version 2012 and Moderate Resolution Imaging Spectroradiometer (MODIS) collection 6 cloud masks, SYNOP (surface synoptic observations) weather reports, Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) vertical feature mask version 3 and to MODIS collection 5 snow mask. The outcomes of conducted analyses proved fine detection skills of the PCM method with results comparable to or better than the reference PPS algorithm."
"7005354212;7005236944;7004587891;14014252200;55465604600;6602128668;22234180300;","A geometry-based approach to identifying cloud shadows in the VIIRS cloud mask algorithm for NPOESS",2009,"10.1175/2009JTECHA1198.1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-73049088947&doi=10.1175%2f2009JTECHA1198.1&partnerID=40&md5=1d550b2ff87707efc1579b6337b60043","A geometry-based approach is presented to identify cloud shadows using an automated cloud classification algorithm developed for the National Polar-orbiting Operational Environmental Satellite System (NPOESS) program. These new procedures exploit both the cloud confidence and cloud phase intermediate products generated by the Visible/Infrared Imager/Radiometer Suite (VIIRS) cloud mask (VCM) algorithm. The procedures have been tested and found to accurately detect cloud shadows in global datasets collected by NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) sensor and are applied over both land and ocean background conditions. These new procedures represent a marked departure from those used in the heritage MODIS cloud mask algorithm, which utilizes spectral signatures in an attempt to identify cloud shadows. However, they more closely follow those developed to identify cloud shadows in the MODIS Surface Reflectance (MOD09) data product. Significant differences were necessary in the implementation of the MOD09 procedures to meet NPOESS latency requirements in the VCM algorithm. In this paper, the geometry-based approach used to predict cloud shadows is presented, differences are highlighted between the heritage MOD09 algorithm and new VIIRS cloud shadow algorithm, and results are shown for both these algorithms plus cloud shadows generated by the spectral-based approach. The comparisons show that the geometry-based procedures produce cloud shadows far superior to those predicted with the spectral procedures. In addition, the new VCM procedures predict cloud shadows that agree well with those found in the MOD09 product while significantly reducing the execution time as required to meet the operational time constraints of the NPOESS system. © 2009 American Meteorological Society."
"25227508200;7005339628;","A new methodology for cloud detection and classification with ASTER data",2008,"10.1029/2008GL034644","https://www.scopus.com/inward/record.uri?eid=2-s2.0-56449097731&doi=10.1029%2f2008GL034644&partnerID=40&md5=205c8b15de81f2f55d636edc567a014c","High spatial resolution sensors such as Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) have the potential to produce gridded, large area datasets of surface parameters such as elevation and emissivity. These datasets are typically derived by combining all clear-sky pixels over a given location for a specified time period necessitating the use of an automated cloud detection and classification algorithm. The current ASTER L1A cloud mask lacks several key features needed to use it for this purpose. We have developed a new cloud detection algorithm which addresses these limitations by using a 2-pass approach similar to Landsat-7 and including further spectral tests for cirrus and cloud shadows from MODIS. The new cloud detection methodology is described together with several case studies that highlight key aspects of the algorithm and comparisons with the MODIS and the current ASTER L1A cloud mask. Copyright 2008 by the American Geophysical Union."
"7006246996;7004225735;","Evaluation of an AVHRR cloud detection and classification method over the central arctic ocean",1998,"10.1175/1520-0450(1998)037<0166:eoaacd>2.0.co;2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0031806977&doi=10.1175%2f1520-0450%281998%29037%3c0166%3aeoaacd%3e2.0.co%3b2&partnerID=40&md5=58034ce0d083cc62c409f9285c4c8ea8","A cloud classification method that uses both multispectral and textural features with a maximum likelihood discriminator is applied to full-resolution AVHRR (Advanced Very High Resolution Radiometer) data from 100 NOAA polar-orbiter overpasses tracked from an icebreaker during the 1994 Arctic Ocean Section. The cloud classification method is applied to the 32 × 32 pixel cell centered about the ship's position during each overpass. These overpasses have matching surface weather observations in the form of all-sky photographs or, during a period of heavy weather, an objective record that the sky was overcast with low water clouds. The cloud classifications from the maximum likelihood method are compared with the surface weather observations to determine if the automated satellite cloud classifier actually produces realistic descriptions of the scene. These comparisons are favorable in most cases, with the exception of a frequent error in which the classifier confuses Ci/Cc/Ac with extensive low water clouds over sea ice. This overall evaluation does not change appreciably if global area coverage resolution is used instead of full resolution or if the authors attempt to recalibrate the data to the NOAA-7 data for which the algorithm was originally developed. The authors find that the Ci/Cc/Ac cloud error can usually be avoided by 1) modifying the textural feature values for some cloud-over-ice categories and 2) applying a threshold value of 30% to the AVHRR channel 2 albedo averaged over the cell (and normalized by the cosine of the solar zenith angle). For a cell that the classifier identifies as containing Ci/Cc/Ac over sea ice, a cell-average channel 2 albedo greater than 30% usually indicates that the cell instead contains extensive low water clouds. When compared to the surface weather observations, the skill score of the satellite cloud classifier thus modified is 81%, which is very close to that claimed by its original author. This study suggests that satellite cloud detection and classification schemes based on both spectral signatures and texture recognition may indeed yield realistic results."
"57070621900;57193115806;57216328909;57193112920;","MSNet: Multi-scale convolutional network for point cloud classification",2018,"10.3390/rs10040612","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85045965088&doi=10.3390%2frs10040612&partnerID=40&md5=9448cc9e80e0f218dfe7d7050e4c96e9","Point cloud classification is quite challenging due to the influence of noise, occlusion, and the variety of types and sizes of objects. Currently, most methods mainly focus on subjectively designing and extracting features. However, the features rely on prior knowledge, and it is also difficult to accurately characterize the complex objects of point clouds. In this paper, we propose a concise multi-scale convolutional network (MSNet) for adaptive and robust point cloud classification. Both the local feature and global context are incorporated for this purpose. First, around each point, the spatial contexts of different sizes are partitioned as voxels of different scales. A voxel-based MSNet is then simultaneously applied at multiple scales to adaptively learn the discriminative local features. The class probability of a point cloud is predicted by fusing the features together across multiple scales. Finally, the predicted class probabilities of MSNet are optimized globally using the conditional random field (CRF) with a spatial consistency constraint. The proposed method was tested with data sets of mobile laser scanning (MLS), terrestrial laser scanning (TLS), and airborne laser scanning (ALS) point clouds. The experimental results show that the proposed method was able to achieve appreciable classification accuracies of 83.18%, 98.24%, and 97.02% on the MLS, TLS, and ALS data sets, respectively. The results also demonstrate that the proposed network has a strong generalization capability for classifying different kinds of point clouds under complex urban environments. © 2018 by the authors."
"36064917000;57189490787;57200687118;57192190518;","Large-scale urban point cloud labeling and reconstruction",2018,"10.1016/j.isprsjprs.2018.02.008","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85042192599&doi=10.1016%2fj.isprsjprs.2018.02.008&partnerID=40&md5=85a41d4e4a9e294a7e0b17101a6db041","The large number of object categories and many overlapping or closely neighboring objects in large-scale urban scenes pose great challenges in point cloud classification. In this paper, a novel framework is proposed for classification and reconstruction of airborne laser scanning point cloud data. To label point clouds, we present a rectified linear units neural network named ReLu-NN where the rectified linear units (ReLu) instead of the traditional sigmoid are taken as the activation function in order to speed up the convergence. Since the features of the point cloud are sparse, we reduce the number of neurons by the dropout to avoid over-fitting of the training process. The set of feature descriptors for each 3D point is encoded through self-taught learning, and forms a discriminative feature representation which is taken as the input of the ReLu-NN. The segmented building points are consolidated through an edge-aware point set resampling algorithm, and then they are reconstructed into 3D lightweight models using the 2.5D contouring method (Zhou and Neumann, 2010). Compared with deep learning approaches, the ReLu-NN introduced can easily classify unorganized point clouds without rasterizing the data, and it does not need a large number of training samples. Most of the parameters in the network are learned, and thus the intensive parameter tuning cost is significantly reduced. Experimental results on various datasets demonstrate that the proposed framework achieves better performance than other related algorithms in terms of classification accuracy and reconstruction quality. © 2018 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS)"
"55818523800;7801347466;24401175200;36341884100;","GEOMETRIC FEATURES and THEIR RELEVANCE for 3D POINT CLOUD CLASSIFICATION",2017,"10.5194/isprs-annals-IV-1-W1-157-2017","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85044510127&doi=10.5194%2fisprs-annals-IV-1-W1-157-2017&partnerID=40&md5=42ce6cee6b2f221578ac48c809423209","In this paper, we focus on the automatic interpretation of 3D point cloud data in terms of associating a class label to each 3D point. While much effort has recently been spent on this research topic, little attention has been paid to the influencing factors that affect the quality of the derived classification results. For this reason, we investigate fundamental influencing factors making geometric features more or less relevant with respect to the classification task. We present a framework which consists of five components addressing point sampling, neighborhood recovery, feature extraction, classification and feature relevance assessment. To analyze the impact of the main influencing factors which are represented by the given point sampling and the selected neighborhood type, we present the results derived with different configurations of our framework for a commonly used benchmark dataset for which a reference labeling with respect to three structural classes (linear structures, planar structures and volumetric structures) as well as a reference labeling with respect to five semantic classes (Wire, Pole/Trunk, Façade, Ground and Vegetation) is available. © 2017 Copernicus GmbH. All rights reserved."
"35389411400;22980035400;55574923100;56900194500;7006204393;6701873414;7005452282;","X-band polarimetric and Ka-band Doppler spectral radar observations of a graupel-producing arctic mixed-phase cloud",2015,"10.1175/JAMC-D-14-0315.1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84943773941&doi=10.1175%2fJAMC-D-14-0315.1&partnerID=40&md5=9c6fa97ef80e58fa9dec353c8a158ba9","Characteristics of graupel in an Arctic deep mixed-phase cloud on 7 December 2013 were identified with observations from an X-band scanning polarimetric radar and a Ka-band zenith-pointing radar in conjunction with scattering calculations. The cloud system produced generating cells and strongly sheared precipitation fall streaks. The X-band radar hemispheric RHI observables revealed spatial sorting of polarimetric signatures: decreasing (with increasing range) differential propagation phase shift ϕDP, negative specific differential phase KDP collocated with negative differential reflectivity ZDR in the upper half of the fall streak, and increasing or near-constant ϕDP with positive ZDR at the bottom edge of the fall streak. The negative KDP and ZDR, indicating prolate particles with vertically oriented maximum dimensions, were consistent with small, slow-falling conical graupel coexisting with low concentrations of more isometric graupel. The observed negative KDP values were best matched by scattering calculations for small, dense conical graupel with 30°-40° cone angles. The positive KDP and ZDR and the Doppler spectra indicate that large isometric graupel coexisted with a second population of slower-falling rimed platelike particles in the lower half of the fall streak. Through the core of the fall streak, ϕDP decreased in range while ZDR was slightly positive, indicating that the prolate conical graupel dominated ϕDP while the isometric larger graupel dominated reflectivity (and thus ZDR). These results demonstrate the capability of polarimetric observables and Doppler spectra to distinguish different growth stages of rimed particles, allowing for the improvement of hydrometeor classification methods. © 2015 American Meteorological Society."
"13006055400;7102425008;","The impact of low clouds on surface shortwave radiation in the ECMWF model",2012,"10.1175/MWR-D-11-00316.1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84871913014&doi=10.1175%2fMWR-D-11-00316.1&partnerID=40&md5=8715dcefad060f4afdf8dcf4a2849b5d","The long-term measurement records from the Atmospheric Radiation Measurement site on the Southern Great Plains show evidence of a bias in the ECMWF model's surface irradiance. Based on previous studies, which have suggested that summertime shallow clouds may contribute to the bias, an evaluation of 146 days with observed nonprecipitating fair-weather cumulus clouds is performed. In-cloud liquid water path and effective radius are both overestimated in the model with liquid water path dominating to produce clouds that are too reflective. These are compensated by occasional cloud-free days in the model such that the fairweather cumulus regime overall does not contribute significantly to the multiyear daytime mean surface irradiance bias of 23 W m22. To further explore the origin of the bias, observed and modeled cloud fraction profiles over 6 years are classified and sorted based on the surface irradiance bias associated with each sample pair. Overcast low cloud conditions during the spring and fall seasons are identified as a major contributor. For samples with low cloud present in both observations and model, opposing surface irradiance biases are found for overcast and broken cloud cover conditions. A reduction of cloud liquid to a third for broken low clouds and an increase by a factor of 1. 5 in overcast situations improves agreement with the observed liquid water path distribution. This approach of combining the model shortwave bias with a cloud classification helps to identify compensating errors in the model, providing guidance for a targeted improvement of cloud parameterizations. ©2012 American Meteorological Society."
"26026749200;23094149200;6603478504;","Phenomenological description of tropical clouds using cloudsat cloud classification",2012,"10.1175/MWR-D-11-00247.1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84867935570&doi=10.1175%2fMWR-D-11-00247.1&partnerID=40&md5=5405d1eb1dba68064309e46a80dc386b","Two years of tropical oceanic cloud observations are analyzed using the operational CloudSat cloud classification product and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) lidar. Relationships are examined between cloud types, sea surface temperature (SST), and location during the CloudSat early morning and afternoon overpasses. Based on CloudSat and combined lidar-radar products, the maximum and minimum cloud fractions occur at SSTs near 303 and 299 K, respectively, corresponding to deep convective/detrained cloud populations and the transition from shallow to deep convection. For SSTs below approximately 301 K, low clouds (stratiform and stratocumulus) are dominant (cloud fraction between 0.15 and 0.37) whereas high clouds are dominant for SSTs greater than about 301 K (cloud fraction between 0.18 and 0.28). Consistent with previous studies, most tropical low clouds are associated with lower SSTs, with a strong inverse linear relationship between low cloud frequency and SST. For all cloud types except nimbostratus, stratus, and stratocumulus, a sharp increase in frequency of occurrence is observed for SSTs between 299 and 300.5 K, deduced as the onset of deeper convection. Peak fractions of high, deep convective, altostratus, and altocumulus clouds occur at SSTs close to 303 K, while cumulus clouds, which have lower tops, show a smooth cloud fractional peak about 28° cooler. Deep convective and other high cloud types decrease sharply above SSTs of 303 K, in accordance with previous work suggesting a narrow window of tropical deep convection. Finally, significant cloud frequency differences exist between CloudSat early morning and afternoon overpasses, suggesting a diurnal cycle of some cloud types, particularly stratocumulus, high, and deep convective clouds. ©2012 American Meteorological Society."
"55535058500;15051249600;","Evaluating the performance of SVM in dust aerosol discrimination and testing its ability in an extended area",2012,"10.1109/JSTARS.2012.2206572","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84872083642&doi=10.1109%2fJSTARS.2012.2206572&partnerID=40&md5=7e227476e99ea5e66d67999b196b6fb7","The Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) has been run more than five years, and one of its payloads-the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP)-can offer global atmospheric vertical profiles, resulting in it being widely used in aerosol research. Because the Lidar ratio is an important parameter for aerosol parameter retrieval and the choice of the Lidar ratio needs confirm the type of layers (aerosol or cloud), the data process cannot do without accurate and efficient aerosol and cloud classification. Initially, we found the classification errors existed in PDF version 1 data, so we introduce the SVM classifier to improve the accurate of thick dust and cloud classification. Despite NASA issued the PDF version 2 and improve the correct rate greatly, but we cannot ignore SVM advantages. Therefore in this paper, to continue the former study, we will not only validate the feasibility of this method (especially in dust source areas), but also make a thorough study of classification result effected by different feature vectors and samples. Through the accuracy testing we found, with the increase of the number of samples, SVM have the better result, and the feature space which include depolarization ratio have the more stable result. Though we validated the advantage of SVM, but if we want to use this algorithm in global scale or the whole process of dust deposition, there are still some improvements we need to do. To obtain the thinking of algorithm revision, we using MODIS, HYSPLIT, and CALIPSO products in a dust storm and observe its transmission, then the deficiencies of SVM can be found when compare with PDF version 2. Indeed PDF considered the regional variation, with this transmission of dust storm we need adjustment training samples and hyperplane of classifier. The diversity between SVM and PDF will be applied in the future research as the reference to make the new algorithm more robust and accurate. © 2008-2012 IEEE."
"55003524100;7003510377;","How much can a priori hydrologic model predictability help in optimal merging of satellite precipitation products?",2011,"10.1175/JHM-D-10-05023.1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84858243878&doi=10.1175%2fJHM-D-10-05023.1&partnerID=40&md5=38790e5cc418eafe0993bea462315f62","In this study, the authors ask the question: Can a more superior precipitation product be developed by merging individual products according to their a priori hydrologic predictability? The performance of three widely used high-resolution satellite precipitation products [Tropical Rainfall Measuring Mission (TRMM) real-time precipitation product 3B42 (3B42-RT), the NOAA/Climate Prediction Center morphing technique (CMORPH), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS)] was evaluated in terms streamflow predictability for the entire Mississippi River basin using the Variable Infiltration Capacity (VIC) macroscale hydrologic model. A merging concept that was not based on a single universal merging formula for the whole basin but rather used a ""localized"" (grid box by grid box) approach for merging precipitation products was then explored. In this merging technique, the a priori (historical) hydrologic predictive skill of each product for each grid box was first identified. Prior to streamflow routing, the corresponding accuracy of the spatially distributed simulations of soil moisture and runoff were used as proxy for weights in merging the precipitation products. It was found that the merged product derived on the basis of runoff predictability outperformed its counterpart merged product derived on the basis of soil moisture simulation. Results indicate that such a grid box by grid box merging concept that leverages a priori information on predictability of individual products has the potential to yield a more superior product for streamflow prediction than what the individual products can deliver for hydrologic prediction. © 2011 American Meteorological Society."
"26428367000;57213292861;7401522856;","A new algorithm for the extraction of cloud parameters using multipeak analysis of cloud radar data - First application and preliminary results",2008,"10.1127/0941-2948/2008/0322","https://www.scopus.com/inward/record.uri?eid=2-s2.0-63649136088&doi=10.1127%2f0941-2948%2f2008%2f0322&partnerID=40&md5=bbdb080451075234f53534778b2052c7","Spectral and polarimetric signatures of echoes, obtained with a vertically pointing millimeter wave radar, are examined with regard of their potential to retrieve range resolved microphysical characteristics of clouds, including the detection of mixed phases. A novel target classification algorithm based on this information was applied to deep frontal clouds. Examples of spectral/polarimetric profiles are shown to illustrate the retrieval technique, and first findings on microphysical structures and dynamics of such cloud systems are presented. © Gebrüder Borntraeger, Berlin, Stuttgart 2008."
"7201755709;7006183956;6601980113;","Determination of spatial and temporal characteristics as an aid to neural network cloud classification",1997,"10.1080/014311697218827","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0031106398&doi=10.1080%2f014311697218827&partnerID=40&md5=279da6a945faa60a8a47881241829a50","Previous studies of cloud classification from meteorological satellite imagery have shown that artificial neural networks (ANNs) perform as well as, or better than, statistical pattern recognition when multispectral features, supplemented with selected textural features, are used. These features, however, represent only a subset of the full range of features available in this imagery. Spatial characteristics based on the shape of clouds, and temporal characteristics, derived from image sequences, can be more direct pointers to cloud type. In this paper the methods for the determination of such parameters are described, some results are presented, and the effectiveness of the methods are discussed. © 1997 Taylor and Francis Group, LLC."
"7201472576;","Development of an operational cloud classification model",1989,"10.1080/01431168908903910","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0024476035&doi=10.1080%2f01431168908903910&partnerID=40&md5=0f9a2515ba8fc25c3255853efa5c4615","Within the PROgramme for a Meteorological Information System (PROMIS) project in Sweden a method of multi-spectral analysis and classification of Advanced Very High Resolution Radiometer (AVHRR) data is developed. The model will be based on a statistical database of object classes and ruled by information about prevailing Sun elevations and air mass temperatures. A new system provides the image processing and data handling capacity necessary for an operational classification of AVHRR data. Operational tests will start during the beginning of 1988. © 1989 Taylor & Francis Ltd."
"55805773500;6701339904;35779366300;55080097400;7202016984;26028515700;7006577245;35319507500;7501502356;8839842400;56239378700;7004378370;7004678728;7003865921;","Discriminating between clouds and aerosols in the CALIOP version 4.1 data products",2019,"10.5194/amt-12-703-2019","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061149607&doi=10.5194%2famt-12-703-2019&partnerID=40&md5=00902de25cc195d653cc00067310aec6","The Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Operations (CALIPSO) mission released version 4.1 (V4) of their lidar level 2 cloud and aerosol data products in November 2016. These new products were derived from the CALIPSO V4 lidar level 1 data, in which the calibration of the measured backscatter data at both 532 and 1064 nm was significantly improved. This paper describes updates to the V4 level 2 cloud-aerosol discrimination (CAD) algorithm that more accurately differentiate between clouds and aerosols throughout the Earth's atmosphere. The level 2 data products are improved with new CAD probability density functions (PDFs) that were developed to accommodate extensive calibration changes in the level 1 data. To enable more reliable identification of aerosol layers lofted into the upper troposphere and lower stratosphere, the CAD training dataset used in the earlier data releases was expanded to include stratospheric layers and representative examples of volcanic aerosol layers. The generic ""stratospheric layer"" classification reported in previous versions has been eliminated in V4, and cloud-aerosol classification is now performed on all layers detected everywhere from the surface to 30 km. Cloud-aerosol classification has been further extended to layers detected at single-shot resolution, which were previously classified by default as clouds. In this paper, we describe the underlying rationale used in constructing the V4 PDFs and assess the performance of the V4 CAD algorithm in the troposphere and stratosphere. Previous misclassifications of lofted dust and smoke in the troposphere have been largely improved, and volcanic aerosol layers and aerosol layers in the stratosphere are now being properly classified. CAD performance for single-shot layer detections is also evaluated. Most of the single-shot layers classified as aerosol occur within the dust belt, as may be expected. Due to changes in the 532 nm calibration coefficients, the V4 feature finder detects ∼9.0% more features at night and ∼2.5 % more during the day. These features are typically weakly scattering and classified about equally as clouds and aerosols. For those tropospheric layers detected in both V3 and V4, the CAD classifications of more than 95 % of all cloud and daytime aerosol layers remain unchanged, as do the classifications of ∼89 % of nighttime aerosol layers. Overall, the nighttime net cloud and aerosol fractions remain unchanged from V3 to V4, but the daytime net aerosol fraction is increased by about 2 % and the daytime net cloud fraction is decreased by about 2 %. © 2019 Author(s)."
"6504793116;55826210400;55705803300;8326989900;55855213300;7006960661;","Post-processing to remove residual clouds from aerosol optical depth retrieved using the Advanced Along Track Scanning Radiometer",2017,"10.5194/amt-10-491-2017","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85012873832&doi=10.5194%2famt-10-491-2017&partnerID=40&md5=5275b6b712837c39fd0b40411af33f10","Cloud misclassification is a serious problem in the retrieval of aerosol optical depth (AOD), which might considerably bias the AOD results. On the one hand, residual cloud contamination leads to AOD overestimation, whereas the removal of high-AOD pixels (due to their misclassification as clouds) leads to underestimation. To remove cloud-contaminated areas in AOD retrieved from reflectances measured with the (Advanced) Along Track Scanning Radiometers (ATSR-2 and AATSR), using the ATSR dual-view algorithm (ADV) over land or the ATSR single-view algorithm (ASV) over ocean, a cloud post-processing (CPP) scheme has been developed at the Finnish Meteorological Institute (FMI) as described in Kolmonen et al. (2016). The application of this scheme results in the removal of cloud-contaminated areas, providing spatially smoother AOD maps and favourable comparison with AOD obtained from the ground-based reference measurements from the AERONET sun photometer network. However, closer inspection shows that the CPP also removes areas with elevated AOD not due to cloud contamination, as shown in this paper. We present an improved CPP scheme which better discriminates between cloud-free and cloud-contaminated areas. The CPP thresholds have been further evaluated and adjusted according to the findings. The thresholds for the detection of high-AOD regions (&gt;ĝ€60ĝ€% of the retrieved pixels should be high-AOD (&gt;ĝ€0.6) pixels), and cloud contamination criteria for low-AOD regions have been accepted as the default for AOD global post-processing in the improved CPP. Retaining elevated AOD while effectively removing cloud-contaminated pixels affects the resulting global and regional mean AOD values as well as coverage. Effects of the CPP scheme on both spatial and temporal variation for the period 2002-2012 are discussed. With the improved CPP, the AOD coverage increases by 10-15ĝ€% with respect to the existing scheme. The validation versus AERONET shows an improvement of the correlation coefficient from 0.84 to 0.86 for the global data set for the period 2002-2012. The global aggregated AOD over land for the period 2003-2011 is 0.163 with the improved CPP compared to 0.144 with the existing scheme. The aggregated AOD over ocean and globally (land and ocean together) is 0.164 with the improved CPP scheme (compared to 0.152 and 0.150 with the existing scheme, for ocean and globally respectively). Effects of the improved CPP scheme on the 10-year time series are illustrated and seasonal and temporal changes are discussed. The improved CPP method introduced here is applicable to other aerosol retrieval algorithms. However, the thresholds for detecting the high-AOD regions, which were developed for AATSR, might have to be adjusted to the actual features of the instruments. © Author(s) 2017."
"55613105600;6701915334;56298802300;6701832491;7404297096;","New insight of Arctic cloud parameterization from regional climate model simulations, satellite-based, and drifting station data",2016,"10.1002/2015GL067530","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84978870264&doi=10.1002%2f2015GL067530&partnerID=40&md5=7484cea0cc31823f783b6ec043a25535","Cloud observations from the CloudSat and CALIPSO satellites helped to explain the reduced total cloud cover (Ctot) in the atmospheric regional climate model HIRHAM5 with modified cloud physics. Arctic climate conditions are found to be better reproduced with (1) a more efficient Bergeron-Findeisen process and (2) a more generalized subgrid-scale variability of total water content. As a result, the annual cycle of Ctot is improved over sea ice, associated with an almost 14% smaller area average than in the control simulation. The modified cloud scheme reduces the Ctot bias with respect to the satellite observations. Except for autumn, the cloud reduction over sea ice improves low-level temperature profiles compared to drifting station data. The HIRHAM5 sensitivity study highlights the need for improving accuracy of low-level (&lt;700 m) cloud observations, as these clouds exert a strong impact on the near-surface climate. ©2016. American Geophysical Union. All Rights Reserved."
"7005354212;22234180300;14014252200;55465604600;","Distinguishing aerosols from clouds in global, multispectral satellite data with automated cloud classification algorithms",2008,"10.1175/2007JTECHA1004.1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-45749142458&doi=10.1175%2f2007JTECHA1004.1&partnerID=40&md5=4cbb952c29628c5d0b83802d0e249265","A new approach is presented to distinguish between clouds and heavy aerosols with automated cloud classification algorithms developed for the National Polar-orbiting Operational Environmental Satellite System (NPOESS) program. These new procedures exploit differences in both spectral and textural signatures between clouds and aerosols to isolate pixels originally classified as cloudy by the Visible/Infrared Imager/ Radiometer Suite (VIIRS) cloud mask algorithm that in reality contains heavy aerosols. The procedures have been tested and found to accurately distinguish clouds from dust, smoke, volcanic ash, and industrial pollution over both land and ocean backgrounds in global datasets collected by NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. This new methodology relies strongly upon data collected in the 0.412-μm bandpass, where smoke has a maximum reflectance in the VIIRS bands while dust simultaneously has a minimum reflectance. The procedures benefit from the VIIRS design, which is dual gain in this band, to avoid saturation in cloudy conditions. These new procedures also exploit other information available from the VIIRS cloud mask algorithm in addition to cloud confidence, including the phase of each cloudy pixel, which is critical to identify water clouds and restrict the use of spectral tests that would misclassify ice clouds as heavy aerosols. Comparisons between results from these new procedures, automated cloud analyses from VIIRS heritage algorithms, manually generated analyses, and MODIS imagery show the effectiveness of the new procedures and suggest that it is feasible to identify and distinguish between clouds and heavy aerosols in a single cloud mask algorithm. © 2008 American Meteorological Society."
"6701752045;6603347622;","Changes in the amount of low clouds in Estonia (1955-1995)",2001,"10.1002/joc.618","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0035868625&doi=10.1002%2fjoc.618&partnerID=40&md5=c3dbae33151d0675ef855c80bcf63748","Based on data from 16 meteorological stations, trends in the amount of low clouds in Estonia have been investigated for the period 1955-1995. Analysis shows that the amount of low clouds has increased in March at all stations, in June at 12 stations and in September at eight stations. The amount of low clouds has decreased in May at seven stations and in October at five stations. Only trends that are significant at least the 0.1 level were taken into account. Regression analysis shows that the increase in the amount of low clouds in March can be ascribed to changes in the frequencies of occurrence of European circulation patterns and/or to an increase in the amount of low clouds within the zonal circulation. Copyright © 2001 Royal Meteorological Society."
"35509639400;56520921400;8661887700;","Indian Ocean low clouds during the winter monsoon",2000,"10.1175/1520-0442(2000)013<2028:IOLCDT>2.0.CO;2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0034209861&doi=10.1175%2f1520-0442%282000%29013%3c2028%3aIOLCDT%3e2.0.CO%3b2&partnerID=40&md5=f9b5067c48473cb96b26e209787b1dd5","While low-level clouds over the Pacific and Atlantic Oceans have been investigated extensively, low clouds over the Indian Ocean are not as well characterized. This study examines the occurrence of nonoverlapped low clouds over the Indian Ocean during the northeast monsoon using several sources of data. Climatologies derived from surface observations and from the International Satellite Cloud Climatology Project are reviewed. Another cloud climatology is developed using infrared and visible imagery from the Indian geostationary satellite. The new climatology has better spatial and temporal resolution than in situ observations. The three datasets are generally consistent and show several persistent features in the cloud distribution. During January-April, maxima in the occurrence of low clouds occur at subtropical latitudes over the Arabian Sea, the Bay of Bengal, the China Sea, and the southern Indian Ocean. The predominant types of low clouds differ in the northern and southern areas of the Indian Ocean region and China Sea. The Arabian Sea and the Bay of Bengal are covered mostly by cumulus clouds, while the southern Indian Ocean and the China Sea are covered mostly by large-scale stratiform clouds such as stratocumulus. These observations are consistent with atmospheric analysis of temperature, humidity, and stability over the Indian Ocean.While low-level clouds over the Pacific and Atlantic Oceans have been investigated extensively, low clouds over the Indian Ocean are not as well characterized. This study examines the occurrence of nonoverlapped low clouds over the Indian Ocean during the northeast monsoon using several sources of data. Climatologies derived from surface observations and from the International Satellite Cloud Climatology Project are reviewed. Another cloud climatology is developed using infrared and visible imagery from the Indian geostationary satellite. The new climatology has better spatial and temporal resolution than in situ observations. The three datasets are generally consistent and show several persistent features in the cloud distribution. During January-April, maxima in the occurrence of low clouds occur at subtropical latitudes over the Arabian Sea, the Bay of Bengal, the China Sea, and the southern Indian Ocean. The predominant types of low clouds differ in the northern and southern areas of the Indian Ocean region and China Sea. The Arabian Sea and the Bay of Bengal are covered mostly by cumulus clouds, while the southern Indian Ocean and the China Sea are covered mostly by large-scale stratiform clouds such as stratocumulus. These observations are consistent with atmospheric analyses of temperature, humidity, and stability over the Indian Ocean."
"36720598100;7003833060;","Are there real interdecadal variations in marine low clouds?",1998,"10.1175/1520-0442(1998)011<2910:ATRIVI>2.0.CO;2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0032214458&doi=10.1175%2f1520-0442%281998%29011%3c2910%3aATRIVI%3e2.0.CO%3b2&partnerID=40&md5=7b9eb32ea279ab8b441c43919491f573","The dominant interdecadal signal in normalized frequency of occurrence of cumulonimbus reported by volunteer observing ships is globally uniform over the period 1952-92 over all ocean areas between 40°S and 50°N. Globally uniform signals in both normalized frequency of occurrence and amount-when-present also dominate interdecadal variations for other low cloud types. This pattern is inconsistent with plausible physical mechanisms and is apparently due to slow changes in observational practice. Eight ocean weather ships with approximately fixed positions also reported gradual changes in low cloud occurrence frequencies between 1952 and 1969 that were similar in pattern for all eight ships, but for most cloud types these variation patterns differed markedly from those at nearly collocated volunteer observing ships. These apparently spurious variations make it difficult to identify real interdecadal variations in marine clouds from ship observations. However, over the tropical Indian Ocean and central and eastern Pacific Ocean, small but widespread decreases in stratocumulus frequency and increases in deep convective cloud frequency between 1955 and 1978, and 1979 and 1991 tend to be consistently related to changes in sea surface temperature and are likely to be real. Over the western Pacific Ocean, ship reports indicate an increase in the frequency of deep convective clouds between these two periods that is not consistently related to SST changes and is less likely to be real.The dominant interdecadal signal in normalized frequency of occurrence of cumulonimbus reported by volunteer observing ships is globally uniform over the period 1952-92 over all ocean areas between 40°S and 50°N. Globally uniform signals in both normalized frequency of occurrence and amount-when-present also dominate interdecadal variations for other low cloud types. This pattern is inconsistent with plausible physical mechanisms and is apparently due to slow changes in observational practice. Eight ocean weather ships with approximately fixed positions also reported gradual changes in low cloud occurrence frequencies between 1952 and 1969 that were similar in pattern for all eight ships, but for most cloud types these variation patterns differed markedly from those at nearly collocated volunteer observing ships. These apparently spurious variations make it difficult to identify real interdecadal variations in marine clouds from ship observations. However, over the tropical Indian Ocean and central and eastern Pacific Ocean, small but widespread decreases in stratocumulus frequency and increases in deep convective cloud frequency between 1955 and 1978, arid 1979 and 1991 tend to be consistently related to changes in sea surface temperature and are likely to be real. Over the western Pacific Ocean, ship reports indicate an increase in the frequency of deep convective clouds between these two periods that is not consistently related to SST changes and is less likely to be real."
"57190005529;57204971082;15051249600;55092505400;57091796400;56350627000;56822458600;57192636079;23494267100;","Multispectral LiDAR point cloud classification: A two-step approach",2017,"10.3390/rs9040373","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85017650239&doi=10.3390%2frs9040373&partnerID=40&md5=59dfdf29e9448da0d47b059c6c014e79","Target classification techniques using spectral imagery and light detection and ranging (LiDAR) are widely used in many disciplines. However, none of the existing methods can directly capture spectral and 3D spatial information simultaneously. Multispectral LiDAR was proposed to solve this problem as its data combines spectral and 3D spatial information. Point-based classification experiments have been conducted with the use of multispectral LiDAR; however, the low signal to noise ratio creates salt and pepper noise in the spectral-only classification, thus lowering overall classification accuracy. In our study, a two-step classification approach is proposed to eliminate this noise during target classification: routine classification based on spectral information using spectral reflectance or a vegetation index, followed by neighborhood spatial reclassification. In an experiment, a point cloud was first classified with a routine classifier using spectral information and then reclassified with the k-nearest neighbors (k-NN) algorithm using neighborhood spatial information. Next, a vegetation index (VI) was introduced for the classification of healthy and withered leaves. Experimental results show that our proposed two-step classification method is feasible if the first spectral classification accuracy is reasonable. After the reclassification based on the k-NN algorithm was combined with neighborhood spatial information, accuracies increased by 1.50-11.06%. Regarding identification of withered leaves, VI performed much better than raw spectral reflectance, with producer accuracy increasing from 23.272% to 70.507%. © 2017 by the authors."
"55871853200;7402866430;57207721059;","Structural evolution of monsoon clouds in the Indian CTCZ",2013,"10.1002/grl.50970","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84884951769&doi=10.1002%2fgrl.50970&partnerID=40&md5=c9246333e79d644d731707f6fa5f3d75","Structural evolution of monsoon clouds in the core monsoon region of India has been examined using multisensor data. Invigoration of warm clouds above 4.5 km (occurring in only 15.4% days of the last 11 monsoon seasons) is associated with a transition from negative to positive normalized rainfall anomaly. Cloud top pressure reduces with an increase in aerosol optical depth at a higher rate of invigoration in drier condition (characterized by large fraction of absorbing aerosols) than wet condition. Cloud effective radius for warm clouds does not show any significant change with an increase in aerosol concentration in the presence of high liquid water path, probably due to strong buffering role of meteorology. The structural evolution of monsoon clouds is influenced by both dynamic and microphysical processes that prolong the cloud lifetime, resulting in infrequent rainfall. Our results call for improved representation of aerosol and cloud vertical structures in the climate models to resolve this issue. Key Points Transition to positive rainfall anomaly for cloud invigoration above 4.5 km Higher rate of invigoration in response to aerosols in drier condition At high liquid water path, cloud effective radius is insensitive to aerosols ©2013. American Geophysical Union. All Rights Reserved."
"56267759500;55495868700;55804452100;24833754500;","Comparison of cloud properties from ground-based infrared cloud measurement and visual observations",2013,"10.1175/JTECH-D-12-00157.1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84880726521&doi=10.1175%2fJTECH-D-12-00157.1&partnerID=40&md5=f4db668e2d569837b8ea361780c937c6","Cloud properties derived from the whole-sky infrared cloud-measuring system (WSIRCMS) are analyzed in relation to measurements of visual observations and a ceilometer during the period July-August 2010 at the Chinese Meteorological Administration Yangjiang Station, Guangdong Province, China. The comparison focuses on the performance and features of the WSIRCMS as a prototype instrument for automatic cloud observations. Cloud cover derived from the WSIRCMS cloud algorithm compares quite well with cloud cover derived from visual observations. Cloud cover differences between WSIRCMS and visual observations are within ±1 octa in 70.83% and within 62 octa in ±2.44% of the cases. For cloud-base height from WSIRCMS data and Vaisala ceilometer CL51, the comparison shows a generally good correspondence in the lower and midtroposphere up to the height of about 6 km, with some systematic difference due to different detection methods. Differences between the resulting cloud-type classifications derived from the WSIRCMS and from visual observations show that cumulus and cirrus are classified with high accuracy, but that stratocumulus and altocumulus are not. Stratocumulus and altocumulus are suggested to be treated as waveform cloud for classification purposes. In addition, it is considered an intractable problem for automatic cloud-measurement instruments to do cloud classification when the cloud amount is less than 2 octa. © 2013 American Meteorological Society."
"14063370300;57192604786;56222085000;","A Bayesian-Network-Based Classification Method Integrating Airborne LiDAR Data with Optical Images",2017,"10.1109/JSTARS.2016.2628775","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85006957575&doi=10.1109%2fJSTARS.2016.2628775&partnerID=40&md5=b8efedd46928cede635b2452d2a2863f","Point cloud classification is of great importance to applications of airborne Light Detection And Ranging (LiDAR) data. In recent years, airborne LiDAR has been integrated with various other sensors, e.g., optical imaging sensors, and thus, the fusion of multiple data types for scene classification has become a hot topic. Therefore, this paper proposes a Bayesian network (BN) model that is suitable for airborne point cloud classification fusing multiple data types. Based on an analysis of the characteristics of LiDAR point clouds and aerial images, we first extract the geometric features from the point clouds and the spectral features from the optical images. The optimal BN structure is then trained using an improved mutual-information-based K2 algorithm to obtain the optimal BN classifier for point cloud classification. Experiments demonstrate that the BN classifier can effectively distinguish four types of basic ground objects, including ground, vegetation, trees, and buildings, with a high accuracy of over 90%. Moreover, compared with other classifiers, the proposed BN classifier can achieve the highest overall accuracies, and in particular, the classifier demonstrates its advantage in the classification of ground and low vegetation points. © 2008-2012 IEEE."
"7403045983;56420772600;57191953810;","Multiple Kernel Sparse Representation for Airborne LiDAR Data Classification",2017,"10.1109/TGRS.2016.2619384","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84995514061&doi=10.1109%2fTGRS.2016.2619384&partnerID=40&md5=4a159b4ddef2423da63d7c910d94c4c6","To effectively learn heterogeneous features extracted from raw LiDAR point cloud data for landcover classification, a multiple kernel sparse representation classification (MKSRC) framework is proposed in this paper. In the MKSRC, multiple kernel learning (MKL) is embedded into sparse representation classification (SRC). The heterogeneous features are first extracted from the raw LiDAR point cloud data before classification. These features contain useful information from different dimensions, including single point features and neighbor features. Based on feature extraction, on the one hand, MKL is reasonably integrated into the SRC, namely, different base kernels that are constructed with each heterogeneous feature separately are utilized in the process of sparse representation. Furthermore, joint sparsity model is also introduced into the MKSRC framework and multiple kernel joint SRC (MKJSRC) is then proposed. On the other hand, improved kernel alignment (IKA) methods are proposed to more effectively determine the weights of base kernels in both of MKSRC and MKJSRC. Experiments are conducted on three real airborne LiDAR data sets. The experimental results demonstrate that MKSRC and MKJSRC frameworks can effectively learn the heterogeneous features for LiDAR point cloud classification and outperforms the other state-of-the-art sparse representation-based classifiers and the recent MKL algorithm. Moreover, the proposed IKA is helpful to better determine the 'optimal' weights of the base kernels in both MKSRC and MKJSRC than in the existing kernel alignment method. © 1980-2012 IEEE."
"26533302300;56213274700;24467861000;6506555110;52664397200;56953593400;55382330300;55382486900;55235428500;7006802109;","Assessing the quality of a real-time Snow Cover Area product for hydrological applications",2012,"10.1016/j.rse.2012.09.006","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84867206169&doi=10.1016%2fj.rse.2012.09.006&partnerID=40&md5=56687c03840ae0082f93c8c04f575242","Using reliable observed data is important for performing real-time flood forecasts or hydrological simulation in order to calibrate parameters or to update model variables. Satellite snow products can be one of these data, since snow is a water reservoir with a high impact on the quality of discharge simulation. The satellite Snow Cover Area products are known to be of good quality and are regularly used for studies in the meteorological and hydrological fields. However, these products are reprocessed and thus are not representative of the quality of a real-time product that would be needed for operational applications. The assessment of a real-time Snow Cover Area daily product at 250. m-resolution (the EURAC MODIS SCA product, which is based on the MODIS sensor) by comparing it directly or indirectly to the classical NASA MODIS SCA daily product and to the simulated Snow Cover Area of the distributed hydrological model, LISFLOOD, is realized in this article at the pan-European scale. This real-time product showed an overall good performance compared with the classical product, and a good agreement with the LISFLOOD simulated snow over Europe. The study showed the impact of forest cover on the scores of the compared products, whereas altitude did not have an impact. Using quality flags that are provided with the EURAC product improved its performance by reducing the misclassification of clouds as snow. © 2012 Elsevier Inc."
"7103373860;7005877775;","Cloud classes and radiative heating profiles at the Manus and Nauru atmospheric radiation measurement (ARM) sites",2009,"10.1029/2009JDO11703","https://www.scopus.com/inward/record.uri?eid=2-s2.0-72049092702&doi=10.1029%2f2009JDO11703&partnerID=40&md5=29d85f81c9b905b858f335a4e5d4a324","The tropical western Pacific is a convective regime; however, the frequency and depth of convection is dependent on dynamical forcing which exhibits variability on a range of temporal scales and also on location within the region. Manus Island, Papua New Guinea, lies in the heart of the western Pacific warm pool region and exhibits frequent deep convection much of the time, while Nauru, which lies approximately 20 degrees to the east of Manus, is in a transition zone where the frequency of convection is dependent on the phase of the El Niño-Southern Oscillation. Because of this difference in dynamical regime, the distribution of clouds and the associated radiative heating is quite different at the two sites. Individual cloud types (boundary layer cumulus, thin cirrus, stratiform convective outflow) do occur at both sites, but with different frequencies. In this study we compare cloud profiles and heating profiles for specific cloud types at these two sites using data from the Atmospheric Radiation Measurement (ARM) Climate Research Facility. Results of this comparison indicate that while the frequency of specific cloud types differ between the two sites as one would expect, the characteristics of individual cloud classes are remarkably similar. This information could prove to be very useful for applying tropical ARM data to the broader region. Copyright 2009 bv the American Geophysical Union."
"26032130600;7003466102;55722577700;","Cloud distributions over the coastal Arctic Ocean: Surface-based and satellite observations",2004,"10.1016/j.atmosres.2004.03.029","https://www.scopus.com/inward/record.uri?eid=2-s2.0-8644268048&doi=10.1016%2fj.atmosres.2004.03.029&partnerID=40&md5=6098f394748e218dec0898148760a5e5","All-weather Arctic cloud analyses primarily derived from a surface-based hemispheric all-sky imager are compared against ISCCP D-1 cloud amount, type, and phase during the sunlit polar season. Increasing surface temperatures and decreasing ice cover over the past decade have altered heat and moisture fluxes around the Arctic, providing conditions more conducive for cloud generation. Shipboard and ice camp measurements from field experiments conducted over an 8-year period show cloudy skies in 70-95% of the record. Most of these occurrences are stratiform or multi-level, multi-form cloud, increasing in amount with time through the season. Collocated ISCCP retrievals underestimate cloud amount at small solar zenith angles and overestimate at large angles, sometimes by as much as 50%. Satellite assessments of cloud form classify 95% of scenes as having multiple cloud types, the majority of which are mid-level ice cloud and low-level liquid cloud. Despite large discrepancies in diurnal cloud amount, regional averages of ISCCP pixel cloudiness over the length of the experiments agree within ±5% of surface observations. © 2004 Elsevier B.V. All rights reserved."
"6506942596;7102018821;","Detection of thin cirrus using a combination of 1.38-μm reflectance and window brightness temperature difference",2003,"10.1029/2002jd003346","https://www.scopus.com/inward/record.uri?eid=2-s2.0-1342289431&doi=10.1029%2f2002jd003346&partnerID=40&md5=1f54407000318c0929921cb55721817b","A new cloud-detection scheme has been developed that utilizes 1.38-μm reflectance in combination with 8.6-11 μm brightness temperature difference (BTD8.6-11) to detect thin cirrus clouds. The 1.38-μm channel of the moderate resolution imaging spectroradiometer (MODIS) is useful in detecting thin cirrus due to its high sensitivity to upper tropospheric clouds and a nearly negligible sensitivity to low-level reflectance. Dependent upon neighboring cloud type, water vapor concentration, and the viewing geometry, specific 1.38-μm reflectance threshold levels can be utilized to detect thin cirrus that has previously been undetectable by downward looking satellite imagery. BTD8.6-11 is also sensitive to ice clouds and is used as a second, independent, cirrus cloud test. Each test can either support or negate results from the other. Final cloud type results are produced by using cirrus detected by either the 1.38-μm reflectance test or the BTD6.8-11 test or by using only that detected by both tests depending on whether a sizable amount of the neighboring cloud is opaque or not as determined by a simple visible reflectance test. It is found that 1.38-μm reflectance can often detect a greater amount of thin cirrus than the BTD8.6-11. Satellite data from 10 MODIS cases over the atmospheric radiation measurement-tropical western Pacific and southern Great Plains sites were chosen because they provide land and ocean surface cases, variation in cloud type to test the algorithms reliability, and ground truth in the form of millimeter-wave radar data. Two-dimensional horizontal cloud type detection results are shown to correlate well with the 1-hour cloud radar reflectivity time series centered at the MODIS overpass time. Statistics indicate that the new algorithm more accurately identifies thin cirrus in cases involving only single-layer cirrus and where thin cirrus overrides low cloud. © 2003 by the American Geophysical Union."
"6506887943;7003364414;7006211890;7101764967;7005067383;","Statistical analysis of cloud light scattering and microphysical properties obtained from airborne measurements",2003,"10.1029/2002jd002723","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0346639293&doi=10.1029%2f2002jd002723&partnerID=40&md5=bf06b6bcf6ac483c369bd26c28e20baa","A new statistical analysis of the in situ scattering phase function measurements performed by the Laboratoire de Météorologie Physique's airborne polar nephelometer is implemented. A principal component analysis along with neural networks leads to the classification of a large data set into three typical averaged scattering phase functions. The cloud classification in terms of particle phase composition (water droplets, mixed-phase, and ice crystals) is done by a neural network and is validated by direct Particle Measuring Systems, Inc., probe measurements. The results show that the measured scattering phase functions carry enough information to accurately retrieve component composition and particle size distributions. For each classified cloud, we support the statement by application of an inversion method using a physical model of light scattering to the average scattering phase function. Furthermore, the retrievals are compared with size composition obtained by independent direct measurements."
"6603164038;7102278892;7003604969;7003558261;7201914101;7201826462;6505929959;6603421020;","A comparison of paired histogram, maximum likelihood, class elimination, and neural network approaches for daylight global cloud classification using AVHRR imagery",1999,"10.1029/98JD02584","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0033608662&doi=10.1029%2f98JD02584&partnerID=40&md5=6a9bee917ec65b42a210fc72976b0f45","The accuracy and efficiency of four approaches to identifying clouds and aerosols in remote sensing imagery are compared. These approaches are as follows: a maximum likelihood classifier, a paired histogram technique, a hybrid class elimination approach, and a back-propagation neural network. Regional comparisons were conducted on advanced very high resolution radiometer (AVHRR) local area coverage (LAC) scenes from the polar regions, desert areas, and regions of biomass-burning, areas which are known to be particularly difficult. For the polar, desert, and biomass burning regions, the maximum likelihood classifier achieved 94-97% accuracy, the neural network achieved 95-96% accuracy, and the paired histogram approach achieved 93-94% accuracy. The primary advantage to the class elimination scheme lies in its speed; its accuracy of 94-96% is comparable to that of the maximum likelihood classifier. Experiments also clearly demonstrate the effectiveness of decomposing a single global classifier into separate regional classifiers, since the regional classifiers can be more finely tuned to recognize local conditions. In addition, the effectiveness of using composite features is compared to the simpler approach of using the five AVHRR channels and the reflectance of channel 3 treated as a sixth channel as the elements of the feature vector. The results varied, demonstrating that the features cannot be chosen independently of the classifier to be used. It is also shown that superior results can obtained by training the classifiers using subclass information and collapsing the subclasses after classification. Finally, ancillary data were incorporated into the classifiers, consisting of a land/water mask, a terrain map, and a computed sunglint probability. While the neural network did not benefit from this information, the accuracy of the maximum likelihood classifier improved by 1%, and the accuracy of the paired histogram method increased by up to 4%. Copyright 1999 by the American Geophysical Union."
"6602544698;7006203051;","Seasonal variability of shallow cumuliform snowfall: A CloudSat perspective",2018,"10.1002/qj.3222","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85041023603&doi=10.1002%2fqj.3222&partnerID=40&md5=cfa2b4185b865f5aba1aa1dde54c7e81","Cumuliform snowfall seasonal variability is studied using a multi-year CloudSat snowfall rate and cloud classification retrieval dataset. Microwave radiometer sea ice concentration datasets are also utilized to illustrate the intimate link between oceanic cumuliform snowfall production and decreased sea ice coverage. Three metrics are calculated to illustrate seasonal cumuliform snowfall signatures: (a) cumuliform snowfall frequency of occurrence, (b) mean cumuliform snowfall rate, and (c) fraction of snowfall attributed to cumuliform snowfall events. Distinct seasonal cumuliform snowfall cycles are observed over the Northern Hemispheric oceans. Cumuliform snowfall frequency of occurrence (mean snowfall rate) peaks in months SON (DJF) at most latitudes. Maximum mean cumuliform snowfall rates exceed 300 mm/year in various North Atlantic Ocean locations, with DJF exhibiting the largest areal extent of higher snowfall rates. Cumuliform snow occurrence fraction frequently exceeds 0.5, but regional seasonal sensitivity is observed where transient sea ice coverage exists. Annual snowfall rate fraction attributed to cumuliform snow does not vary appreciably during SON, DJF and MAM north of ∼70°N, but seasonal zonal variability is evident south of this latitudinal threshold. Land cumuliform snowfall features do not universally display strong seasonal signals. Southern Hemisphere seasonal results indicate a strong mean cumuliform snowfall rate maximum (minimum) in JJA (DJF) accompanied by a seasonal latitudinal shift in the snowfall rate peak. Maximum regional snowfall rates exceed 300 mm/year over a broader area compared to the Northern Hemisphere. Cumuliform snowfall production is again strongly linked to seasonal sea ice coverage. Southern Hemispheric cumuliform snowfall occurrence and snowfall rate fraction seasonality is not as obvious as in the Northern Hemisphere, but some latitudinal zones experience ∼5–20% seasonal variability in these quantities. Typical cumuliform snowfall fractions range from 0.4 to 0.6 in the prominent cumuliform snowfall belt covering most of the Southern Ocean. Some subtle seasonal cumuliform snowfall signatures are observed over Antarctica. © 2018 Royal Meteorological Society"
"6505813584;","Catenary system detection, localization and classification using mobile scanning data",2016,"10.3390/rs8100801","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85019711778&doi=10.3390%2frs8100801&partnerID=40&md5=411e4dc7013b7448554ace3b39e6ddf0","This paper presents a new method for detecting, locating and classifying overhead contact systems (catenary systems) in point clouds collected by mobile mapping systems (MMS) on rail roads. Contrary to many other application types, railway embankments are highly regulated and standardized. Railway infrastructure geometric relations remain roughly unchanged within established regions and have similarities between them. The newly-developed method exploits both these characteristics, as well as the survey process. There are several steps in this approach. Firstly, it restricts the search for catenaries relative to the distance to registered MMS trajectory, then finds possible support structures according to the density of points above the track. Subsequently, the method verifies the structures' presence and classifies the points with the use of the RANSAC algorithm. It establishes the presence of cantilevers, as well as poles or structural beams, depending on the type of detected support structure. The method also determines the coordinates of the identified object on the ground. Finally, a classification is clarified with the use of a modified DBSCAN algorithm. The design method has been verified with data collected in four surveys where the cumulative length of the route was almost 90 km. Over 97% of support structures were correctly detected, and out of these, over 95% were completely classified. © 2016 by the authors."
"36945003900;7004384155;23017945100;","Ice water content vertical profiles of high-level clouds: Classification and impact on radiative fluxes",2015,"10.5194/acp-15-12327-2015","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84946761267&doi=10.5194%2facp-15-12327-2015&partnerID=40&md5=a9b6fc11373868d2fcf35e1497c5643e","In this article, we discuss the shape of ice water content (IWC) vertical profiles in high ice clouds and its effect on their radiative properties, both in short- and in long-wave bands (SW and LW). Based on the analysis of collocated satellite data, we propose a minimal set of primitive shapes (rectangular, isosceles trapezoid, lower and upper triangle), which represents the IWC profiles sufficiently well. About 75 % of all high-level ice clouds (P &lt; 440 hPa) have an ice water path (IWP) smaller than 100 g m-2, with a 10 % smaller contribution from single layer clouds. Most IWC profiles (80 %) can be represented by a rectangular or isosceles trapezoid shape. However, with increasing IWP, the number of lower triangle profiles (IWC rises towards cloud base) increases, reaching up to 40 % for IWP values greater than 300 g m-2. The number of upper triangle profiles (IWC rises towards cloud top) is in general small and decreases with IWP, with the maximum occurrence of 15 % in cases of IWP less than 10 g m-2. We propose a statistical classification of the IWC shapes using IWP as a single parameter. We have estimated the radiative effects of clouds with the same IWP and with different IWC profile shapes for five typical atmospheric scenarios and over a broad range of IWP, cloud height, cloud vertical extent, and effective ice crystal diameter (De). We explain changes in outgoing LW fluxes at the top of the atmosphere (TOA) by the cloud thermal radiance while differences in TOA SW fluxes relate to the De vertical profile within the cloud. Absolute differences in net TOA and surface fluxes associated with these parameterized IWC profiles instead of assuming constant IWC profiles are in general of the order of 1-2 W m-2: they are negligible for clouds with IWP &lt; 30 g m-2, but may reach 2 W m-2 for clouds with IWP &gt; 300 W m-2. © Author(s) 2015."
"20434940900;8629713500;56037559900;7201920350;7401796996;57191636820;57191636379;","Comparison of atmospheric profiles between microwave radiometer retrievals and radiosonde soundings",2015,"10.1002/2015JD023438","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84976426133&doi=10.1002%2f2015JD023438&partnerID=40&md5=aba7b552505de1b6c6d1bff428cbddf5","Atmospheric profiles of temperature (T), vapor density (ρv), and relative humidity (RH) retrieved from ground-based microwave radiometer (MWR) measurements are compared with radiosonde soundings at Wuhan, China. The MWR retrievals were averaged in the ±30 min period centered at sounding times of 00 and 12 UTC. A total of 403 and 760 profiles under clear and cloudy skies were selected. Based on the comparisons, temperature profiles have better consistency than the ρv and RH profiles, lower levels are better than upper levels, and the cloudy are better than the clear-sky profiles. Three cloud types (low, middle, and high) were identified by matching the infrared radiation thermometer-detected cloud base temperature to the MWR-retrieved temperature-height profiles. Temperature profile under high cloud has the highest correlation coefficient (R) and the lowest bias and RMS, but under low cloud is in the opposite direction. The ρv profile under middle cloud has the highest R and the lowest bias but under high cloud has the lowest R, the largest bias, and RMS. Based on the radiosonde soundings, both clear and cloudy wind speeds and drifting distances increase with height but increase much faster under clear than cloudy above 4 km. The increased wind speeds and drifting distances with height have resulted in decreased correlation coefficient and increased temperature biases and RMSs with height for both clear and cloudy skies. The differences in R, bias, and RMS between clear and cloudy skies are primarily resulted from their wind speeds and drifting distances. © 2015. American Geophysical Union. All Rights Reserved."
"55972035800;55545672000;55613230864;24479005300;","Classification of airborne laser scanning data using JointBoost",2014,"10.1016/j.isprsjprs.2014.03.004","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84897983621&doi=10.1016%2fj.isprsjprs.2014.03.004&partnerID=40&md5=b3215c9bb6db2d6a4fabe50f43780f53","The demands for automatic point cloud classification have dramatically increased with the wide-spread use of airborne LiDAR. Existing research has mainly concentrated on a few dominant objects such as terrain, buildings, and vegetation. In addition to those key objects, this paper proposes a supervised classification method to identify other types of objects including power-lines and pylons from point clouds using a JointBoost classifier. The parameters for the learning model are estimated with various features computed based on the geometry and echo information of a LiDAR point cloud. In order to overcome the shortcomings stemming from the inclusion of bare ground data before classification, the proposed classifier directly distinguishes terrain using a feature step-off count. Feature selection is conducted using JointBoost to evaluate feature correlations thus improving both classification accuracy and operational efficiency. In this paper, the contextual constraints for objects extracted by graph-cut segmentation are used to optimize the initial classification results obtained by the JointBoost classifier. Our experimental results show that the step-off count significantly contributes to classification. Seventeen effective features are selected for the initial classification results using the JointBoost classifier. Our experiments indicate that the proposed features and method are effective for classification of airborne LiDAR data from complex scenarios. © 2014 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS)."
"41763136800;55386235300;55969140000;35219644000;","On the sensitivity of Tropical Rainfall Measuring Mission (TRMM) Microwave Imager channels to overland rainfall",2011,"10.1029/2010JD015345","https://www.scopus.com/inward/record.uri?eid=2-s2.0-79959669885&doi=10.1029%2f2010JD015345&partnerID=40&md5=a5ba6d353eb8d1cd92a41e0c4cf15082","The response of brightness temperatures at different microwave frequencies to overland precipitation is investigated by using the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) and Microwave Imager (TMI) data. The Spearman correlation coefficients between observations at TMI channels or channel combinations and PR-measured near-surface rain are computed using 3 years of TRMM data. The results showed that the brightness temperature combinations from 19 and 37 GHz, that is, V19-V37 (the letter V denotes vertical polarization, and the numbers denote frequency in GHz) or V21-V37, can explain ∼10% more variance of near-surface rainfall rate than can the V85 brightness temperature. Also, the global distribution of the above correlation revealed that over almost all of the tropical land area covered by TRMM satellite, the V19-V37 channel has a closer response to the overland rainfall than does the V85 channel. This result is somewhat counterintuitive, because it has been long believed that the dominant signature of overland rainfall is the brightness temperature depression caused by ice scattering at high microwave frequencies (e.g., 85 GHz). To understand the underlying physics of this better low-frequency response, data analysis and radiative transfer modeling have been conducted to assess the influence on brightness temperatures from clouds with different ice and liquid water partitions. The results showed that under the condition of low frozen water and medium liquid water in the atmospheric column, the signal from the V19-V37 channel responded better to rainfall rate than did the one from the V85 channel. A plausible explanation to this result is that in addition to ice scattering signature, the V19-V37 channel contains liquid water information as well, which is more directly related to surface rain than to ice water aloft. At heavy rainfall conditions, the V19-V37, V37, and V85 channels all are correlated with near-surface rain reasonably well, and the V37 or V21 channel becomes the top responder to surface rain as the amount of hydrometeors in the atmospheric column reaches very high values. Additionally, it is found that land surface type and 2 m air temperature have significant skills in characterizing rain cloud types, so that the V19-V37 channel is more sensitive to surface rainfall for more vegetated warm surface, while the V85 channel is more sensitive to cold bare land. This finding implies that the above two parameters may be used to prioritize satellite observations at different channels, so that the channel that has the best rainfall sensitivity under a given condition receives the highest weight in retrieval algorithms. Copyright 2011 by the American Geophysical Union."
"6602636344;7003644931;7006499081;","Use of Meteosat imagery to define clouds linked with floods in Greece",2000,"10.1080/014311600210425","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0034689672&doi=10.1080%2f014311600210425&partnerID=40&md5=5c9d72e07050a209975630dfcfe25dc3","A cloud classification scheme has been developed with the aim of defining and monitoring clouds cells associated with heavy rain. In the first stage of the scheme, Meteosat images in the visible were corrected to account for varying illumination times and angles. In the second stage, analysis of Meteosat images in the visible, infrared and water vapour channels resulted in the assignment of spectral signatures to seven categories of cloud class. The analysis was supported by temperature and humidity profiles from radiosondes in the wider geographical area as well as by synoptic maps of the area. Finally, Meteosat images reflecting two flood incidents which occurred in Greece on 21 October 1994 and 12 January 1997 were classified on the basis of the defined cloud categories; cloud cells associated with heavy rain were clearly depicted on the classified images through the category of thick opaque convective clouds. © 2000 Taylor & Francis Group, LLC."
"6506500643;7006366653;","Solar radiation climatology of Alaska",1998,"10.1007/s007040050061","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0032440712&doi=10.1007%2fs007040050061&partnerID=40&md5=be95f10d993295410c7ecc8ceb299439","There are only six locations in Alaska for which global radiation data of more than a year in duration are available. This is an extremely sparse coverage for a state which covers 1.5 x 106km2 and stretches over at least three climatic zones. Cloud observations are, however, available from 18 stations. We used fractional cloud cover and cloud type data to model the global radiation and thus obtain a more complete radiation coverage for Alaska. This extended data set allowed an analysis of geographic and seasonal trends. A simple 1-layer model based on Haurwitz's semi-empirical approach, allowing for changes in cloud type and fractional coverage, was developed. The model predicts the annual global radiation fluxes to within 2-11% of the observed values. Estimated monthly mean values gave an average accuracy within about 6% of the measurements. The estimates agree well with the observations during the first four months of the year but less so for the last four. Changing surface albedo might explain this deviation. Previously, the 1993 National Solar Radiation Data Base (NSRDB) from the National Renewable Energy Laboratory (NREL) modeled global radiation data for 16 Alaskan stations. Although more complete and complex, the NREL model requires a larger number of input parameters, which are not available for Alaska. Hence, we believe that our model, which is based on cloud-radiation relationship and is specifically tuned to Alaskan conditions, produces better results for this region. Annual global solar radiation flux measurements are compared with results from global coverage models based on the International Satellite Cloud Climatology Project (ISCCP) data. Contour plots of seasonal and mean annual spatial distribution of global radiation for Alaska are presented and discussed in the context of their climatic and geographic settings."
"7201472576;","Cloud climate investigations in the Nordic region using NOAA AVHRR data",1997,"10.1007/BF00863612","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0031431636&doi=10.1007%2fBF00863612&partnerID=40&md5=e9cc3a3760df95e095524769bc35e86c","A method to estimate monthly cloud conditions (monthly cloud frequencies) from multispectral satellite imagery is described. The operational cloud classification scheme SCANDIA (the SMHI Cloud ANalysis model using Digital AVHRR data), based on high resolution imagery from the polar orbiting NOAA-satellites, has been used to produce monthly cloud frequencies for the entire year of 1993 and some additional months in 1991, 1992, 1994 and 1995. Cloud analyses were made for an area covering the Nordic countries with a horizontal resolution of four km. Examples of seasonal, monthly and diurnal variation in cloud conditions are given and an annual mean for 1993 is presented. Comparisons with existing surface observations showed very good agreement for horizontal cloud distributions but approximately 5% smaller cloud amounts were found in the satellite estimations. The most evident problems were encountered in the winter season due to difficulties in identifying low-level cloudiness at very low sun elevations. The underestimation in the summer season was partly fictious and caused by the overestimation of convective cloud cover by surface observers. SCANDIA results were compared to ISCCP (International Satellite Cloud Climatology Project) cloud climatologies for two selected months in 1991 and 1992. ISCCP cloudiness was indicated to be higher, especially during the month with anticyclonic conditions where a cloudiness excess of more than 10% were found. The regional variation of cloud conditions in the area was found to be inadequately described by ISCCP cloud climatologies. An improvement of the horizontal resolution of ISCCP data seems necessary to enable use for regional applications. SCANDIA model is proposed as a valuable tool for local and regional monitoring of the cloud climatology at high latitudes. More extensive comparisons with ISCCP cloud climatologies are suggested as well as comparisons with modelled cloudiness from atmospheric general circulation models and climate models. Special studies of cloud conditions in the Polar areas are also proposed."
"57193951496;7401526171;7005052907;35975568000;","Bias adjustment of infrared-based rainfall estimation using Passive Microwave satellite rainfall data",2017,"10.1002/2016JD026037","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85017573021&doi=10.1002%2f2016JD026037&partnerID=40&md5=c97afae80c154c67c5854ab853740824","This study explores using Passive Microwave (PMW) rainfall estimation for spatial and temporal adjustment of Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System(PERSIANN-CCS). The PERSIANN-CCS algorithm collects information from infrared images to estimate rainfall. PERSIANN-CCS is one of the algorithms used in the IntegratedMultisatellite Retrievals for GPM (Global Precipitation Mission) estimation for the time period PMW rainfall estimations are limited or not available. Continued improvement of PERSIANN-CCS will support Integrated Multisatellite Retrievals for GPM for current as well as retrospective estimations of global precipitation. This study takes advantage of the high spatial and temporal resolution of GEO-based PERSIANN-CCS estimation and the more effective, but lower sample frequency, PMW estimation. The Probability Matching Method (PMM) was used to adjust the rainfall distribution of GEO-based PERSIANN-CCS toward that of PMW rainfall estimation. The results show that a significant improvement of global PERSIANN-CCS rainfall estimation is obtained. © 2017. American Geophysical Union. All Rights Reserved."
"55502994400;6602390932;56175387100;37078009200;56204562000;","Absolute calibration of the colour index and O4 absorption derived from Multi AXis (MAX-)DOAS measurements and their application to a standardised cloud classification algorithm",2016,"10.5194/amt-9-4803-2016","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84990963992&doi=10.5194%2famt-9-4803-2016&partnerID=40&md5=ef75c661646872c4f7a39c59a62136f5","A method is developed for the calibration of the colour index (CI) and the O4 absorption derived from differential optical absorption spectroscopy (DOAS) measurements of scattered sunlight. The method is based on the comparison of measurements and radiative transfer simulations for well-defined atmospheric conditions and viewing geometries. Calibrated measurements of the CI and the O4 absorption are important for the detection and classification of clouds from MAX-DOAS observations. Such information is needed for the identification and correction of the cloud influence on Multi AXis (MAX-)DOAS profile inversion results, but might be also be of interest on their own, e.g. for meteorological applications. The calibration algorithm was successfully applied to measurements at two locations: Cabauw in the Netherlands and Wuxi in China. We used CI and O4 observations calibrated by the new method as input for our recently developed cloud classification scheme and also adapted the corresponding threshold values accordingly. For the observations at Cabauw, good agreement is found with the results of the original algorithm. Together with the calibration procedure of the CI and O4 absorption, the cloud classification scheme, which has been tuned to specific locations/conditions so far, can now be applied consistently to MAX-DOAS measurements at different locations. In addition to the new threshold values, further improvements were introduced to the cloud classification algorithm, namely a better description of the SZA (solar zenith angle) dependence of the threshold values and a new set of wavelengths for the determination of the CI. We also indicate specific areas for future research to further improve the cloud classification scheme. © Author(s) 2016."
"24070152900;15059495000;","Assessing lidar-based classification schemes for polar stratospheric clouds based on 16 years of measurements at Esrange, Sweden",2014,"10.1002/2013JD020355","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84898953210&doi=10.1002%2f2013JD020355&partnerID=40&md5=e41efe6a92f17481c96991fb12405c2a","Lidar measurements of polar stratospheric clouds (PSCs) are commonly analyzed in classification schemes that apply the backscatter ratio and the particle depolarization ratio. This similarity of input data suggests comparable results of different classification schemes—despite measurements being performed with a variety of mostly custom-made instruments. Based on a time series of 16 years of lidar measurements at Esrange (68°N, 21°E), Sweden, we show that PSC classification differs substantially depending on the applied scheme. The discrepancies result from varying threshold values of lidar-derived parameters used to define certain PSC types. The resulting inconsistencies could impact the understanding of long-term PSC observations documented in the literature. We identify two out of seven considered classification schemes that are most likely to give reliable results and should be used in future lidar-based studies. Using polarized backscatter ratios gives the advantage of increased contrast for observations of weakly backscattering and weakly depolarizing particles. Improved confidence in PSC classification can be achieved by a more comprehensive consideration of the effect of measurement uncertainties. The particle depolarization ratio is the key to a reliable identification of different PSC types. Hence, detailed information on the calibration of the polarization-sensitive measurement channels should be provided to assess the findings of a study. Presently, most PSC measurements with lidar are performed at 532 nm only. The information from additional polarization-sensitive measurements in the near infrared could lead to an improved PSC classification. Coincident lidar-based temperature measurements at PSC level might provide useful information for an assessment of PSC classification. © 2014. The Authors."
"6602574676;6603453147;36098286300;7003397919;35468686100;","Taking the pulse of pyrocumulus clouds",2012,"10.1016/j.atmosenv.2012.01.045","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84858781257&doi=10.1016%2fj.atmosenv.2012.01.045&partnerID=40&md5=384bf37261d60cc19dff4f87085d0eee","Large forest fires are a known natural and dominant disturbance factor in high northern latitudes, and form pyrocumulus (pyroCu), and occasionally pyrocumulonimbus (pyroCb) clouds. These clouds can transport emissions into the upper troposphere/lower stratosphere (UT/LS) and produce significant regional and even global climate effects, as is the case with some volcanoes. However, the lack of observational data within pyroCu or pyroCb complicates our ability to investigate pyro-convection and to understand the vertical and cross-isentropic transport mechanisms responsible for UT/LS injection. Here, we report detailed airborne radiation measurements within strong pyroCu taken over boreal forest fires in Saskatchewan, Canada during the Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCTAS) summer field campaign in 2008. We find a prominent smoke core within the pyroCu, which is defined by strong extinction in the UV, VIS and NIR, and high gas-particle concentrations. We also find that the angular distribution of radiance within the pyroCu is closely related to the diffusion domain in water clouds, which is dominated by multiple scattering processes. The radiation field of pyroCu can be described by diffusion approximations that are comprised of simple cosine functions, which can be used to calculate the spatial and temporal characteristics of the radiance field, and applied in cloud resolving models. We demonstrate with Monte Carlo simulations that radiation transport in pyroCu is inherently a 3D problem and must account for particle absorption. © 2012 Elsevier Ltd."
"36816070800;7004671182;8278450900;6507294227;36618357400;","On the use of the genetic algorithm filter-based feature selection technique for satellite precipitation estimation",2012,"10.1109/LGRS.2012.2187513","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84861927279&doi=10.1109%2fLGRS.2012.2187513&partnerID=40&md5=28be55f6ead3a38164ddc409a2c2aaef","A feature selection technique is used to enhance the precipitation estimation from remotely sensed imagery using an artificial neural network (PERSIANN) and cloud classification system (CCS) method (PERSIANN-CCS) enriched by wavelet features. The feature selection technique includes a feature similarity selection method and a filter-based feature selection using genetic algorithm (FFSGA). It is employed in this study to find an optimal set of features where redundant and irrelevant features are removed. The entropy index fitness function is used to evaluate the feature subsets. The results show that using the feature selection technique not only improves the equitable threat score by almost 7% at some threshold values for the winter season, but also it extremely decreases the dimensionality. The bias also decreases in both the winter (January and February) and summer (June, July, and August) seasons. © 2012 IEEE."
"55918993800;26643251000;","Retrieval of warm cloud optical properties using simple approximations",2011,"10.1016/j.rse.2011.01.010","https://www.scopus.com/inward/record.uri?eid=2-s2.0-79953220706&doi=10.1016%2fj.rse.2011.01.010&partnerID=40&md5=1a1c15b2f7fbd1b89cc2d80305430115","A new technique relying on SimpLe Approximations for cLOudy Media (SLALOM) for the retrieval of cloud optical and microphysical parameters from optical satellite data during daytime is introduced. The technique is based on simple yet highly accurate approximations of the asymptotic solutions of the radiative transfer theory which have already been implemented in the forward radiative transfer model CLOUD. These approximations enable a solution of the equations of the corresponding backward model during runtime leading to a very fast computation speed. Since these asymptotic solutions are generally applicable to weakly absorbing media only, pre-calculated look-up tables for the reflection function of a semi-infinite cloud (and also the escape function) are used to overcome this restriction within this new retrieval. SLALOM is capable of retrieving the cloud optical thickness, the effective cloud droplet radius, the liquid and ice water paths, the particle absorption length as well as some other properties of water and ice clouds. The comparison of SLALOM with both exact radiative transfer computations and the NASA MODIS cloud property product shows a very good agreement. A Fortran implementation of both CLOUD and SLALOM is available for download under the Creative Commons Attribution-Noncommercial-Share Alike 3.0 license (see http://creativecommons.org/licenses/by-nc-sa/3.0) at http://www.klimatologie.uni-bayreuth.de. © 2011 Elsevier Inc."
"7401526171;26026749200;6602886081;7005052907;","Extreme precipitation estimation using satellite-Based PERSIANN-CCS algorithm",2010,"10.1007/978-90-481-2915-7_4","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84891382236&doi=10.1007%2f978-90-481-2915-7_4&partnerID=40&md5=a34bfd5999726c805d1c0cb4c1a00401","The need for frequent observations of precipitation is critical to many hydrological applications. The recently developed high resolution satellite-based precipitation algorithms that generate precipitation estimates at sub-daily scale provide a great potential for such purpose. This chapter describes the concept of developing high resolution Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS). Evaluation of PERSIANN-CCS precipitation is demonstrated through the extreme precipitation events from two hurricanes: Ernesto in 2006 and Katrina in 2005. Finally, the global near real-time precipitation data service through the UNESCO G-WADI data server is introduced. The query functions for viewing and accessing the data are included in the chapter. © 2010 Springer Science+Business Media B.V."
"13006903300;56218731300;6602715030;13005116700;","Evaluation of the ALADIN 3D-VAR with observations of the MAP campaign",2006,"10.1007/s00703-005-0156-5","https://www.scopus.com/inward/record.uri?eid=2-s2.0-33645960101&doi=10.1007%2fs00703-005-0156-5&partnerID=40&md5=4f46756f31890e91f8fd900cfa5ace55","The aim of this work is to evaluate several 3D-VAR assimilation cycles in the limited area model ALADIN, in comparison with the dynamical adaptation of the global model (ARPEGE) analysis, and with a focus on the precipitation forecasts. We perform a detailed evaluation for a specific, well documented, test case: Intensive observing period 14 of the Mesoscale Alpine Programme (MAP) field campaign, which is well described through various MAP data. The meteorological situation was of high interest, with triggering of convection both over the Alps and over the sea, therefore it has been chosen as the framework of our case study. There are clear benefits in favour of the 3D-VAR assimilation cycles over the first hours of forecast thanks to the observations and to the preservation of small-scale features. These improvements are further enhanced with a large-scale update step added to the 3D-VAR analysis. Innovative data are used, such as relative humidity pseudo-profiles which are processed data generated from cloud classification. They bring an important information which can redesign the frontal areas. When they are used jointly with the conventional observations in the 3D-VAR, they also lead to an improvement of the precipitation scores. © Springer-Verlag 2006."
"56060986400;8711886600;7004100461;14060147600;6603230487;7004020627;7402177459;","Meteorological characterisation of the FEBUKO hill cap cloud experiments, Part II: Tracer experiments and flow characterisation with nested non-hydrostatic atmospheric models",2005,"10.1016/j.atmosenv.2005.02.036","https://www.scopus.com/inward/record.uri?eid=2-s2.0-22144451758&doi=10.1016%2fj.atmosenv.2005.02.036&partnerID=40&md5=a55c077d42ecd269d663029b08321c19","The mesoscale and local flow conditions during the ground-based cloud passage experiment FEBUKO performed at the Schmücke Mountain (Thüringer Wald) during October 2001 and 2002 are investigated and discussed. Several methods are applied to characterise and classify the cloud episodes in terms of the flow conditions and their consistency to the philosophy of cloud passage experiments. For this the flow over the mountain range and a flow that connects the experimental sites are of crucial importance. The resulting selection of events is based on a synoptical evaluation (Part I of the work) and provides a recommendation of events, which are adequate for subsequent investigations. The mesoscale air flow over the complex terrain is characterised by means of non-dimensional flow parameters like Froude number and the non-hydrostatic meteorological model LM. An analysis of the locally measured natural tracer ozone is intended to assure that measurements were performed in identical air masses at the different locations during the 14 cloud events. It is found that the flow connecting the measurement sites is distinctly associated with the flow over and/or around the Thüringer Wald, which in turn is determined by the synoptical flow and the thermal stratification. Furthermore, applications of tracer techniques using the inert SF6 for studies of transport processes in the experimental site and verification of the location of measurement stations are presented. For the tracer experiments in October 2001 and 2002 an attempt is made to reproduce them with an anelastic non-hydrostatic model in conservation form in order to understand the tracer dispersion. © 2005 Elsevier Ltd. All rights reserved."
"55732568400;7005052907;7403872687;35601880000;","A cloud-patch technique for identification and removal of no-rain clouds from satellite infrared imagery",1999,"10.1175/1520-0450(1999)038<1170:ACPTFI>2.0.CO;2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0033395118&doi=10.1175%2f1520-0450%281999%29038%3c1170%3aACPTFI%3e2.0.CO%3b2&partnerID=40&md5=e7546cde20d1087074a50fb4dcec7b75","A new cloud-patch method for the identification and removal of no-rain cold clouds from infrared (IR) imagery is presented. A cloud patch is defined as a cluster of connected IR imagery pixels that are colder than a given IR brightness temperature threshold. The threshold is derived through a combination of the rainfall field estimated from microwave observations and the IR data closely coincident with microwave sensor satellite overpasses. Seven cloud-patch features are used to describe cloud-top properties, including six IR based and one VIS based. The ID3 algorithm is used to extract structural knowledge from a training dataset and to produce classification rules expressed explicitly on the values of various patch features; these rules can be used to explain the physical principles underlying the cloud classification. The method was evaluated for the Japanese islands and surrounding oceans using AIP/1 data for June (training period) and July-August (evaluation period) 1989. The results of identifying no-rain cloud patches are very good for both periods in spite of the change in rainfall regime from frontal to subtropical convective. Nearly 20% of the total pixels and 60% of the no-rain cloud pixels were removed with negligible rain losses due to misclassification. Moreover, visible data were found to be useful for enhancing the no-rain cold patch identification and thereby reducing the rain loss."
"57212075803;7005964236;","Assimilation of clear- and cloudy-sky upper-tropospheric humidity estimates using GOES-8 and GOES-9 data",1997,"10.1175/1520-0426(1997)014<1036:AOCACS>2.0.CO;2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0001520278&doi=10.1175%2f1520-0426%281997%29014%3c1036%3aAOCACS%3e2.0.CO%3b2&partnerID=40&md5=ce6f137292f5a6892603facd54a20a1b","A strong linearity exists between the 6.7-μm clear-sky outgoing brightness temperature (BT) and dewpoint depression (DPD) at upper-tropospheric levels. A similar relationship, using the logarithm of relative humidity instead of DPD, was developed by Soden and Bretherton. Here, however, the humidity at specific levels is derived as opposed to the humidity integrated over upper-tropospheric levels. Linear relationships are obtained between a 6-h model forecast of DPD and calculated BTs at different viewing angles. The data are further stratified in terms of 400-mb temperature as an indicator of airmass type. Applying these relationships using observed 6.7-μm BTs and a 6-h forecast of 400-mb temperature yields vertically correlated estimates of DPD between 200 and 500 mb, with DPD typically decreasing with height, and corresponding rms error estimates in the range 3-6 K. The retrieval technique is applied to GOES-8 and GOES-9 data, which cover about 40% of the globe. In cloudy regions, proxy humidity estimates based on cloud classification are used. These clear-and cloudy-sky DPD estimates are assimilated every 6 h in a global forecast model, taking into consideration the horizontal correlation of the error. The system is supplemented by quality-control procedures. In parallel runs at the Canadian Meteorological Centre, the analyses and forecasts with satellite data (SAT) were found significantly improved with respect to those without satellite data (NOSAT). The system was therefore implemented. The superiority of the SAT forecasts in terms of 6.7-μm BT, 2-K versus 4-K rms at initial time, gradually decreases to the level of the NOSAT forecasts in 48 h. A slight improvement on geopotential, DPD, and temperature is observed in 48-h forecasts with respect to radiosondes over North America. The new upper-tropospheric DPD retrieval technique is robust and could easily be applied to other geostationary or polarorbiting platforms providing 6.7-μm imagery."
"57212384407;57193920957;57195287585;57212271698;7401526171;7005052907;35975568000;","PERSIANN-CNN: Precipitation estimation from remotely sensed information using artificial neural networks–convolutional neural networks",2019,"10.1175/JHM-D-19-0110.1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076544546&doi=10.1175%2fJHM-D-19-0110.1&partnerID=40&md5=6f6f49ee263571a6b9ccc9f01e467f02","Accurate and timely precipitation estimates are critical for monitoring and forecasting natural disasters such as floods. Despite having highŠresolution satellite information, precipitation estimation from remotely sensed data still suffers from methodological limitations. StateŠofŠtheŠart deep learning algorithms, renowned for their skill in learning accurate patterns within large and complex datasets, appear well suited to the task of precipitation estimation, given the ample amount of highŠresolution satellite data. In this study, the effectiveness of applying convolutional neural networks (CNNs) together with the infrared (IR) and water vapor (WV) channels from geostationary satellites for estimating precipitation rate is explored. The proposed model performances are evaluated during summer 2012 and 2013 over central CONUS at the spatial resolution of 0.088 and at an hourly time scale. Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN)–Cloud Classification System (CCS), which is an operational satelliteŠbased product, and PERSIANN–Stacked Denoising Autoencoder (PERSIANNŠSDAE) are employed as baseline models. Results demonstrate that the proposed model (PERSIANNŠCNN) provides more accurate rainfall estimates compared to the baseline models at various temporal and spatial scales. Specifically, PERSIANNŠCNN outperforms PERSIANNŠCCS (and PERSIANNŠSDAE) by 54% (and 23%) in the critical success index (CSI), demonstrating the detection skills of the model. Furthermore, the rootŠmean-square error (RMSE) of the rainfall estimates with respect to the National Centers for Environmental Prediction (NCEP) Stage IV gauge–radar data, for PERSIANNŠCNN was lower than that of PERSIANNŠCCS (PERSIANNŠSDAE) by 37% (14%), showing the estimation accuracy of the proposed model. © 2019 American Meteorological Society."
"23983423100;35095461100;18133256900;57201733749;7003995144;57201737833;6504524263;57201733954;57194385572;57194393470;57203217480;35863893500;","Analysis of Livorno heavy rainfall event: Examples of satellite-based observation techniques in support of numericalweather prediction",2018,"10.3390/rs10101549","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85055442438&doi=10.3390%2frs10101549&partnerID=40&md5=39da2bac9682538bf9347cc4ee77b21a","This study investigates the value of satellite-based observational algorithms in supporting numerical weather prediction (NWP) for improving the alert and monitoring of extreme rainfall events. To this aim, the analysis of the very intense precipitation that affected the city of Livorno on 9 and 10 September 2017 is performed by applying three remote sensing techniques based on satellite observations at infrared/visible and microwave frequencies and by using maps of accumulated rainfall from the weather research and forecasting (WRF) model. The satellite-based observational algorithms are the precipitation evolving technique (PET), the rain class evaluation from infrared and visible observations (RainCEIV) technique and the cloud classification mask coupling of statistical and physics methods (C-MACSP). Moreover, the rain rates estimated by the ItalianWeather Radar Network are also considered to get a quantitative evaluation of RainCEIV and PET performance. The statistical assessment shows good skills for both the algorithms (for PET: bias = 1.03, POD = 0.76, FAR = 0.26; for RainCEIV: bias = 1.33, POD = 0.77, FAR = 0.41). In addition, a qualitative comparison among the three technique outputs, rain rate radar maps, and WRF accumulated rainfall maps is also carried out in order to highlight the advantages of the different techniques in providing real-time monitoring, as well as quantitative characterization of rainy areas, especially when rain rate measurements fromWeather Radar Network and/or from rain gauges are not available. © 2018 by the authors."
"55969140000;55247565600;55948466000;14625770800;","Ground-based cloud classification by learning stable local binary patterns",2018,"10.1016/j.atmosres.2018.02.023","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85044060510&doi=10.1016%2fj.atmosres.2018.02.023&partnerID=40&md5=e0e60cf6e098a578cd8b1e75400b7bf7","Feature selection and extraction is the first step in implementing pattern classification. The same is true for ground-based cloud classification. Histogram features based on local binary patterns (LBPs) are widely used to classify texture images. However, the conventional uniform LBP approach cannot capture all the dominant patterns in cloud texture images, thereby resulting in low classification performance. In this study, a robust feature extraction method by learning stable LBPs is proposed based on the averaged ranks of the occurrence frequencies of all rotation invariant patterns defined in the LBPs of cloud images. The proposed method is validated with a ground-based cloud classification database comprising five cloud types. Experimental results demonstrate that the proposed method achieves significantly higher classification accuracy than the uniform LBP, local texture patterns (LTP), dominant LBP (DLBP), completed LBP (CLTP) and salient LBP (SaLBP) methods in this cloud image database and under different noise conditions. And the performance of the proposed method is comparable with that of the popular deep convolutional neural network (DCNN) method, but with less computation complexity. Furthermore, the proposed method also achieves superior performance on an independent test data set. © 2018 Elsevier B.V."
"56135196400;7401526171;6507378331;7403872687;7005052907;","A two-stage deep neural network framework for precipitation estimation from bispectral satellite information",2018,"10.1175/JHM-D-17-0077.1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85042644755&doi=10.1175%2fJHM-D-17-0077.1&partnerID=40&md5=19c9be33f301231ab06f0f26d9ebf9ce","Compared to ground precipitation measurements, satellite-based precipitation estimation products have the advantage of global coverage and high spatiotemporal resolutions. However, the accuracy of satellitebased precipitation products is still insufficient to serve many weather, climate, and hydrologic applications at high resolutions. In this paper, the authors develop a state-of-the-art deep learning framework for precipitation estimation using bispectral satellite information, infrared (IR), and water vapor (WV) channels. Specifically, a two-stage framework for precipitation estimation from bispectral information is designed, consisting of an initial rain/no-rain (R/NR) binary classification, followed by a second stage estimating the nonzero precipitation amount. In the first stage, the model aims to eliminate the large fraction of NR pixels and to delineate precipitation regions precisely. In the second stage, the model aims to estimate the pointwise precipitation amount accurately while preserving its heavily skewed distribution. Stacked denoising autoencoders (SDAEs), a commonly used deep learning method, are applied in both stages. Performance is evaluated along a number of common performance measures, including both R/NR and real-valued precipitation accuracy, and compared with an operational product, Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS). For R/NR binary classification, the proposed two-stage model outperforms PERSIANN-CCS by 32.56% in the critical success index (CSI). For real-valued precipitation estimation, the two-stage model is 23.40% lower in average bias, is 44.52% lower in average mean squared error, and has a 27.21% higher correlation coefficient. Hence, the two-stage deep learning framework has the potential to serve as a more accurate and more reliable satellite-based precipitation estimation product. The authors also provide some future directions for development of satellite-based precipitation estimation products in both incorporating auxiliary information and improving retrieval algorithms. © 2018 American Meteorological Society."
"57190867837;14048744800;56111060800;57149551600;56735366800;55687238300;56954125400;","Cloud Type Classification of Total-Sky Images Using Duplex Norm-Bounded Sparse Coding",2017,"10.1109/JSTARS.2017.2669206","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85016500979&doi=10.1109%2fJSTARS.2017.2669206&partnerID=40&md5=5d5ebc8757300f0c69474233dff48381","Cloud type classification plays an essential role in ground-based cloud observation. However, it is a challenge to accurately identify the cloud categories that involve a large variety of structural patterns and visual appearances. Image representation and classifier are the crucial factors for cloud classification, though they are individually investigated in the literature. This paper proposes a new cloud type classification method using duplex norm-bounded sparse coding (DNSC), which designs image representation and classifier under the same framework, i.e., norm-bounded sparse coding (NSC). NSC is not only used to encode local descriptors, but also well explored to develop an effective classifier. NSC takes both locality and sparseness into account, and it can be benefit to capture discriminative patterns for image representation and have discriminative power for classifier. Furthermore, NSC has closed-form solution and can be computed efficiently. More specifically, DNSC first extracts local descriptor from an input cloud image, and then DNSC forms a holistic representation leveraging NSC and max-pooling strategy. Finally, a classifier is built on the holistic representation using NSC. The proposed DNSC is evaluated on the total-sky cloud image set, and the experimental results demonstrate that DNSC outperforms the state-of-the-art methods and its accuracy increases by about 7% compared with baselines. In addition, the categorywise performance improvement is particularly pronounced over the complex categories, such as Cirriform and Mixed cloudiness. © 2017 IEEE."
"7405431519;7401526171;7005052907;55644003021;35975568000;55937227700;56148670500;","Merging high-resolution satellite-based precipitation fields and point-scale rain gauge measurements-A case study in Chile",2017,"10.1002/2016JD026177","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85019937963&doi=10.1002%2f2016JD026177&partnerID=40&md5=efca0d10850572ba9b9448816d994410","With high spatial-temporal resolution, Satellite-based Precipitation Estimates (SPE) are becoming valuable alternative rainfall data for hydrologic and climatic studies but are subject to considerable uncertainty. Effective merging of SPE and ground-based gauge measurements may help to improve precipitation estimation in both better resolution and accuracy. In this study, a framework for merging satellite and gauge precipitation data is developed based on three steps, including SPE bias adjustment, gauge observation gridding, and data merging, with the objective to produce high-quality precipitation estimates. An inverse-root-mean-square-error weighting approach is proposed to combine the satellite and gauge estimates that are in advance adjusted and gridded, respectively. The model is applied and tested with the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS) estimates (daily, 0.04° × 0.04°) over Chile, for the 6 year period of 2009-2014. Daily observations from about 90% of collected gauges over the study area are used for model calibration; the rest of the gauged data are regarded as ground “truth” for validation. Evaluation results indicate high effectiveness of the model in producing high-resolution-precision precipitation data. Compared to reference data, the merged data (daily) show correlation coefficients, probabilities of detection, root-mean-square errors, and absolute mean biases that were consistently improved from the original PERSIANN-CCS estimates. The cross-validation evidences that the framework is effective in providing high-quality estimates even over nongauged satellite pixels. The same method can be applied globally and is expected to produce precipitation products in near real time by integrating gauge observations with satellite estimates. © 2017. American Geophysical Union. All Rights Reserved."
"57188989662;23984671400;","Artificial intelligence systems for rainy areas detection and convective cells' delineation for the south shore of Mediterranean Sea during day and nighttime using MSG satellite images",2016,"10.1016/j.atmosres.2016.04.013","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84964452566&doi=10.1016%2fj.atmosres.2016.04.013&partnerID=40&md5=f6145466b90aa917b4cd8371cf950cea","The aim of this study is to investigate the potential of cloud classification by means of support vector machines using high resolution images from northern Algeria. The images were taken from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board of the Meteosat Second Generation (MSG) satellite. An automatic system was developed to operate during both day and nighttime by following two steps of data processing. The first aims to detect rainy areas in cloud systems, whereas the second delineates convective cells from stratiform ones. A set of 12 spectral parameters was selected to extract information about cloud properties, which are different from day to night. The training and validation steps of this study were performed by in-situ rainfall measurement data, collected during the rainy season of years 2011 and 2012 via automatic rain gauge stations distributed in northern Algeria. Artificial neural networks (ANNs) and support vector machine (SVM) were explored, by combining spectral parameters derived from MSG images. Better performances were obtained by the SVM classifier, in terms of Critical Success Index and Probability of Detection for rainy areas detection (CSI = 0.81, POD = 91%), and also for convective/stratiform delineation (CSI = 0.55, POD = 74%). © 2016 Elsevier B.V."
"56452028600;54790498500;26642240800;57089599900;57157796600;","MCLOUD: A multiview visual feature extraction mechanism for ground-based cloud image categorization",2016,"10.1175/JTECH-D-15-0015.1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84964963362&doi=10.1175%2fJTECH-D-15-0015.1&partnerID=40&md5=f97ee089ff62bbe55c691195c8ecfbfe","In this paper, a novel Multiview CLOUD (mCLOUD) visual feature extraction mechanism is proposed for the task of categorizing clouds based on ground-based images. To completely characterize the different types of clouds, mCLOUD first extracts the raw visual descriptors from the views of texture, structure, and color simultaneously, in a densely sampled way-specifically, the scale invariant feature transform (SIFT), the census transform histogram (CENTRIST), and the statistical color features are extracted, respectively. To obtain a more descriptive cloud representation, the feature encoding of the raw descriptors is realized by using the Fisher vector. This is followed by the feature aggregation procedure. A linear support vector machine (SVM) is employed as the classifier to yield the final cloud image categorization result. The experiments on a challenging cloud dataset termed the six-class Huazhong University of Science and Technology (HUST) cloud demonstrate that mCLOUD consistently outperforms the state-of-the-art cloud classification approaches by large margins (at least 6.9%) under all the different experimental settings. It has also been verified that, compared to the single view, the multiview cloud representation generally enhances the performance. © 2016 American Meteorological Society."
"56111060800;57149551600;14048744800;56735366800;55687238300;56954125400;","From pixels to patches: A cloud classification method based on a bag of micro-structures",2016,"10.5194/amt-9-753-2016","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84959465150&doi=10.5194%2famt-9-753-2016&partnerID=40&md5=aa423df54eaad46499d1990da6b456b9","Automatic cloud classification has attracted more and more attention with the increasing development of whole sky imagers, but it is still in progress for ground-based cloud observation. This paper proposes a new cloud classification method, named bag of micro-structures (BoMS). This method treats an all-sky image as a collection of micro-structures mapped from image patches, rather than a collection of pixels. It represents the image with a weighted histogram of micro-structures. Based on this representation, BoMS recognizes the cloud class of the image by a support vector machine (SVM) classifier. Five classes of sky condition are identified: cirriform, cumuliform, stratiform, clear sky, and mixed cloudiness. BoMS is evaluated on a large data set, which contains 5000 all-sky images captured by a total-sky cloud imager located in Tibet (29.25° N, 88.88° E). BoMS achieves an accuracy of 90.9 % for 10-fold cross-validation, and it outperforms state-of-the-art methods with an increase of 19 %. Furthermore, influence of key parameters in BoMS is investigated to verify their robustness. © 2016 Author(s)."
"7005354212;22234180300;7005236944;","Enhanced snow and ice identification with the VIIRS cloud mask algorithm",2013,"10.1080/2150704X.2013.815381","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84880445175&doi=10.1080%2f2150704X.2013.815381&partnerID=40&md5=0afe6f08659459091e3654e782260e6d","New procedures have been developed to help identify snow and sea ice with the Suomi-National Polar-orbiting Partnership (S-NPP) Visible Infrared Imager Radiometer Suite (VIIRS) Cloud Mask (VCM) algorithm. The accurate detection of snow and sea ice is necessary in order to apply the correct spectral tests needed to detect clouds and make accurate cloud confidence classifications. During the VCM Calibration Validation activity, it was found that the procedures in place at the time of the satellite launch occasionally produced four types of misclassifications: (1) snow and/or ice surfaces in dry atmospheric regions misclassified as clouds, (2) multi-layered clouds in humid regions misclassified as snow, (3) low-level clouds with glaciated tops misclassified as sea ice, and (4) frozen lakes not classified as ice. The new procedures presented in this article use data collected in the VIIRS mid-wavelength region, i.e. both the 3.7 μm and 4.0 μm bands, as well as the 12.0 μm IR band to eliminate all four types of misclassifications. The results demonstrate that split window, mid-wavelength IR imagery provides valuable information for developers of automated cloud classification algorithms as well as those who generate sea ice analyses in support of ocean navigation during polar wintertime conditions. It is concluded that collecting data in these mid-wavelength IR bands should be considered part of any future satellite sensor designed for environmental monitoring. © 2013 Taylor & Francis Group, LLC."
"56058752000;57203502073;8619872100;","OBJECT-BASED CLASSIFICATION of URBAN AIRBORNE LIDAR POINT CLOUDS with MULTIPLE ECHOES USING SVM",2012,"10.5194/isprsannals-I-3-135-2012","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85021139239&doi=10.5194%2fisprsannals-I-3-135-2012&partnerID=40&md5=0646ea4344ad53874e88e4a2e8b8aebb","Airborne LiDAR point clouds classification is meaningful for various applications. In this paper, an object-based analysis method is proposed to classify the point clouds in urban areas. In the process of classification, outliers in the point clouds are first removed. Second, surface growing algorithm is employed to segment the point clouds into different clusters. The above point cloud segmentation is helpful to derive useful features such as average height, size/area, proportion of multiple echoes, slope/orientation, elevation difference, rectangularity, ratio of length to width, and compactness. At last, SVM-based classification is performed on the segmented point clouds with radial basis function as kernel. Two datasets with high point densities are employed to test the proposed method, and three classes are predefined. The results suggest that our method will produce the overall classification accuracy larger than 97% and the Kappa coefficient larger than 0.95."
"49861093000;49861735000;51161851900;6701607011;","A land and ocean microwave cloud classification algorithm derived from AMSU-A and -B, trained using MSG-SEVIRI infrared and visible observations",2011,"10.1175/MWR-D-10-05012.1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-80052414657&doi=10.1175%2fMWR-D-10-05012.1&partnerID=40&md5=5f97e960027256ee199e2ff1f0ddf2ef","A statistical cloud classification and cloud mask algorithm is developed based on Advanced Microwave Sounding Unit (AMSU-A and -B) microwave (MW) observations. The visible and infrared data from the Meteosat Third Generation-Spinning Enhanced Visible and Infrared Imager (MSG-SEVIRI) are used to train the microwave classifier. The goal of the MW algorithms is not to fully reproduce this MSG-SEVIRI cloud classification, as theMW observations do not have enough information on clouds to reach this level of precision. The objective is instead to obtain a stand-alone MW cloud mask and classification algorithm that can be used efficiently in forthcoming retrieval schemes of surface or atmospheric parameters from microwave satellite observations. This is an important tool over both ocean and land since the assimilation of the MW observations in the operational centers is independent from the other satellite observations. Clear sky and low, medium, and opaque-high clouds can be retrieved over ocean and land at a confidence level of more than 80%. An information content analysis shows that AMSU-B provides significant information over both land and ocean, especially for the classification of medium and high clouds, whereas AMSU-A is more efficient over ocean when discriminating clear situations and low clouds. © 2011 American Meteorological Society."
"6508287655;57209692325;35330367300;42361599300;6603631763;7102797196;","The recent state of the climate: Driving components of cloud-type variability",2011,"10.1029/2010JD014559","https://www.scopus.com/inward/record.uri?eid=2-s2.0-79958784184&doi=10.1029%2f2010JD014559&partnerID=40&md5=ee57573cc3066cf5fb2fdabd4a8f7540","To reduce the Earth's radiation budget uncertainty related to cloud types' changes, and better understand the climate constraints resulting from long-term clouds' variability, frequent and finer (than actually existing) observations are necessary. This is one of the aims of future satellite programs such as the Global Change Observation Mission-Climate (GCOM-C) satellite, to be launched by the Japan Aerospace Exploration Agency (JAXA). To facilitate the transition from past to future observations, the actual state of climate variables (e.g., cloud types) needs to be evaluated. This evaluation is attempted in the present work with the analysis of long-term cloud types' distribution and amounts. The data set used for this study is 25 years (1982-2006) of global daytime cloud properties observed by the National Oceanic and Atmospheric Administration- Advanced Very-High-Resolution Radiometer (NOAA-AVHRR) satellites sensors. Though various calibrations have been applied on NOAA-AVHRR data, the effects of the orbit drift experienced by these satellites need to be corrected. A signal processing decomposition method allowing the filtering of the cloud types' amount trend affected by the orbit drift is used to perform the necessary corrections. The results obtained show a quantifiable improvement of the cloud amount estimation and trends of the individual NOAA satellites initial observations, at the global and regional scales. The corrected global cloud amount shows a slight decrease in its linear trend. The driving factors of this trend are the decrease in mid and low clouds overwhelming the increase in high clouds (+0.04% cloud amount/yr). A comparison with other cloud climatology studies such as the International Cloud Satellite Climatology Project (ISCCP) data set shows that the global cloud decrease noticed in NOAA-AVHRR's data is smaller. And, contrary to the NOAA-AVHRR's data, the driving force of the ISCCP linear trend is a sharp decrease in low clouds (-0.20% cloud amount/yr). Copyright 2011 by the American Geophysical Union."
"56218203200;23096443800;8539422800;16230028100;55575353100;7102866124;","Life cycle of deep convective systems over the eastern tropical pacific observed by TRMM and GOES-W",2009,"10.2151/jmsj.87A.381","https://www.scopus.com/inward/record.uri?eid=2-s2.0-71549157492&doi=10.2151%2fjmsj.87A.381&partnerID=40&md5=597cdd82730eba8ad598246e86a3ff8f","The life cycle of deep convective systems over the eastern tropical Pacific (30°N to 30°S, 180 to 90°W) was studied in terms of cloud types, as classified by a split window (11 μm and 12 μm). Hourly split window image data of Geostationary Operational Environmental Satellite (GOES-W) from January 2001 to December 2002 was used in this study. Deep convective systems consist mostly of optically thick cumulus type clouds in the earlier stage and a cirrus type cloud area that increases with time in the later stage. During this analysis period and over the analysis area, the life stage of deep convective system, to a large extent, can be identified by computing the percentage of cirrus type clouds within the deep convective system from a single snap shot of the split window image. Coincident Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) observations were used to study the relationship between the percentage of cirrus type clouds within a deep convective system (i.e., its life stage) and the rainfall rate. It was found that the rainfall rate tends to be larger in the earlier stage of the life cycle when a smaller percentage of cirrus type cloud is present within the deep convection. © 2009, Meteorological Society of Japan."
"7004406545;24080709900;6507442204;","A simple model of light transmission through the atmosphere over the Baltic Sea utilising satellite data",2008,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-52349095506&partnerID=40&md5=7a1e3141fc9a17a855e5ad3413033c66","A simple spectral model of solar energy input to the sea surface was extended to incorporate space-borne data. The extension involved finding a method of determining aerosol optical thickness (on the basis of AVHRR data) and the influence of cloudiness (on the basis of METEOSAT data) on the solar energy flux. The algorithm for satellite data assimilation involves the analysis of satellite images from the point of view of cloud identification and their classification with respect to light transmission. Solar energy input values measured at the Earth's surface by traditional methods were used to calibrate and validate the model. Preliminary evaluation of the results indicates a substantial improvement in the accuracy of estimates of solar energy input to the sea surface in relation to models utilising only traditionally obtained data on the state of the atmosphere. © 2008, by Institute of Oceanology PAS."
"35069885100;9037443200;","Cloud classification in NOAA AVHRR imageries using spectral and textural features",2008,"10.1007/s12524-008-0017-z","https://www.scopus.com/inward/record.uri?eid=2-s2.0-70349548893&doi=10.1007%2fs12524-008-0017-z&partnerID=40&md5=877bf53eed7507b4840b0dd8fbe4d5ec","Clouds contribute significantly to the formation of many of the natural hazards. Hence cloud mapping and its classification becomes a major component of the various physical models which are used for forecasting natural hazards. The problem of cloud data classification from NOAA AVHRR (advance very high resolution radiometer) satellite imagery using image transformation techniques is considered in this paper. The singular value decomposition (SVD) scheme is used to extract the salient spectral and textural features attributed to satellite snow and cloud data in visible and IR channels. The goals of this paper are to discriminate between clear sky and clouds in an 8 × 8 pixel array of 1.1 km AVHRR data. If clouds are present then classify them as low, medium or high range. This scheme can effectively segregate clouds and non-cloud features in the visible and IR bands of the imagery. It can also classify clouds as low, medium or high range with a success rate of 70-90%. Computer-based snow and cloud discrimination and automatic cloud classification system will help the forecaster in various climatological applications, viz., energy balance estimation, precipitation forecasting, landslide forecasting, weather forecasting and avalanche forecasting etc. © Indian Society of Remote Sensing 2008."
"8711886600;56060986400;8645916500;7402177459;","Meteorological characterisation of the FEBUKO hill cap cloud experiments, Part I: Synoptic characterisation of measurement periods",2005,"10.1016/j.atmosenv.2005.02.006","https://www.scopus.com/inward/record.uri?eid=2-s2.0-22144464388&doi=10.1016%2fj.atmosenv.2005.02.006&partnerID=40&md5=dd82f5a9dd4346cc3f4c4a90785c6526","The synoptic and local meteorological conditions during the ground-based cloud passage experiment FEBUKO performed at the Schmücke Mountain (Thüringer Wald) during October 2001 and 2002 are reviewed and discussed. A general description of the weather types and a classification of air masses are presented. In the second part the meteorological situations are illustrated in detail for the different experimental cloud events. The main objective of this two-part study is to classify the cloud events with respect to the occurring weather conditions and consistency to the philosophy of cloud passage experiments. Therefore, particular emphasis is placed on the incident flow conditions and on the separation of orographic and non-orographic cloud types. In the case of the flow characterisation, weather charts and calculated backward trajectories are used to determine the horizontal wind pattern and the rawinsonde data for the vertical structure of wind vectors. Additionally, in order to describe the local flow conditions the observed wind speed and direction at the experimental site on the summit are applied for the total of 14 cloud episodes. For the examination of the orographic character and properties of clouds, satellite pictures of different spectral channels, vertical thermodynamic data of the rawinsonde as well as the measured liquid water content and the cloud base height are evaluated. The resulting event evaluation provides a basis for subsequent local analysis of the flow over and/or around the mountain range (Part II of the study). Generally, it is found that more suitable conditions were encountered in October 2001 than in October 2002. Especially for the anticyclonic southwest weather-type, stable incoming flow condition as well as orographically induced clouds could be clearly identified. © 2005 Elsevier Ltd. All rights reserved."
"36065490500;57205605065;24545539300;57205601188;57207352282;55492712600;57193083690;","Clouds classification from Sentinel-2 imagery with deep residual learning and semantic image segmentation",2019,"10.3390/rs11020119","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85060691480&doi=10.3390%2frs11020119&partnerID=40&md5=afc23c6257fa27fa27d30e7becc9fb7e","Detecting changes in land use and land cover (LULC) from space has long been the main goal of satellite remote sensing (RS), yet the existing and available algorithms for cloud classification are not reliable enough to attain this goal in an automated fashion. Clouds are very strong optical signals that dominate the results of change detection if they are not removed completely from imagery. As various architectures of deep learning (DL) have been proposed and advanced quickly, their potential in perceptual tasks has been widely accepted and successfully applied to many fields. A comprehensive survey of DL in RS has been reviewed, and the RS community has been suggested to be leading researchers in DL. Based on deep residual learning, semantic image segmentation, and the concept of atrous convolution, we propose a new DL architecture, named CloudNet, with an enhanced capability of feature extraction for classifying cloud and haze from Sentinel-2 imagery, with the intention of supporting automatic change detection in LULC. To ensure the quality of the training dataset, scene classification maps of Taiwan processed by Sen2cor were visually examined and edited, resulting in a total of 12,769 sub-images with a standard size of 224 × 224 pixels, cut from the Sen2cor-corrected images and compiled in a trainset. The data augmentation technique enabled CloudNet to have stable cirrus identification capability without extensive training data. Compared to the traditional method and other DL methods, CloudNet had higher accuracy in cloud and haze classification, as well as better performance in cirrus cloud recognition. CloudNet will be incorporated into the Open Access Satellite Image Service to facilitate change detection by using Sentinel-2 imagery on a regular and automatic basis. © 2019 by the authors."
"55561559600;57201373924;57201378591;57202193525;6507534695;","Classification of aerial photogrammetric 3D point clouds",2018,"10.14358/PERS.84.5.287","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85047395345&doi=10.14358%2fPERS.84.5.287&partnerID=40&md5=f0eee31acdd513bf06de58aa4cadf01c","We present a powerful method to extract per-point semantic class labels from aerial photogrammetry data. Labeling this kind of data is important for tasks such as environmental modeling, object classification, and scene understanding. Unlike previous point cloud classification methods that rely exclusively on geometric features, we show that incorporating color information yields a significant increase in accuracy in detecting semantic classes. We test our classification method on four real-world photogrammetry datasets that were generated with Pix4Dmapper, and with varying point densities. We show that off-the-shelf machine learning techniques coupled with our new features allow us to train highly accurate classifiers that generalize well to unseen data, processing point clouds containing 10 million points in less than three minutes on a desktop computer. We also demonstrate that our approach can be used to generate accurate Digital Terrain Models, outperforming approaches based on more simple heuristics such as Maximally Stable Extremal Regions. © 2018 American Society for Photogrammetry and Remote Sensing."
"57194545411;57194542466;55885020100;","POINT CLOUD CLASSIFICATION of TESSERAE from TERRESTRIAL LASER DATA COMBINED with DENSE IMAGE MATCHING for ARCHAEOLOGICAL INFORMATION EXTRACTION",2017,"10.5194/isprs-annals-IV-2-W2-203-2017","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85030238494&doi=10.5194%2fisprs-annals-IV-2-W2-203-2017&partnerID=40&md5=3b623f73c6e0ffb987f25f7d0cc59f58","Reasoning from information extraction given by point cloud data mining allows contextual adaptation and fast decision making. However, to achieve this perceptive level, a point cloud must be semantically rich, retaining relevant information for the end user. This paper presents an automatic knowledge-based method for pre-processing multi-sensory data and classifying a hybrid point cloud from both terrestrial laser scanning and dense image matching. Using 18 features including sensor's biased data, each tessera in the high-density point cloud from the 3D captured complex mosaics of Germigny-des-prés (France) is segmented via a colour multi-scale abstraction-based featuring extracting connectivity. A 2D surface and outline polygon of each tessera is generated by a RANSAC plane extraction and convex hull fitting. Knowledge is then used to classify every tesserae based on their size, surface, shape, material properties and their neighbour's class. The detection and semantic enrichment method shows promising results of 94% correct semantization, a first step toward the creation of an archaeological smart point cloud. © Authors 2017. CC BY 4.0 License."
"26026749200;57203043665;7006029393;56219284300;6603478504;","On the quantification of atmospheric rivers precipitation from space: Composite assessments and case studies over the eastern north pacific ocean and the Western United States",2016,"10.1175/JHM-D-15-0061.1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84957921884&doi=10.1175%2fJHM-D-15-0061.1&partnerID=40&md5=4371e9fa198e288b11b7587eac03ee6a","Atmospheric rivers (ARs) are often associated with extreme precipitation, which can lead to flooding or alleviate droughts. A decade (2003-12) of landfalling ARs impacting the North American west coast (between 32.5° and 52.5°N) is collected to assess the skill of five commonly used satellite-based precipitation products [T3B42, T3B42 real-time (T3B42RT), CPC morphing technique (CMORPH), PERSIANN, and PERSIANN-Cloud Classification System (CCS)] in capturing ARs' precipitation rate and pattern. AR detection was carried out using a database containing twice-daily satellite-based integrated water vapor composite observations. It was found that satellite products are more consistent over ocean than land and often significantly underestimate precipitation rate over land compared to ground observations. Incorrect detection of precipitation from IR-based methods is prevalent over snow and ice surfaces where microwave estimates often show underestimation or missing data. Bias adjustment using ground observation is found very effective to improve satellite products, but it also raises concern regarding near-real-time applicability of satellite products for ARs. The analysis using individual case studies (6-8 January and 13-14 October 2009) and an ensemble of AR events suggests that further advancement in capturing orographic precipitation and precipitation over cold and frozen surfaces is needed to more reliably quantify AR precipitation from space. © 2016 American Meteorological Society."
"36816070800;8278450900;7004671182;6507294227;7401526171;","On an enhanced PERSIANN-CCS algorithm for precipitation estimation",2012,"10.1175/JTECH-D-11-00146.1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84868229759&doi=10.1175%2fJTECH-D-11-00146.1&partnerID=40&md5=bff2fef2a384cad8babe1fc3d0cfdd86","By employing wavelet and selected features (WSF), median merging (MM), and selected curve-fitting (SCF) techniques, the Precipitation Estimation from Remotely Sensed Imagery using an Artificial Neural Networks Cloud Classification System (PERSIANN-CCS) has been improved. The PERSIANN-CCS methodology includes the following four main steps: 1) segmentation of satellite cloud images into cloud patches, 2) feature extraction, 3) classification of cloud patches, and 4) derivation of the temperature-rain-rate (T-R) relationship for every cluster. The enhancements help improve step 2 by employing WSF, and step 4 by employing MM and SCF. For the study area herein, the results show that the enhanced methodology improves the equitable threat score (ETS) of the daily and hourly rainfall estimates mostly in the winter and fall. The ETS percentage improvement is about 20% for the daily (10% for hourly) estimates in the winter, 10% for the daily (8% for hourly) estimates in the fall, and at most 5% for the daily estimates in the summer at some rainfall thresholds. In the winter and fall, the area bias is improved almost at all rainfall thresholds for daily and hourly estimates. However, no significant improvement is obtained in the spring, and the area bias in the summer is also greater than that of the implemented PERSIANN-CCS algorithm. © 2012 American Meteorological Society."
"55946401600;7405972102;","Two-dimensional, threshold-based cloud type classification using mtsat data",2012,"10.1080/2150704X.2012.698320","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85008834180&doi=10.1080%2f2150704X.2012.698320&partnerID=40&md5=06bb6f0811cfefe6acf5234b539d01a8","A new two-dimensional threshold diagram (2D-THR) has been developed based on maximum likelihood cloud classification results, which can readily be applied for Multi-functional Transport Satellite (MTSAT) split window datasets. Because 2D-THR was trained using northern summer 2010 data for Japan and its surrounding area, it is typically suitable only for summer. Comparison of snapshot cloud type distributions showed that 2D-THR images and the corresponding night-time microphysical colour composite images as well as 2D-THR images and Japan Meteorological Agency (JMA) cloud type images are in good agreement. A time series inter-comparison of the hourly 2D-THR cloud classification results with the JMA cloud type classification data product was performed by calculating spatial correlation of cloud percentage for 1◦ × 1◦ grid cells. For cumulonimbus, high-level, middle-level and low-level clouds over tropical and subtropical areas in the northwestern Pacific Ocean region, the spatial correlation between 2D-THR and JMA is moderate. Thus, 2D-THR cloud classification algorithm can be applied in both regions. © 2012 Taylor & Francis Group, LLC. All rights reserved."
"7004893330;57193132723;6602137606;8206969400;57207507108;12645700600;7402934750;57215071208;7006303509;","Thermodynamic phase profiles of optically thin midlatitude clouds and their relation to temperature",2010,"10.1029/2009JD012889","https://www.scopus.com/inward/record.uri?eid=2-s2.0-77953737812&doi=10.1029%2f2009JD012889&partnerID=40&md5=21741ee8449f1126b019a00c8ad5b74f","The relationship between cloud thermodynamic phase and temperature in some aircraft measurements conducted in midlatitude frontal clouds suggests that significant liquid does not exist at temperatures colder than 258 K. This data set is often used to verify parameterizations of cloud phase in general circulation models. However, other aircraft campaigns and different instruments suggest a different relationship. Here we examine the temperature-phase relationship for midlatitude optically thin winter clouds. Cloud phase and temperature profiles derived from 5 years of ground-based lidar depolarization and radiosonde measurements are analyzed for two midlatitude locations: the U. S. Atmospheric Radiation Measurement Program Southern Great Plains site and the Site Instrumental de Recherche par Tldtection Atmosphrique in France. Because lidars are attenuated in optically thick clouds, the data set only includes clouds with optical thickness of <3. Cloud phase is obtained by using the classical method based on a depolarization ratio threshold of 11% for differentiating liquid from ice. The frequency of occurrence of clouds either completely liquid or completely glaciated in the temperature range from 233 to 273 K is similar to previous observations in the midlatitudes but somewhat greater than in the Arctic. The relationship between ice phase occurrence and temperature only slightly changes between cloud base and top. At both sites, liquid is more prevalent at colder temperatures than has been found previously in some thicker frontal clouds, suggesting different processes for glaciation in nonfrontal optically thin clouds. Copyright 2010 by the American Geophysical Union."
"24168891800;7404433688;7401436524;56640142900;7101614616;","A new satellite-based census of precipitating and nonprecipitating clouds over the tropics and subtropics",2008,"10.1029/2008GL033208","https://www.scopus.com/inward/record.uri?eid=2-s2.0-45549098534&doi=10.1029%2f2008GL033208&partnerID=40&md5=5676d38a5c89cd085d0dd097d0883b97","Cloud properties retrieved from TRMM VIRS measurements analyzed for PR-determined precipitating clouds (P-PCs) and nonprecipitating clouds (PN-PCs), respectively. The total cloud amount (CA) averaged across the tropics and subtropics in boreal summer is about 55.9 and 40.1, over ocean and land, respectively, with P-PCs contributing less than 10% to the total CA. Low P-PCs that have cloud top lower than 680 mb are extremely scanty, while low PN-PCs account for near half of total PN-PCs. The mean cloud optical thickness (COT) of P-PCs exceeds 60, approximately 10 times that of PN-PCs. According to ISCCP cloud classification, four primary cloud types of PN-PCs, cumulus, stratocumulus, altocumulus and cirrus are revealed, whereas deep convective clouds and cirrostratus are proved to be the first and second primary cloud type of P-PCs, implying a considerable amount of P-PCs with high cloud top but moderate COT. Copyright 2008 by the American Geophysical Union."
"7004484970;7102291050;7402717381;8319623900;7003376335;6601945161;6602835531;","Weather systems occurring over Fort Simpson, Northwest Territories, Canada, during three seasons of 1998-1999: 1. Cloud features",2004,"10.1029/2004JD004876","https://www.scopus.com/inward/record.uri?eid=2-s2.0-14344260544&doi=10.1029%2f2004JD004876&partnerID=40&md5=c6df5d8fa78d80a700b68816a4575fbf","An investigation of high-latitude continental cloud systems was carried out in the interior of the Northwest Territories of Canada during three multiweek periods during the fall, winter, and spring of 1998-1999 as part of the Canadian Global Energy and Water Cycle Experiment (GEWEX) Enhanced Study. Radar data supplemented by satellite, upper air, and surface observations were used to determine the seasonal behavior of cloud macroscopic properties and compare these with similar observations elsewhere. Unique features included the prevalence of multilayered systems, the cold temperatures of low clouds, and a significant diurnal trend in cloud properties in the winter. A synoptic classification was developed and shown to be an important factor in explaining the variability of cloud properties. A consistent picture emerges of the upslope component and wind shear aloft contributing to the cloud structure in five synoptic classes. Vertically resolved cloud properties highlighted the importance of the ice process in these cloud systems. The cloud system reflectivity and temperature dependencies further supported the synoptic characterizations and highlighted the significance of using seasonally based relationships in automated cloud identification algorithms. The implication of the cloud system variability for radiation measurements was also shown. The radar reflectivity data, degraded to match CloudSat resolution and sensitivity, showed that cloud detection was reliable but that there was a positive bias with cloud thickness. Negative biases in cloud top retrievals based on advanced very high resolution radiometer data were also identified. The Global Environmental Multiscale model illustrated some degree of bias in the occurrence and vertical distribution of these cloud systems. Winter situations in general and midclouds situations in particular were the most poorly handled in both the satellite applications and the model simulations. Copyright 2004 by the American Geophysical Union."
"7201472576;","Validation of modelled cloudiness using satellite-estimated cloud climatologies",1996,"10.3402/tellusa.v48i5.12206","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0030404855&doi=10.3402%2ftellusa.v48i5.12206&partnerID=40&md5=520cffd573a0cc40e462d878a097fe10","A method to evaluate forecasts of total fractional cloud cover using satellite measurements is demonstrated. Cloud analyses in the form of monthly cloud climatologies are extracted from NOAA AVHRR data which are compared to corresponding cloud forecast information from the HIRLAM and ECMWF numerical weather prediction models. The satellite-based cloud information is extracted for a summer month in 1994 and a winter month in 1995 by use of the SMHI cloud classification model SCANDIA. Cloud analyses are conducted for an area covering a substantial part of northern Europe. Deficiencies in forecasted cloud amounts are found for both models, especially the underestimation of cloudiness for short forecast lengths with the HIRLAM model. Forecast improvements using the HIRLAM model are indicated when introducing a cloud initialisation technique using cloud fields from initial 6-hour forecasts (first-guess fields). Future systematic validations using this technique are, however, needed to make firm conclusions on the general model behaviour. SCANDIA-derived cloud information is proposed as a valuable complement to other datasets used for cloud forecast validation (e.g., the SSM/I- and ISCCP data sets)."
"57188848315;35098748100;7403564495;35099345700;56095856700;55576700800;57203228273;57214924430;56770031000;57196548688;57190214513;","Spatiotemporal Distributions of Cloud Parameters and the Temperature Response Over the Mongolian Plateau During 2006-2015 Based on MODIS Data",2019,"10.1109/JSTARS.2018.2857827","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050993218&doi=10.1109%2fJSTARS.2018.2857827&partnerID=40&md5=c5a975359828b8ef4c1704ff1d9940a3","The Mongolian Plateau (MP) has important influences on regional and global climate change. The spatiotemporal variations in the cloud cover and cloud optical thickness of total, high, middle, and low clouds over the MP during the daytime from 2006 to 2015 are analyzed using MODIS level 2 atmospheric data. Results show that the annual average total cloud cover over the MP decreases from the forest area in the northeast to the desert area in the southwest. The total cloud cover over the MP is obviously higher in summer than in other seasons, in which high clouds have a largest proportion, with the substantial total cloud cover changes. The spatial distributions of the high, middle, and low cloud covers over the MP are highly variable. The cooling effect of the cloud net radiative forcing is greater during the daytime in summer than in other seasons, which is likely associated with thick cloud optical thickness or large cloud cover in summer. Combined with the analyses of relationships among cloud cover, cloud optical thickness, cloud radiative forcing, and air temperature, the results show that significantly negative correlations exist between cloud optical thickness and cloud radiative forcing, and between total cloud cover and air temperature in the MP. The decrease in air temperature in summer over the MP during daytime confirm that the increase in the daytime total cloud cover strengthen the cooling effects of clouds and decrease the air temperature, especially in the high-value area with cloud cover distribution over the northeast MP. © 2018 IEEE."
"57205418691;54790508000;57205415509;56769697500;","Evaluation of ground surface models derived from unmanned aerial systems with digital aerial photogrammetry in a disturbed conifer forest",2019,"10.3390/rs11010084","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85059933051&doi=10.3390%2frs11010084&partnerID=40&md5=c14acc736080149c4e1a83143185e345","Detailed vertical forest structure information can be remotely sensed by combining technologies of unmanned aerial systems (UAS) and digital aerial photogrammetry (DAP). A key limitation in the application of DAP methods, however, is the inability to produce accurate digital elevation models (DEM) in areas of dense vegetation. This study investigates the terrain modeling potential of UAS-DAP methods within a temperate conifer forest in British Columbia, Canada. UAS-acquired images were photogrammetrically processed to produce high-resolution DAP point clouds. To evaluate the terrain modeling ability of DAP, first, a sensitivity analysis was conducted to estimate optimal parameters of three ground-point classification algorithms designed for airborne laser scanning (ALS). Algorithms tested include progressive triangulated irregular network (TIN) densification (PTD), hierarchical robust interpolation (HRI) and simple progressive morphological filtering (SMRF). Points were classified as ground from the ALS and served as ground-truth data to which UAS-DAP derived DEMs were compared. The proportion of area with root mean square error (RMSE) < 1.5 m were 56.5%, 51.6% and 52.3% for the PTD, HRI and SMRF methods respectively. To assess the influence of terrain slope and canopy cover, error values of DAP-DEMs produced using optimal parameters were compared to stratified classes of canopy cover and slope generated from ALS point clouds. Results indicate that canopy cover was approximately three times more influential on RMSE than terrain slope. © 2019 by the authors."
"37011423400;36064917000;55292994700;57202250355;57201878011;57201423155;","A deep neural network with spatial pooling (DNNSP) for 3-D point cloud classification",2018,"10.1109/TGRS.2018.2829625","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85047637490&doi=10.1109%2fTGRS.2018.2829625&partnerID=40&md5=e3d4934eee861eda190e21b57ed8c371","The large number of object categories and many overlapping or closely neighboring objects in large-scale urban scenes pose great challenges in point cloud classification. Most works in deep learning have achieved a great success on regular input representations, but they are hard to be directly applied to classify point clouds due to the irregularity and inhomogeneity of the data. In this paper, a deep neural network with spatial pooling (DNNSP) is proposed to classify large-scale point clouds without rasterization. The DNNSP first obtains the point-based feature descriptors of all points in each point cluster. The distance minimum spanning tree-based pooling is then applied in the point feature representation to describe the spatial information among the points in the point clusters. The max pooling is next employed to aggregate the point-based features into the cluster-based features. To assure the DNNSP is invariant to the point permutation and sizes of the point clusters, the point-based feature representation is determined by the multilayer perception (MLP) and the weight sharing for each point is retained, which means that the weight of each point in the same layer is the same. In this way, the DNNSP can learn the features of points scaled from the entire regions to the centers of the point clusters, which makes the point cluster-based feature representations robust and discriminative. Finally, the cluster-based features are input to another MLP for point cloud classification. We have evaluated qualitatively and quantitatively the proposed method using several airborne laser scanning and terrestrial laser scanning point cloud data sets. The experimental results have demonstrated the effectiveness of our method in improving classification accuracy. © 1980-2012 IEEE."
"55704387600;13204458100;6701599239;8680433600;36722293600;","A global multilayer cloud identification with POLDER/PARASOL",2017,"10.1175/JAMC-D-16-0159.1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85017497714&doi=10.1175%2fJAMC-D-16-0159.1&partnerID=40&md5=38bd06f6e03cd5b2d439da18c950cb34","The detection of multilayer cloud situations is important for satellite retrieval algorithms and for many climate-related applications. In this paper, the authors describe an algorithm based on the exploitation of the Polarization and Directionality of the Earth's Reflectance (POLDER) observations to identify monolayered and multilayered cloudy situations along with a confidence index. The authors' reference comes from the synergy of the active instruments of the A-Train satellite constellation. The algorithm is based upon a decision tree that uses a metric from information theory and a series of tests on POLDER level-2 products. The authors obtain a multilayer flag as the final result of a tree classification, which takes discrete values between 0 and 100. Values closest to 0 (100) indicate a higher confidence in the monolayer (multilayer) character. This indicator can be used as it is or with a threshold level that minimizes the risk of misclassification, as a binary index to distinguish between monolayer and multilayer clouds. For almost fully covered and optically thick enough cloud scenes, the risk of misclassification ranges from 29% to 34% over the period 2006-10, and the average confidences in the estimated monolayer and multilayer characters of the cloud scenes are 74.0% and 58.2%, respectively. With the binary distinction, POLDER provides a climatology of the mono-multilayer cloud character that exhibits some interesting features. Comparisons with the performance of the Moderate Resolution Imaging Spectroradiometer (MODIS) multilayer flag are given. © 2017 American Meteorological Society."
"6508287655;7102797196;","Alternative approach for satellite cloud classification: Edge gradient application",2013,"10.1155/2013/584816","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84893803137&doi=10.1155%2f2013%2f584816&partnerID=40&md5=460590bf914c4331c67c5c7067885055","Global atmospheric heat exchanges are highly dependent on the variation of cloud types and amounts. For a better understanding of these exchanges, an appropriate cloud type classification method is necessary. The present study proposes an alternative approach to the often used cloud optical and thermodynamic properties based classifications. This approach relies on the application of edge detection techniques on cloud top temperature (CTT) derived from global satellite maps. The gradient map obtained through these techniques is then used to distinguish various types of clouds. The edge detection techniques used are based on the idea that a pixel's neighborhood contains information about its intensity. The variation of this intensity (gradient) offers the possibility to decompose the image into different cloud morphological features. High gradient areas would correspond to cumulus-like clouds, while low gradient areas would be associated with stratus-like clouds. Following the application of these principles, the results of the cloud classification obtained are evaluated against a common cloud classification method based on cloud optical properties' variations. Relatively good matches between the two approaches are obtained. The best results are observed with high gradient clouds and the worst with low gradient clouds. © 2013 Jules R. Dim and Tamio Takamura."
"35775565200;7401513327;7202162685;","Application of cloud vertical structure from CloudSat to investigate MODIS-derived cloud properties of cirriform, anvil, and deep convective clouds",2013,"10.1002/jgrd.50306","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84881183116&doi=10.1002%2fjgrd.50306&partnerID=40&md5=a5c9d1787e6c492f8dbd30d5b6cc8be1","CloudSat cloud vertical structure is combined with the CALIPSO Lidar and Collection-5 Level 2 cloud data from Aqua's Moderate Resolution Imaging Spectroradiometer (MODIS) to investigate the mean properties of high/cirriform, anvil, and deep convective (DC) clouds. Cloud properties are sampled over 30°S-30°N for 1 year and compared to existing results of Collection-4 Aqua MODIS high-level cloud observations where cloud types were categorized using the International Satellite Cloud Climatology Project (ISCCP) cloud classification scheme. Results show high/cirriform sampled in this study have high biases in cloud top pressure and temperature due to CloudSat's sensitivity to thin high clouds. Mean cloud properties of DC show reasonable agreement with existing DC results notwithstanding mean cloud optical thickness which is ~23% higher due to the exclusion of thick cirrus and anvil clouds. Anvil cloud properties are a mix between high/cirriform and DC according to ISCCP cloud optical thickness thresholds whereby ~80% are associated with high/cirriform and the other 20% are associated with DC. The variability of cloud effective particle radii was also evaluated using DC with ≥5 dBZ echoes at and above 10 km. No evidence of larger cloud effective particle radii are given despite considering higher reaching echoes. Using ISCCP cloud optical thickness thresholds, ~25% of DC would be classified as cirrostratus clouds. These results provide a basis to evaluate the uncertainty of the ISCCP cloud classification scheme and MODIS-derived cloud properties using active satellite observations. Key Points Optical and microphysical properties of high clouds are investigated using CVSThe data show anvil clouds are predominantly characteristic of Hi/Ci cloudsCVS is useful to assess the uncertainty of passive RS cloud taxonomy schemes ©2013. American Geophysical Union. All Rights Reserved."
"35227762400;7006246996;7202252296;6701333444;","Cloud properties over the North Slope of Alaska: Identifying the prevailing meteorological regimes",2012,"10.1175/JCLI-D-11-00636.1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84871779810&doi=10.1175%2fJCLI-D-11-00636.1&partnerID=40&md5=fc654612f16640ec211afa7d6239ff33","Long time series of Arctic atmospheric measurements are assembled into meteorological categories that can serve as test cases for climate model evaluation. The meteorological categories are established by applying an objective k-means clustering algorithm to 11 years of standard surface-meteorological observations collected from 1 January 2000 to 31 December 2010 at the North Slope of Alaska (NSA) site of the U.S. Department of Energy Atmospheric Radiation Measurement Program (ARM). Four meteorological categories emerge. These meteorological categories constitute the first classification by meteorological regime of a long time series of Arctic meteorological conditions. The synoptic-scale patterns associated with each category, which include well-known synoptic features such as the Aleutian low and Beaufort Sea high, are used to explain the conditions at the NSA site. Cloud properties, which are not used as inputs to the k-means clustering, are found to differ significantly between the regimes and are also well explained by the synopticscale influences in each regime. Since the data available at the ARM NSA site include a wealth of cloud observations, this classification is well suited for model-observation comparison studies. Each category comprises an ensemble of test cases covering a representative range in variables describing atmospheric structure, moisture content, and cloud properties. This classification is offered as a complement to standard case-study evaluation of climate model parameterizations, in which models are compared against limited realizations of the Earth-atmosphere system (e.g., from detailed aircraft measurements). © 2012 American Meteorological Society."
"36816070800;7004671182;8278450900;6507294227;36618357400;","On the use of a cluster ensemble cloud classification technique in satellite precipitation estimation",2012,"10.1109/JSTARS.2012.2201449","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84869867471&doi=10.1109%2fJSTARS.2012.2201449&partnerID=40&md5=5c498327d10df72a093013758ddc126b","In this paper, the link-based cluster ensemble (LCE) method is utilized to improve cloud classification and satellite precipitation estimation. High resolution Satellite Precipitation Estimation (SPE) is based on the Precipitation Estimation from Remotely Sensed Imagery using an Artificial Neural Network Cloud Classification (PERSIANN-CCS) algorithm. This modified SPE with the incorporation of LCE involves the following four steps: 1) segmentation of infrared cloud images into patches; 2) cloud patch feature extraction; 3) clustering cloud patches using LCE; and 4) dynamic application of brightness temperature (Tb) and rain-rate relationships, derived using satellite observations. In order to cluster the cloud patches, the LCE method combines multiple data partitions from different clustering methods. The results show that using the cluster ensemble increases the performance of rainfall estimates compared to the SPE algorithm using a Self Organizing Map (SOM) neural network. The false alarm ratio (FAR), probabilities of detection (POD), equitable threat score (ETS), and bias are used as quantitative measures to assess the performance of the algorithm. It is shown that both the ETS and bias provide improvement in the summer and winter seasons. Almost 5% ETS improvement is obtained at some threshold values for the winter season using the cluster ensemble. © 2008-2012 IEEE."
"16040260000;7004280182;8255132900;34881962400;34881831300;","Techniques based on Support Vector Machines for cloud detection on QuickBird satellite imagery",2011,"10.1109/IGARSS.2011.6049178","https://www.scopus.com/inward/record.uri?eid=2-s2.0-80955164610&doi=10.1109%2fIGARSS.2011.6049178&partnerID=40&md5=db2c031482273285c1c877cc62c924f9","Purpose of this work is the study of cloud detection techniques. This work identifies the cloud cover of optical images acquired by the QuickBird satellite, comparing these with others of the same area, acquired by Landsat 7 in which there are no clouds. The images are combined using an early fusion technique [1]. The tool exploits the neighborhood model [2] for increasing the amount of information for the training set and the Singular Value Decomposition for carrying out the feature extraction [3]. In order to introduce these structures into thematic classification tasks by SVMs it was necessary develop a tree kernel function based on tree kernel function defined in SVM-LightTK. The aim of the tree kernel function is evaluate the similarity level between a generic couples of tree structures. In this paper we report the results obtained comparing the performance of different approaches in cloud classification problem. The final purpose is the production of cloud cover maps. Throughout such different experimental setups we measured the capabilities of each algorithm under different points of view. First of all, we considered the classification accuracy by computing traditional parameter such as overall accuracy. A second analysis regarded the efforts that are required in the design of optimal algorithms. Indeed, these techniques are characterized by different parameters that have to be appropriately tuned in order to obtain the best performance. Finally the robustness of the techniques has been also considered. In particular the classification accuracy has been evaluated also for images not considered in the training phase. © 2011 IEEE."
"35362779300;6603767711;7402215419;","Validation of cloud-resolving model background data for cloud data assimilation",2010,"10.1175/2009MWR3012.1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-77953205032&doi=10.1175%2f2009MWR3012.1&partnerID=40&md5=384a05f70e12bd57bcee2031bda3c902","Results from a cloud-resolving model are systematically compared with a variety of observations, both ground based and satellite, in order to better understand the mean background errors and their correlations. This is a step in the direction of developing a background error covariance matrix for use in cloud data assimilation. Observation sources include the Geostationary Operational Environmental Satellite (GOES), the Atmospheric Emitted Radiance Interferometer (AERI), a microwave radiometer (MWR), radiosonde, and cloud radar. When exploring model biases in temperature, precipitable water vapor, and liquid water path, a warm and moist bias at night and a cool and dry bias during the day are observed. Values for the background decorrelation length of water variables are determined. In addition, a dynamic cloud mask is presented to give more control in the assimilation of cloudy satellite radiances, allowing different cloud types to be excluded from the assimilation as well as establishing values for the maximum residuals to be considered. © 2010 American Meteorological Society."
"7006802750;15841242400;","Optimization of an instance-based GOES cloud classification algorithm",2007,"10.1175/JAM2451.1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-33846837523&doi=10.1175%2fJAM2451.1&partnerID=40&md5=6cf972163271ed49654ac9898e3b40c6","An instance-based nearest-neighbor algorithm was developed for a Geostationary Operational Environmental Satellite (GOES) cloud classifier. Expert-labeled samples serve as the training sets for the various GOES image classification scenes. The initial implementation of the classifier using the complete set of available training samples has proven to be an inefficient method for real-time image classifications, requiring long computational run times and significant computer resources. A variety of training-set reduction methods were examined to find smaller training sets that provide quicker classifier run times with minimal reduction in classifier testing set accuracy. General differences within real-time image classifications as a result of using the various reduction methods were also analyzed. The fast condensed nearest-neighbor (FCNN) method reduced the size of the individual training sets by 68.3% (fourfold cross-validation testing average) while the average overall accuracy of the testing sets decreased by only 4.1%. Training sets resulting from these reduction methods were also applied within a real-time classifier using a one-nearest-neighbor subroutine. Using the FCNN-reduced set, the subroutine run time on a 30° latitude X 30° longitude image (GOES-10 daytime) with 11 289 600 total pixels decreased by an average of 60.7%."
"6506440816;57202670988;","Neural network based methods for cloud classification on AVHRR images",2000,"10.1080/014311600209977","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0034690706&doi=10.1080%2f014311600209977&partnerID=40&md5=b336aa976b17ffdc2376d20ae87edddc","Artificial neural networks trained on spectral and textural features extracted from Advanced Very High Resolution Radiometer (AVHRR) images have been used to develop an automated cloud classification system. Selection of the optimum combination of features was achieved by using statistical methods presented in earlier work by Gu et al. and by running large numbers of neural network simulations on test datasets. The performance of these methods surpasses that of other approaches such as the use of Gabor filters for texture segmentation and the maximum likelihood classifier. A particular architecture for an operational classification system is presented based on a two-stage multiple network configuration which is shown to segment complex images to a high degree of accuracy and achieves an overall accuracy on an independent, representative test set of 91%. © 2000 Taylor & Francis Group, LLC."
"6603307411;","A multi-dimensional histogram technique for cloud classification",2000,"10.1080/01431160050030565","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0033869634&doi=10.1080%2f01431160050030565&partnerID=40&md5=7a45e8044584347085db706a34f5672f","An old method (often called graphical) for decomposing a mixed distribution to Gaussian components was generalized for the multidimensional case. The technique approximates an initial histogram by means of a sum of normally distributed components. A special separation algorithm for n-dimensional (nD) histograms was introduced for that purpose. Application of the separation method enables one to replace the customary pixel-by-pixel processing with a cluster-by-cluster procedure in any threshold algorithm. Stability of the algorithm was tested, comparing the decomposition of the radiation histograms produced by means of the Advanced Very High Resolution Radiometer (AVHRR) five channel measurements in the 3D and 5D cases for an area over Europe during nine orbits. The results show that multidimensional histograms are easily separable due to sufficiently large Euclidean distance between basic cloud and surface clusters in the measurement space. Applying the separation scheme in conjunction with a certain threshold technique to process the AVHRR-based histograms enables one to produce an automatic cloud detection algorithm. The algorithm sets necessary thresholds without auxiliary (i.e. beyond AVHRR) information and estimates average cloud amount, cloud top temperature and cloud reflectance at three levels for the histogram area. An example of such an algorithm for determining cloudiness parameters necessary for the Earth's radiation budget monitoring is presented. © 2000 Taylor & Francis Group, LLC."
"6603751015;","Cloud classification before Luke Howard",1989,"10.1175/1520-0477(1989)070<0381:CCBLH>2.0.CO;2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0024485220&doi=10.1175%2f1520-0477%281989%29070%3c0381%3aCCBLH%3e2.0.CO%3b2&partnerID=40&md5=348762d5eb62cbe43fd73ac6fad1c41e","A brief outline of the history of cloud painting prior to the first cloud classification schemes of Luke Howard and Lamarck is presented. It is shown that European painters had accurately represented most of the different cloud forms between about 1425 and 1675. -Authors"
"37003839700;56950041300;57194794576;56081246400;57210591181;7003839010;","SfM-based method to assess gorgonian forests (Paramuricea clavata (Cnidaria, Octocorallia))",2018,"10.3390/rs10071154","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050459682&doi=10.3390%2frs10071154&partnerID=40&md5=e3404f1e6aab4d1249c81775b9281008","Animal forests promote marine habitats morphological complexity and functioning. The red gorgonian, Paramuricea clavata, is a key structuring species of the Mediterranean coralligenous habitat and an indicator species of climate effects on habitat functioning. P. clavata metrics such as population structure, morphology and biomass inform on the overall health of coralligenous habitats, but the estimation of these metrics is time and cost consuming, and often requires destructive sampling. As a consequence, the implementation of long-term and wide-area monitoring programmes is limited. This study proposes a novel and transferable Structure from Motion (SfM) based method for the estimation of gorgonian population structure (i.e., maximal height, density, abundance), morphometries (i.e., maximal width, fan surface) and biomass (i.e., coenenchymal Dry Weight, Ash Free DriedWeight). The method includes the estimation of a novel metric (3D canopy surface) describing the gorgonian forest as a mosaic of planes generated by fitting multiple 5 cm × 5 cm facets to a SfM generated point cloud. The performance of the method is assessed for two different cameras (GoPro Hero4 and Sony NEX7). Results showed that for highly dense populations (17 colonies/m2), the SfM-method had lower accuracies in estimating the gorgonians density for both cameras (60% to 89%) than for medium to low density populations (14 and 7 colonies/m2) (71% to 100%). Results for the validation of the method showed that the correlation between ground truth and SfM estimates for maximal height, maximal width and fan surface were between R2 = 0.63 and R2 = 0.9, and R2 = 0.99 for coenenchymal surface estimation. The methodological approach was used to estimate the biomass of the gorgonian population within the study area and across the coralligenous habitat between -25 to -40 m depth in the Portofino Marine Protected Area. For that purpose, the coenenchymal surface of sampled colonies was obtained and used for the calculations. Results showed biomass values of dry weight and ash free dry weight of 220 g and 32 g for the studied area and to 365 kg and 55 Kg for the coralligenous habitat in the Marine Protected Area. This study highlighted the feasibility of the methodology for the quantification of P. clavata metrics as well as the potential of the SfM-method to improve current predictions of the status of the coralligenous habitat in the Mediterranean sea and overall management of threatened ecosystems. © 2018 by the authors."
"56188627800;57201012625;56068624000;14625770800;54402367600;","Multimodal ground-based cloud classification using joint fusion convolutional neural network",2018,"10.3390/rs10060822","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85048980086&doi=10.3390%2frs10060822&partnerID=40&md5=13c2dc8acba6b209b54fcceaf47bd94c","The accurate ground-based cloud classification is a challenging task and still under development. The most current methods are limited to only taking the cloud visual features into consideration, which is not robust to the environmental factors. In this paper, we present the novel joint fusion convolutional neural network (JFCNN) to integrate the multimodal information for ground-based cloud classification. To learn the heterogeneous features (visual features and multimodal features) from the ground-based cloud data, we designed the proposed JFCNN as a two-stream structure which contains the vision subnetwork and multimodal subnetwork. We also proposed a novel layer named joint fusion layer to jointly learn two kinds of cloud features under one framework. After training the proposed JFCNN, we extracted the visual and multimodal features from the two subnetworks and integrated them using a weighted strategy. The proposed JFCNN was validated on the multimodal ground-based cloud (MGC) dataset and achieved remarkable performance, demonstrating its effectiveness for ground-based cloud classification task. © 2018 by the authors."
"57196061602;57195202700;57144638800;6602918196;57196066156;8368714300;","Automatic Cloud-Type Classification Based On the Combined Use of a Sky Camera and a Ceilometer",2017,"10.1002/2017JD027131","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85031706802&doi=10.1002%2f2017JD027131&partnerID=40&md5=ea38eca4d49665b0209408eaee6833bd","A methodology, aimed to be fully operational, for automatic cloud classification based on the synergetic use of a sky camera and a ceilometer is presented. The random forest machine learning algorithm was used to train the classifier with 19 input features: 12 extracted from the sky camera images and 7 from the ceilometer. The method was developed and tested based on a set of 717 images collected at the radiometric stations of the Univ. of Jaén (Spain). Up to nine different types of clouds (plus clear sky) were considered (clear sky, cumulus, stratocumulus, nimbostratus, altocumulus, altostratus, stratus, cirrocumulus, cirrostratus, and cirrus) plus an additional category multicloud, aiming to account for the frequent cases in which the sky is covered by several cloud types. A total of eight experiments was conducted by (1) excluding/including the ceilometer information, (2) including/excluding the multicloud category, and (3) using six or nine different cloud types, aside from the clear-sky and multicloud category. The method provided accuracies ranging from 45% to 78%, being highly dependent on the use of the ceilometer information. This information showed to be particularly relevant for accurately classifying “cumuliform” clouds and to account for the multicloud category. In this regard, the camera information alone was found to be not suitable to deal with this category. Finally, while the use of the ceilometer provided an overall superior performance, some limitations were found, mainly related to the classification of clouds with similar cloud base height and geometric thickness. ©2017. American Geophysical Union. All Rights Reserved."
"55968539200;57111978300;6602574642;55311432800;7101742624;7202007759;","Persistence-based temporal filtering for MODIS snow products",2016,"10.1016/j.rse.2015.12.030","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84953897414&doi=10.1016%2fj.rse.2015.12.030&partnerID=40&md5=19a9672921a975a914ec306e6dba57f2","Single-day snow covered area (SCA) products are incomplete and often inadequate representations of ground conditions due to short term variation in cloud cover, snow cover, and sensor geometry. To mitigate these effects, we developed a by-pixel filtering algorithm to produce relatively cloud-free SCA products from 16. days of MODIS imagery. The algorithm uses previous days' data to estimate the current SCA value of each pixel and uses a simple persistence test to reduce the effects of spurious SCA/cloud classifications in the input products. To be positively identified as SCA, a pixel must be snow-covered in the two most recent, cloud-free scenes of the 16-day period. We applied this time-domain-filtering (TDF) methodology to two single-day MODIS fractional snow cover products (MOD10A1 and MODSCAG) over the MODIS period of record (2000-present) and compared the outputs to the unfiltered products, to filtered maps generated using the cloud-gap-filled algorithm (CGF, Hall et al., 2010), and to historical snow assessment reports from the U.S. Army Corps of Engineers, Cold Regions Research and Engineering Laboratory (CRREL). The CRREL reports were manually generated and quality-controlled by an analyst and are treated as ground truth. We find that, when applied to MODSCAG, the TDF algorithm successfully fills in gap pixels and limits the effects of snow/cloud confusion and produces a filtered product that is more consistent and accurate than the MODSCAG CGF product and comparable to the MOD10A1 CGF product. © 2015 Published by Elsevier Inc."
"7404284987;57212025640;","Multi-model solar irradiance prediction based on automatic cloud classification",2015,"10.1016/j.energy.2015.08.075","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84946097446&doi=10.1016%2fj.energy.2015.08.075&partnerID=40&md5=f94dc1f9eb07162e328f65378c968b05","This paper proposes a framework to automatically conduct cloud classification on all-sky images and perform short-term solar irradiance prediction according to the classification results. The all-sky images are divided into blocks to deal with the mixed cloud type conditions. Local texture patterns and statistical texture features are extracted from the image blocks for cloud classification. Different cloud types with various heights, thickness, and opacity have different impact on the variation of solar irradiance. Therefore, several regression models are trained to capture the characteristics of irradiance changes under different cloud types. The current classified cloud type is used to select a corresponding prediction model. Such design substantially increases the prediction accuracy. The experimental results verify the effectiveness of the proposed framework. Both the proposed cloud classification method and irradiance prediction mechanism outperform existing works. Adding local texture patterns in the feature vector enhance the classification performance. Compared with non-block based methods, the proposed block-based method could increase the classification rate by 5%-10%. Utilizing multiple prediction models according cloud types could lower both the mean absolute error and the root mean squared error on short-term irradiance prediction. © 2015 Elsevier Ltd."
"8081431900;7005181100;57203025969;","A multichannel temporally adaptive system for continuous cloud classification from satellite imagery",2003,"10.1109/TGRS.2003.813550","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0038105252&doi=10.1109%2fTGRS.2003.813550&partnerID=40&md5=067c041544d417faf5b4d1a62d0e46d9","A two-channel temporal updating system is presented, which accounts for feature changes in the visible and infrared satellite images. The system uses two probabilistic neural network classifiers and a context-based predictor to perform continuous cloud classification during the day and night. Test results for 27 h of continuous classification and updating are presented on a sequence of Geostationary Operational Environmental Satellite & images. Further test results of the system on two new sets of data with 1-2 weeks time difference are also presented that show the potential of this system as an operational continuous cloud classification system."
"56201438000;7201770023;7201707538;6604026372;","A high-resolution analysis of cloud amount and type over complex topography",2001,"10.1175/1520-0450(2001)040<0016:AHRAOC>2.0.CO;2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0035093476&doi=10.1175%2f1520-0450%282001%29040%3c0016%3aAHRAOC%3e2.0.CO%3b2&partnerID=40&md5=e172343bec5445dcb4dcc7d13ca623b4","This paper reports on the first application of a multispectral textural Bayesian cloud classification algorithm (""SRTex"") to the general problem of the determination of high-spatial resolution cloud-amount and cloud-type climatological distributions. One year of NOAA-14 daylight passes over a region of complex topography (the South Island of New Zealand and adjacent ocean areas) is analyzed, and exploratory cloud-amount and -type climatological distributions are developed. When validated against a set of surface observations, the cloud-amount distributions have no significant bias at seasonal and yearly timescales, and explain between 70% (seasonal) and 90% (annual) of the spatial variance in the surface observations. The cloud-amount distributions show strong land/sea contrasts. Lowest cloud frequencies are found in the lee of the major alpine feature in the analysis domain (the Southern Alps) and over mountain-sheltered valleys and adjacent sea areas. Over the oceans, cloud frequencies are highest over sub-Antarctic water masses, and range from 90% to 95%. However, over the sea adjacent to the coast on the western side of the Southern Alps, there is a distinct minimum in cloud amount that appears to be related to the orography. The cloud-type climatological distributions are analyzed in terms of both simple frequency of occurrence and conditional frequency of occurrence, which is the frequency of occurrence as a fraction of the total number of times that the cloud type could have been observed. These distributions reveal the presence of preferred locations for some cloud types. There is strong evidence that uplift over major mountain ranges is a source of transmissive cirrus (enhancing occurrence by a factor of 2) and that the resulting cirrus coverage is most extensive and frequent in spring. Over the ocean areas, SST-related effects may determine the spatial distributions of stratocumulus, with higher frequencies observed over sub-Antarctic waters than over subtropical waters. Also, there is a positive correlation between mean cloud-top height and SST, but no similar relationship is found for other cloud types."
"7101962277;35615376800;","Alpine cloud climatology using long-term NOAA-AVHRR satellite data",2001,"10.1007/s007040170044","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0035020103&doi=10.1007%2fs007040170044&partnerID=40&md5=850e69b9f79416ea55bd7df13747a50c","Three different climates have been identified by our evaluation of AVHRR (Advanced Very High Resolution Radiometer) data using APOLLO (AVHRR Processing scheme Over Land, Clouds and Ocean) for a five-years cloud climatology of the Alpine region. The cloud cover data from four layers were spatially averaged in boxes of 15km by 14km. The study area only covers 540km by 560km. but contains regions with moderate, Alpine and Mediterranean climate. Data from the period July 1989 until December 1996 have been considered. The temporal resolution is one scene per day, the early afternoon pass, yielding monthly means of satellite derived cloud coverages 5% to 10% above the daily mean compared to conventional surface observation. At non-vegetated sited the cloudiness is sometimes significantly overestimated. Averaging high resolution cloud data seems to be superior to low resolution measurements of cloud properties and averaging is favourable in topographical homogeneous regions only. The annual course of cloud cover reveals typical regional features as foehn or temporal singularities as the so-called Christmas thaw. The cloud cover maps in spatially high resolution show local luff/lee features which outline the orography. Less cloud cover is found over the Alps than over the forelands in winter, an accumulation of thick cirrus is found over the High Alps and an accumulation of thin cirrus north of the Alps."
"7005212820;7202208382;","Simulation of upper tropospheric clouds with the Colorado State University general circulation model",1999,"10.1029/1998JD200074","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0033608681&doi=10.1029%2f1998JD200074&partnerID=40&md5=bb222b938f42a79b979328ef233b1fbe","We have compared the climatology, of upper tropospheric clouds simulated with the Colorado State University (CSU) general circulation model against cloud products retrieved by the International Satellite Cloud Climatology Project (ISCCP). Following the ISCCP cloud classification, upper tropospheric clouds are defined as clouds with cloud tops above 440 hPa. We refined our comparison by considering separately clouds with cloud tops above 180, 310, and 440 hPa in order to exhibit the optical characteristics of the highest clouds in the model and satellite cloud products. Four ranges of visible optical depths (τ) were used to distinguish cirrus (τ ≤ 3.6) from optically thicker cirrostratus (3.6 < τ ≤ 23) and deep convective clouds (τ > 23) and to further differentiate between thin (0.02 < τ ≤ 1.6) and thick (1.6 < τ ≤ 3.6) cirrus. Results show that the CSU GCM simulates satisfactorily the zonally averaged distribution of upper tropospheric clouds when all values of τ are included but systematically underpredicts the frequency of occurrence of clouds with values of τ less than 3.6 when compared against ISCCP-D1 data. This result reveals that simulated total-column optical depths for columns that include upper tropospheric clouds are too large relative to satellite-derived values. The CSU GCM simulates upper tropospheric clouds in the tropics more successfully than those in the middle latitudes. In the middle latitudes the model fails to simulate upper tropospheric clouds over the continents, especially over high plateaus and mountain ranges. Discrepancies between the CSU GCM and the ISCCP cloud products can be addressed in terms of our simple formulation of the optical thickness as a function of the prognostic liquid/ice water content, the prescribed value of the effective radius, and the geometrical thickness of the upper tropospheric model layers. We investigate the impact of the vertical resolution used in the GCM on the calculation of the optical depths of single-layer clouds using estimates of the geometrical thickness of cloudy layers from the Lidar In-Space Technology Experiment. Copyright 1999 by the American Geophysical Union."
"6602137606;7005516084;7004384155;","Improved diurnal interpolation of reflected broadband shortwave observations using ISCCP data",1999,"10.1175/1520-0426(1999)016<0038:IDIORB>2.0.CO;2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0032714164&doi=10.1175%2f1520-0426%281999%29016%3c0038%3aIDIORB%3e2.0.CO%3b2&partnerID=40&md5=e9a710b3bf1a506453f8fc3dc850a79b","The multisatellite Earth Radiation Budget Experiment (ERBE) was designed to provide complete temporal coverage of the solar-reflected and earth-emitted radiation. Following operation of ERBE scanners on as few as one and as many as three satellites between November 1984 and February 1990, narrow-field-of-view earth radiation budget measurements were resumed in March 1994 by the Scanner for Radiation Budget (ScaRaB) mission and in December 1997 by the first Clouds and the Earth's Radiant Energy System (CERES) instrument, each time on a single satellite. Due to sparse temporal sampling, diurnal variations must be accounted for in order to establish accurate unbiased daily and monthly mean radiant exitance. When the ERBE diurnal interpolation algorithm is used alone, large discrepancies appear between monthly mean radiative fluxes obtained from single- and multisatellite data. The authors extend the algorithm by accounting for diurnally varying cloud cover using International Satellite Cloud Climatology Project (ISCCP) data products. Significant improvements are found in regions where clouds have a pronounced diurnal cycle. Further improvements are obtained by also taking into account diurnal variations of cloud properties such as optical thickness using either ISCCP cloud radiance data or a cloud classification. These approaches require the development of directional models to represent the angular dependence of the cloud albedo corresponding to the ISCCP cloud classification."
"7202711022;57200417468;35095032000;55790633100;","Waveform-based point cloud classification in land-cover identification",2015,"10.1016/j.jag.2014.07.004","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84920646105&doi=10.1016%2fj.jag.2014.07.004&partnerID=40&md5=0d95329fa64b708ba3a546caff9290fe","Full-waveform topographic LiDAR data provide more detailed information about objects along the path of a laser pulse than discrete-return (echo) topographic LiDAR data. Full-waveform topographic LiDAR data consist of a succession of cross-section profiles of landscapes and each waveform can be decomposed into a sum of echoes. The echo number reveals critical information in classifying land cover types. Most land covers contain one echo, where as topographic LiDAR data in trees and roof edges contained multi-echo waveform features. To identify land-cover types, waveform-based classifier was integrated single-echoand multi-echo classifiers for point cloud classification. The experimental area was the Namasha district of Southern Taiwan, and the land-cover objects were categorized as roads, trees (canopy), grass (grass and crop), bare (bare ground), and buildings (buildings and roof edges). Waveform features were analyzed with respect to the single- and multi-echo laser-pathsamples, and the critical waveform features were selected according to the Bhattacharyya distance. Next, waveform-based classifiers were performed using support vector machine (SVM) with the local, spatial features of waveform topographic LiDAR information, and optical image information. Results showed that by using fused waveform and optical information, the waveform-based classifiers achieved the highest overall accuracy in identifying land-cover point clouds among the models, especially when compared toan echo-based classifier. © 2014 Elsevier B.V."
"56728284900;56210962100;7409077047;55448001800;","Analysis and application of the relationship between cumulonimbus (Cb) cloud features and precipitation based on FY-2C image",2014,"10.3390/atmos5020211","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84902673340&doi=10.3390%2fatmos5020211&partnerID=40&md5=da7eecb67b60866d67ffe5fa79b879c7","Although cumulonimbus (Cb) clouds are the main source of precipitation in south China, the relationship between Cb cloud characteristics and precipitation remains unclear. Accordingly, the primary objective of this study was to thoroughly analyze the relationship between Cb cloud features and precipitation both at the pixel and cloud patch scale, and then to apply it in precipitation estimation in the Huaihe River Basin using China's first operational geostationary meteorological satellite, FengYun-2C (FY-2C), and the hourly precipitation data of 286 gauges from 2007. First, 31 Cb parameters (14 parameters of three pixel features and 17 parameters of four cloud patch features) were extracted based on a Cb tracking method using an artificial neural network (ANN) cloud classification as a pre-processing procedure to identify homogeneous Cb patches. Then, the relationship between Cb cloud properties and precipitation was analyzed and applied in a look-up table algorithm to estimate precipitation. The results were as follows: (1) Precipitation increases first and then declines with increasing values for cold cloud and time evolution parameters, and heavy precipitation may occur not only near the convective center, but also on the front of the Cb clouds on the pixel scale. (2) As for the cloud patch scale, precipitation is typically associated with cold cloud and rough cloud surfaces, whereas the coldest and roughest cloud surfaces do not correspond to the strongest rain. Moreover, rainfall has no obvious relationship with the cloud motion features and varies significantly over different life stages. The involvement of mergers and splits of minor Cb patches is crucial for precipitation processes. (3) The correlation coefficients of the estimated rain rate and gauge rain can reach 0.62 in the cross-validation period and 0.51 in the testing period, which indicates the feasibility of the further application of the relationship in precipitation estimation. © 2014 by the authors."
"7005181100;7102079222;54681288700;6506197137;57203025969;","Neural network-based cloud detection/classification using textural and spectral features",1996,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-0029696432&partnerID=40&md5=7934a385ab18f7c55318f0571b8430f0","An efficient and robust neural network-based scheme is introduced in this paper to perform automatic cloud detection and classification. An unsupervised Kohonen neural network was used to classify the cloud contents of a 8×8 blocks in an image into ten different cloud classes. Inputs to the network consisted of textural features of each block obtained using an efficient feature extraction scheme namely the Wavelet Transform (WT). This scheme not only reduces the dimensionality of the data but also extracts useful features of the data. To improve the detection rate and reduce the false positive rate, especially for low clouds and thin high clouds, a multi-channel fusion system was constructed to combine the results of different optical bands. An alternative approach for automatic cloud detection/classification based on multi-spectral features was also studied to analyze and compare the effectiveness of multi-spectral-based scheme vs textural-based scheme. The results using high resolution GOES 8 data show the promise of the Kohonen neural network when used in conjunction with WT as feature extractor for cloud detection/classification."
"57201549485;7003656992;","Creating 3D city models with building footprints and LIDAR point cloud classification: A machine learning approach",2019,"10.1016/j.compenvurbsys.2019.01.004","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85060525776&doi=10.1016%2fj.compenvurbsys.2019.01.004&partnerID=40&md5=56d4e0f32197f36009e19d338a596220","The increasing availability of open geospatial data, such as building footprint vector data and LiDAR (Light Detection and Ranging) point clouds, has provided opportunities to generate large-scale 3D city models at low cost. However, using unclassified point clouds with building footprints to estimate building heights may yield erroneous results due to potential errors and anomalies in both datasets and their integration. Some of the points within footprints often reflect irrelevant objects other than roofs, leading to biases in height estimation, and few studies have developed systematic methods to filter them out. In this paper, a LiDAR point classification methodology is proposed that extracts only rooftop points for building height estimation. The LiDAR points are characterized by point, footprint, and neighborhood-based features and classified by the Random Forest (RF) algorithm into four classes – rooftop, wall, ground, and high outlier. The percentage of correctly classified points among 15,577 sample points in Columbus, Ohio, amounts to 96.5%. Conducting this classification separately for different building types (commercial, residential, skyscraper, and small constructions) does not significantly change the overall accuracy. The footprint-based features contribute most to predicting the classes correctly. Height validation results based on a sample of 498 buildings show that (1) using average or median heights with classified points provides the best estimates, minimizing the disparities between computed heights and ground truth and (2) the RF method yields outcomes much closer to ground truth than earlier classification approaches. Some outcomes are visualized in 3D format using Google Earth 3D Imagery and ArcScene. © 2019 Elsevier Ltd"
"7402934750;7003663305;56899043400;","Characteristic atmospheric radiative heating rate profiles in arctic clouds as observed at Barrow, Alaska",2018,"10.1175/JAMC-D-17-0252.1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85047357663&doi=10.1175%2fJAMC-D-17-0252.1&partnerID=40&md5=1dd91d0538e8632e4a4d2c6ae3c85f8e","A2-yr cloud microphysical property dataset derived from ground-based remote sensors at the Atmospheric Radiation Measurement site near Barrow, Alaska, was used as input into a radiative transfer model to compute radiative heating rate (RHR) profiles in the atmosphere. Both the longwave (LW; 5-100 μm) and shortwave (SW; 0.2-5 μm) RHR profiles show significant month-to-month variability because of seasonal dependence in the vertical profiles of cloud liquid and ice water contents, with additional contributions from the seasonal dependencies of solar zenith angle, water vapor amount, and temperature. The LW and SW RHR profiles were binned to provide characteristic profiles as a function of cloud type and liquid water path (LWP). Single-layer liquid-only clouds are shown to have larger (10-30Kday-1) LWradiative cooling rates at the top of the cloud layer than single-layer mixed-phase clouds; this is due primarily to differences in the vertical distribution of liquid water between the two classes. However, differences in SW RHR profiles at the top of these two classes of clouds are less than 3Kday-1. The absolute value of the RHR in single-layer ice-only clouds is an order of magnitude smaller than in liquid-bearing clouds. Furthermore, for double-layer cloud systems, the phase and condensed water path of the upper cloud strongly modulate the radiative cooling both at the top and within the lower-level cloud. While sensitivity to cloud overlap and phase has been shown previously, the characteristic RHR profiles are markedly different between the different cloud classifications. © 2018 American Meteorological Society."
"56591585100;24398842400;6701378450;","Investigating the contribution of secondary ice production to in-cloud ice crystal numbers",2017,"10.1002/2017JD026546","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85029215159&doi=10.1002%2f2017JD026546&partnerID=40&md5=3b50b834d9ae2823e7755aa9bf76a782","In-cloud measurements of ice crystal number concentration can be orders of magnitude higher than the precloud ice nucleating particle number concentration. This disparity may be explained with secondary ice production processes. Several such processes have been proposed, but their relative importance and even the exact physics are not well known. In this work, a six-hydrometeor-class parcel model is developed to investigate the ice crystal number enhancement, both its bounds and its value for different cloud states, from rime splintering and breakup upon graupel-graupel collision. The model also includes ice aggregation and droplet coalescence, ice hydrometeor nonsphericity, and a time delay formulation for hydrometeor growth. Conditions to maximize the breakup contribution, as well as the effects of nonsphericity and turbulence, are discussed. We find that the largest enhancement of ice crystal number occurs for “intermediate” conditions, characterized by moderate updrafts and activation and nucleation rates. In this case, vertical motion is strong enough, and new hydrometeor formation limited enough, to sustain supersaturation as hydrometeors grow to larger sizes. After these larger hydrometeors form at sufficient number concentrations, the ice crystal number can be enhanced by a factor of 104 in some cases relative to the number generated by primary ice nucleation alone. Excluding ice hydrometeor nonsphericity limits secondary production significantly, and the parcel updraft can modulate it by about an order of magnitude. ©2017. American Geophysical Union. All Rights Reserved."
"57209423225;7004082496;36341884100;","A hybrid semantic point cloud classification-segmentation framework based on geometric features and semantic rules [HybridesFramework zur semantischen Klassifikation und Segmentierung von Punktwolken basierend auf geometrischen Merkmalen und semantischen Regeln]",2017,"10.1007/s41064-017-0020-5","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85040657824&doi=10.1007%2fs41064-017-0020-5&partnerID=40&md5=44d186d1db143edd7eca2e9f82f39713","In this paper, we focus on semantic point cloud classification taking into account standard failure cases reported in a variety of investigations. We present a hybrid two-step framework integrating classification, segmentation and semantic rules in a common end-to-end processing pipeline from irregularly distributed points to semantically labelled point clouds. The first step of our framework consists of a point-wise semantic point cloud classification based on rather intuitive, handcrafted, low-level geometric features extracted from local neighbourhoods of locally adaptive size. The second step of our framework consists of refining the point-wise classification results by considering semantic rules applied to geometric features extracted on the basis of an over-segmentation of the derived class-wise point clouds. We demonstrate the performance of our framework on a standard benchmark dataset for which we obtain a semantic labelling of high accuracy and high plausibility. © Deutsche Gesellschaft für Photogrammetrie, Fernerkundung und Geoinformation (DGPF) e.V. 2017."
"55132275700;57196612094;57190220459;14064801700;16639469300;23005893600;","Total canopy transmittance estimated from small-footprint, full-waveform airborne LiDAR",2017,"10.1016/j.isprsjprs.2017.03.008","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85016135685&doi=10.1016%2fj.isprsjprs.2017.03.008&partnerID=40&md5=f5d0de224000cb2391a05f97b386da68","Canopy transmittance is a directional and wavelength-specific physical parameter that quantifies the amount of radiation attenuated when passing through a vegetation layer. The parameter has been estimated from LiDAR data in many different ways over the years. While early LiDAR methods treated each returned echo equally or weighted the echoes according to their return order, recent methods have focused more on the echo energy. In this study, we suggest a new method of estimating the total canopy transmittance considering only the energy of ground echoes. Therefore, this method does not require assumptions for the reflectance or absorption behavior of vegetation. As the oblique looking geometry of LiDAR is explicitly considered, canopy transmittance can be derived for individual laser beams and can be mapped spatially. The method was applied on a contemporary full-waveform LiDAR data set collected under leaf-off conditions and over a study site that contains two sub regions: one with a mixed (coniferous and deciduous) forest and another that is predominantly a deciduous forest in an alluvial plain. The resulting canopy transmittance map was analyzed for both sub regions and compared to aerial photos and the well-known fractional cover method. A visual comparison with aerial photos showed that even single trees and small canopy openings are visible in the canopy transmittance map. In comparison with the fractional cover method, the canopy transmittance map showed no saturation, i.e., there was better separability between patches with different vegetation structure. © 2017 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS)"
"55561559600;57201378591;57201373924;57201372901;6507534695;","CLASSIFICATION of AERIAL PHOTOGRAMMETRIC 3D POINT CLOUDS",2017,"10.5194/isprs-annals-IV-1-W1-3-2017","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85029759044&doi=10.5194%2fisprs-annals-IV-1-W1-3-2017&partnerID=40&md5=8bd2fae7e1dc4b8dd2c9f931e214a7da","We present a powerful method to extract per-point semantic class labels from aerial photogrammetry data. Labelling this kind of data is important for tasks such as environmental modelling, object classification and scene understanding. Unlike previous point cloud classification methods that rely exclusively on geometric features, we show that incorporating color information yields a significant increase in accuracy in detecting semantic classes. We test our classification method on three real-world photogrammetry datasets that were generated with Pix4Dmapper Pro, and with varying point densities. We show that off-the-shelf machine learning techniques coupled with our new features allow us to train highly accurate classifiers that generalize well to unseen data, processing point clouds containing 10 million points in less than 3 minutes on a desktop computer. © 2017 Copernicus GmbH. All rights reserved."
"55999772700;35345247500;56094601600;24281186100;","Extreme precipitation on the Island of Madeira on 20 February 2010 as seen by satellite passive microwave sounders",2013,"10.5721/EuJRS20134628","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84879824582&doi=10.5721%2fEuJRS20134628&partnerID=40&md5=5df183c6921148af4a06af5e58d8f29f","Extreme rainfall on the Island of Madeira on 20 February 2010 triggered flash floods and mudslides with 45 casualties, 8 missing people, and 100 injured. The NE-moving frontal system originating from a low-pressure center in the Madeira Archipelago is not unusual for the area, but its consequences on the island were rather extreme. The study dwells on passive microwave sounders from polar orbiters for the retrieval of rainfall intensity and cloud classification. Heavy rainfall was generated by severe local convection and enhanced over the central mountain chain. Physical cloud classification identifies the shallow convective precipitation type lasting for a few hours around noon and the observations confirm the numerical model results."
"54407530100;","A system based on ratio images and quick probabilistic neural network for continuous cloud classification",2011,"10.1109/TGRS.2011.2153863","https://www.scopus.com/inward/record.uri?eid=2-s2.0-82155185394&doi=10.1109%2fTGRS.2011.2153863&partnerID=40&md5=59061ecf89a43b8e54a3a3f9793d573b","An approach for continuous daytime cloud classification system through satellite images is presented. The system is based on spectral ratio values as input features and a modified version of probabilistic neural network (PNN), named Quick PNN (QPNN), as a classifier. The use of spectral ratio values makes the system more efficient in detecting the minor changes in cloud spectral properties, leading to better classification capability. The modification to PNN consists of shrinking the hidden layer which is accomplished by performing K-means clustering on the training data of each class separately. Thus, for each class, instead of presenting all the training data samples in the hidden layer nodes, only the means of the resultant clusters are presented. The training data and the class labels are derived through the generation and interpretation of ratio images. The application of the approach to Meteosat-8 images has shown the separation of eight classes, including low clouds, middle clouds, high clouds, areas of high water vapor, sea surface, and land. The average accuracy of the system is 87.15% with a range of 84%-91% for the cloud and area of high water vapor classes, 93% for sea surface class, and 85% for land surface class. The computation time of the classification mode, including image ratioing and QPNN operations, is less than 1 min, which is good for continuous cloud classification and monitoring. The approach can be adapted to any multichannel satellite sensor only by using proper combination of ratio images. © 2011 IEEE."
"55495868700;56267759500;24833754500;","Whole sky infrared remote sensing of cloud",2011,"10.1016/j.proeps.2011.09.044","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84863126775&doi=10.1016%2fj.proeps.2011.09.044&partnerID=40&md5=fa63b94128541e8c3f4b01fb2c162866","Clouds are important factors in weather and climate change. Cloud amount, type and height are measured by means of both visual observation on ground and satellites ever before. In recent years, instruments of measuring clouds on ground have been developed. This paper introduces our progress on ground based whole sky infrared remote sensing of cloud. Some results are given. A method for determining clear sky radiance threshold was suggested, and cloud identification combined threshold method with texture method was discussed. An algorithm retrieving cloud base height from downwelling infrared radiance was suggested. Cloud classification of ground based whole sky cloud images was discussed. Structural features are better than texture features in classifying clouds. © 2011 Published by Elsevier Ltd."
"56207864000;36920327800;6603903004;","Pre-processing procedures for raw point clouds from terrestrial laser scanners",2007,"10.1080/14498596.2007.9635123","https://www.scopus.com/inward/record.uri?eid=2-s2.0-38549099135&doi=10.1080%2f14498596.2007.9635123&partnerID=40&md5=faeb1a6875b00fb1ff572dc177f52e1b","The use of geometric primitives such as geometric curvature, variances of curvature and surface normal vectors as pre-processing methods for edge and boundary detection in three-dimensional (3D) unorganised point clouds is proposed. These processes are important for high-level procedures such as registration, segmentation, classification and detection of specific shapes or objects in point clouds. To demonstrate the effectiveness of these methods, we present examples of tree detection and segmentation of terrestrial laser scanner point clouds. © 2007, Taylor & Francis Group, LLC."
"57214531307;7004489385;7402296705;7201863347;8722458900;","Rapid response for cloud monitoring through Meteosat VIS-IR and NOAA-A/TOVS image fusion: Civil aviation application. A first approach to MSG-SEVIRI",2005,"10.1080/01431160512331326602","https://www.scopus.com/inward/record.uri?eid=2-s2.0-19944399114&doi=10.1080%2f01431160512331326602&partnerID=40&md5=d497fbfedf4083332c2273715887a4a6","The aim of this work is to show an automatic method of cloud classification for direct application in civil aviation. We start from the premise of an acceptable trade-off between calculation speed and accuracy in the output data. For this reason, visible and infrared channels of the Meteosat satellite were used alongside data provided by the A/TOVS (Advanced/Tiros-N Operational Vertical Sounder) probe onboard NOAA (National Oceanic and Atmospheric Administration) polar satellites. A historical database of mean temperatures at ground level was also used. The analysis of different significant synoptic and mesoscale situations highlighted the efficacy of this method in the representation of the different cloud structures that normally appear in these situations. Considering the results of the study and given its speed and accuracy, it can be concluded that the method is appropriate for monitoring cloud systems in real time. © 2005 Taylor & Francis Group Ltd."
"6602563767;57210074890;","Classification of thunderstorms over India using multiscale analysis of AMSU-B images",2004,"10.1175/1520-0450(2004)043<0595:COTOIU>2.0.CO;2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-2342648770&doi=10.1175%2f1520-0450%282004%29043%3c0595%3aCOTOIU%3e2.0.CO%3b2&partnerID=40&md5=44efce5328f9d5a4e143584ca14fa0fa","The structure of thunderstorms has been studied for a long time. In the absence of radar coverage, only high-resolution multifrequency satelliteborne sensors of longer wavelengths (i.e., microwaves) can show structures inside thunderstorms. The National Oceanic and Atmospheric Administration (NOAA) Advanced Microwave Sounding Unit-B (AMSU-B), with five frequencies and 16-km resolution, is now capable of looking at thunderstorm structure. To analyze cloud structure, a tool that can separate regions by size is needed. The à trous wavelet transform, a discrete approximation to the continuous wavelet transform, is such a tool. Images, as well as their wavelet components, may be noisy. To remove noise from wavelet components, those smaller than one standard deviation (of the wavelet image) are equated to zero. This is most suitable for meteorological studies. Images at an appropriate wavelet scale are used for the analysis of thunderstorms. Thunderstorm structures show mostly in scales 2 and 3 (sizes less than 32 and 64 km, respectively) of the à trous transformed images. Other cloud classes are seen either in smaller or larger scales. Given the resolution of the images, three parts of the thunderstorms, namely, the cumulonimbus towers, detraining altostratus, and cirrus anvils, are separated. Thunderstorms in the Indian subcontinent and adjoining seas are grouped according to six classes of wind profiles obtained in this region. Different organizations of towers, altostratus, and cirrus anvils emerged in the AMSU-B images of these six classes. © 2004 American Meteorological Society."
"55738957800;6507789680;57206029166;","Spatial characteristics of the tropical cloud systems: Comparison between model simulation and satellite observations",1999,"10.3402/tellusa.v51i5.14502","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0033371702&doi=10.3402%2ftellusa.v51i5.14502&partnerID=40&md5=2e6c26d8f8f337b7382c34d5d1e31a66","A Lagrangian cloud classification algorithm is applied to the cloud fields in the tropical Pacific simulated by a high-resolution regional atmospheric model. The purpose of this work is to assess the model's ability to reproduce the observed spatial characteristics of the tropical cloud systems. The cloud systems are broadly grouped into three categories: deep clouds, mid-level clouds and low clouds. The deep clouds are further divided into mesoscale convective systems and non-mesoscale convective systems. It is shown that the model is able to simulate the total cloud cover for each category reasonably well. However, when the cloud cover is broken down into contributions from cloud systems of different sizes, it is shown that the simulated cloud size distribution is biased toward large cloud systems, with contribution from relatively small cloud systems significantly under-represented in the model for both deep and mid-level clouds. The number distribution and area contribution to the cloud cover from mesoscale convective systems are very well simulated compared to the satellite observations, so are low clouds as well. The dependence of the cloud physical properties on cloud scale is examined. It is found that cloud liquid water path, rainfall, and ocean surface sensible and latent heat fluxes have a clear dependence on cloud types and scale. This is of particular interest to studies of the cloud effects on surface energy budget and hydrological cycle. The diurnal variation of the cloud population and area is also examined. The model exhibits a varying degree of success in simulating the diurnal variation of the cloud number and area. The observed early morning maximum cloud cover in deep convective cloud systems is qualitatively simulated. However, the afternoon secondary maximum is missing in the model simulation. The diurnal variation of the tropospheric temperature is well reproduced by the model while simulation of the diurnal variation of the moisture field is poor. The implication of this comparison between model simulation and observations on cloud parameterization is discussed."
"57199226600;37033273500;56093041400;","Man-computer interactive method on cloud classification based on bispectral satellite imagery",1997,"10.1007/s00376-997-0058-1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0344040934&doi=10.1007%2fs00376-997-0058-1&partnerID=40&md5=c5a586c6de4d8e259a7bd744764fe95e","A bispectral cloud classification method based on man-computer interactive way, i.e. a unit feature space classification method (UFSCM), has been presented in this paper. Apart from land and water, six types of clouds including cumulonimbus, multilayer cloud system, thin/think cirrus, middle and low level clouds are recognized. The method has been tested by using more than two hundred samples, with total accuracy reaching 87.1%."
"8277424000;57193951496;7401526171;8632797000;","Probabilistic precipitation rate estimates with space-based infrared sensors",2018,"10.1002/qj.3243","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85042346233&doi=10.1002%2fqj.3243&partnerID=40&md5=8533a8e3b1374b01ce34f3c67bc64646","The uncertainty structure of satellite-based passive infrared quantitative precipitation estimation (QPE) is largely unknown at fine spatio-temporal scales, and requires more than just one deterministic “best estimate” to adequately cope with the intermittent, highly skewed distribution that characterizes precipitation. An investigation of this subject has been carried out within the framework of the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS). A new method, PIRSO (Probabilistic QPE using InfraRed Satellite Observations), is proposed to advance the use of uncertainty as an integral part of QPE. Probability distributions of precipitation rates are computed instead of deterministic values using a model quantifying the relation between satellite infrared brightness temperatures and the corresponding “true” precipitation rate. Ensembles of brightness temperatures-to-precipitation rate relationships are derived at a 30 min/0.04° scale. This approach conditions probabilistic quantitative precipitation estimates (PQPE) on the precipitation rate and typology. PIRSO's components were estimated based on a data sample covering two warm seasons over the conterminous USA. Precipitation probability maps outperform the deterministic PERSIANN-CCS QPE. PIRSO is shown to mitigate systematic biases from deterministic retrievals, quantify uncertainty, and advance the monitoring of precipitation extremes. It also provides the basis for precipitation probability maps and satellite precipitation ensembles needed for satellite multi-sensor merging of precipitation, early warning and mitigation of hydrometeorological hazards, and hydrological modelling. © 2018 Royal Meteorological Society"
"16402542500;14319476700;25932356800;","Estimation of hydromorphological attributes of a small forested catchment by applying the Structure from Motion (SfM) approach",2018,"10.1016/j.jag.2018.02.015","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85048294573&doi=10.1016%2fj.jag.2018.02.015&partnerID=40&md5=f448a73d2e9df0d0d8b3851ce538a6a3","Structure from Motion (SfM) represents a good low-cost alternative to generate high resolution topography where LiDAR (Light Detection and Ranging) data is scarce or unaffordable. In this work, we demonstrate the advantages of high resolution elevation models (DEM) obtained using the SfM technique to delineate catchment boundaries and the stream network. The SfM-based DEM was compared with LiDAR data, distributed by the Mexican Government, and a previous high resolution topographic map generated by a RTK-GPS system. Aerial images were collected on a forested ecohydrological monitoring site in northwest Mexico using a commercial grade digital camera attached to a tethered helium balloon. Here we applied the SfM method with the removal of the vegetation, similarly to the more advance LiDAR methods. This was achieved by adjusting the point cloud classification parameters (maximum angle, maximum distance and cell size), which to our knowledge, has not has not been reported in the available SfM literature. The SfM terrain model showed minimal differences in ground elevation in the center of the image domain (0-0.5 m) while errors increased on the edges of the domain. The SfM model generated the largest catchment area, main and total channel length (1.07 ha, 106.1 and 223 m, respectively) while LiDAR model obtained the smallest area and main channel length (0.77 ha and 92.9 m, respectively). On the other hand, the SfM model had a better and accurate representation of the river network among all models evaluated due to its closest proximity to the observed GPS-tracked main channel. We concluded that the integration of low cost unmanned aerial vehicles and the SfM method is a good alternative to estimate hydro-morphological attributes in small catchments. Furthermore, we found that high resolution SfM-based terrain models had a fairly good representation of small catchments which is useful in regions with limited data availability. The main findings of this research provide scientific value within the field of hydrological remote sensing in particular in the acquisition of high resolution topography in remote areas without access to more expensive LiDAR or survey techniques. High resolution DEMs allow for a better characterization of catchment area size and stream network delineation which influence hydrological processes (i.e. soil moisture redistribution, runoff, ET). © 2018 Elsevier B.V."
"55851633600;26533129200;7102314226;7003663305;7402934750;","Improved cloud-phase determination of low-level liquid and mixed-phase clouds by enhanced polarimetric lidar",2018,"10.5194/amt-11-835-2018","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85042099093&doi=10.5194%2famt-11-835-2018&partnerID=40&md5=b265fa30f53a8e99c7705bb8194aeeaa","The unambiguous retrieval of cloud phase from polarimetric lidar observations is dependent on the assumption that only cloud scattering processes affect polarization measurements. A systematic bias of the traditional lidar depolarization ratio can occur due to a lidar system's inability to accurately measure the entire backscattered signal dynamic range, and these biases are not always identifiable in traditional polarimetric lidar systems. This results in a misidentification of liquid water in clouds as ice, which has broad implications on evaluating surface energy budgets. The Clouds Aerosol Polarization and Backscatter Lidar at Summit, Greenland employs multiple planes of linear polarization, and photon counting and analog detection schemes, to self evaluate, correct, and optimize signal combinations to improve cloud classification. Using novel measurements of diattenuation that are sensitive to both horizontally oriented ice crystals and counting system nonlinear effects, unambiguous measurements are possible by over constraining polarization measurements. This overdetermined capability for cloud-phase determination allows for system errors to be identified and quantified in terms of their impact on cloud properties. It is shown that lidar system dynamic range effects can cause errors in cloud-phase fractional occurrence estimates on the order of 30ĝ€% causing errors in attribution of cloud radiative effects on the order of 10-30ĝ€%. This paper presents a method to identify and remove lidar system effects from atmospheric polarization measurements and uses co-located sensors at Summit to evaluate this method. Enhanced measurements are achieved in this work with non-orthogonal polarization retrievals as well as analog and photon counting detection facilitating a more complete attribution of radiative effects linked to cloud properties."
"56250119900;6602926744;56350972400;55796882100;57192180109;","An automated satellite cloud classification scheme using self-organizing maps: Alternative ISCCP weather states",2016,"10.1002/2016JD025199","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84999664651&doi=10.1002%2f2016JD025199&partnerID=40&md5=edc73f4957a638109d493ff1f3ab435e","This study explores the application of the self-organizing map (SOM) methodology to cloud classification. In particular, the SOM is applied to the joint frequency distribution of the cloud top pressure and optical depth from the International Satellite Cloud Climatology Project (ISCCP) D1 data set. We demonstrate that this scheme produces clusters which have geographical and seasonal patterns similar to those produced in previous studies using the k-means clustering technique but potentially provides complementary information. For example, this study identifies a wider range of clusters representative of low cloud cover states with distinct geographic patterns. We also demonstrate that two rather similar clusters, which might be considered the same cloud regime in other classifications, are distinct based on the seasonal variation of their geographic distributions and their cloud radiative effect in the shortwave. Examination of the transitions between regimes at particular geographic positions between one day and the next also shows that the SOM produces an objective organization of the various cloud regimes that can aid in their interpretation. This is also supported by examination of the SOM’s Sammon map and correlations between neighboring nodes geographic distributions. Ancillary ERA-Interim reanalysis output also allows us to demonstrate that the clusters, identified based on the joint histograms, are related to an ordered continuum of vertical velocity profiles and two-dimensional vertical velocity versus lower tropospheric stability histograms which have a clear structure within the SOM. The different nodes can also be separated by their longwave and shortwave cloud radiative effect at the top of the atmosphere. © 2016. American Geophysical Union. All Rights Reserved."
"57214541881;27267529400;","Investigation of the sulphate-induced freezing inhibition effect from CloudSat and CALIPSO measurements",2010,"10.1029/2010JD013905","https://www.scopus.com/inward/record.uri?eid=2-s2.0-78649514313&doi=10.1029%2f2010JD013905&partnerID=40&md5=ce34d6e804bd1e0a6db4c4e460430b25","The hypothesis according to which higher sulphate concentrations favor ice clouds made of larger ice crystals is tested using data sets from the CloudSat and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellites. This is a potential consequence of the sulphate-induced freezing inhibition (SIFI) effect, namely, the hypothesis that sulphates contribute to inhibit the onset of ice crystal formation by deactivating ice-forming nuclei during Arctic winter. A simple index based on the backscattering at 532 nm and the color ratio from the CALIPSO lidar measurements is compared against in situ sulphate concentration time series and used as a proxy for this variable. An algorithm using the lidar data and the CloudSat radar microphysical retrievals is also developed for identifying cloud types, focusing on those supposedly favored by the SIFI effect. The analysis includes the effect of the lidar off-nadir angle on the sulphate index and the cloud classification, the validation of the index, as well as the production of circum-Arctic maps of the sulphate index and of the SIFI-favored clouds fraction. The increase of the lidar off-nadir angle is shown to cause an increase in the measured depolarization ratio and hence in the ability to detect ice crystals. The index correlates positively with both sulphates and sea salt concentrations, with a Pearson correlation coefficient (R2) varying from 0.10 to 0.42 for the different comparisons performed. Ultimate findings are the results of two correlation tests of the SIFI effect, which allow for a new outlook on its possible role in the Arctic troposphere during winter. © 2010 by the American Geophysical Union."
"7102881144;23097769200;7005104188;","Fog and stratus formation on the coast of Brazil",2008,"10.1016/j.atmosres.2007.11.008","https://www.scopus.com/inward/record.uri?eid=2-s2.0-39149122186&doi=10.1016%2fj.atmosres.2007.11.008&partnerID=40&md5=6b4c4b65b93c10e1d195e2e301a10461","Abstract: The physical and synoptic processes of fog and stratus cloudiness formation at the northern and southern coasts of Brazil were investigated. The frequencies of both phenomena were higher at the southern coast. The synoptic situation patterns for fog/stratus formation were identified using the following products of NCEP reanalysis: streamlines, pressure, omega and relative humidity at different levels. Fog/stratus formation is associated, in general, with a wave disturbance in the trade winds (WDTW) field at the north coast. Moreover, stratus clouds were observed on the cold front periphery, Easterly wave and under the Upper Tropospheric Cyclonic Vortex. The principal synoptic processes of fog formation at the southern coast are a High dislocation along the east coast of South America and a warm core barotropic Low occurrence north of Argentina. Fog formation is initiated between these two synoptic systems. There were 1 or 2 h of fog duration at the north coast and on average 12 h at the south coast. A variety of meteorological elements and phenomena were studied before and during the fog/stratus days, and results of an atmosphere instability analysis are presented. A classification of vertical profiles (from NCEP reanalysis data) of temperature and dew-point for different fog/stratus physical processes was also developed. At the north coast no stable layers in the vertical profiles were observed and the humid layer was very narrow in all cases. An intense inversion or stable layer with a high humidity up to 950-670 hPa is responsible for fog development at the south coast. © 2007 Elsevier B.V. All rights reserved."
"7003685783;6506887943;6603518408;","Mesoscale cloud pattern classification over ocean with a neural network using a new index of cloud variability",2006,"10.1080/01431160500192512","https://www.scopus.com/inward/record.uri?eid=2-s2.0-33747021145&doi=10.1080%2f01431160500192512&partnerID=40&md5=6a3965b9c8367ddceb6f114ce258a87d","The purpose of this study is to determine the feasibility of a mesoscale (&lt;300 km) cloud classification using infrared radiance data of satellite-borne instruments. A new method is presented involving an index called the diversity index (DI), derived from a parameter commonly used to describe ecosystem variability. In this respect, we consider several classes of value ranges of standard deviation of the brightness temperature at 11 μm (σBT). In order to calculate DI for 128 × 128 km2 grids, subframes of 8 km × 8 km are superimposed to the satellite image, and then σBT is calculated for all 256 subframes and assigned to one of the classes. Each observed cloud pattern is associated with an index characterized by the frequency of σBT classes within the scene, representative of a cloud type. Classification of different clouds is obtained from Advanced Very High Resolution Radiometer (AVHRR)-NOAA 16 data at 1 km resolution. Stratus, stratocumulus and cumulus are specifically recognized by this window analysis using a DI threshold. Then, a six-class scheme is presented, with the standard deviation of the infrared brightness temperature of the entire cloud scene (σc) and DI as inputs of a neural network algorithm. This neural network classifier achieves an overall accuracy of 77.5% for a six-class scheme, and 79.4% for a three-class scheme, as verified against the analyses of nephanalists as verified against a cloud classification from Météo France. As an application of the proposed methodology, regional cloud variability over Pacific is examined using cloud patterns derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) carried aboard Earth Observing System (EOS) Terra polar orbiter platform, for February 2003 and 2004. The comparison shows regional change in monthly mean cloud types, associated with 2003 El Niño and 2004 neutral events. A significant increase in the occurrence of convective clouds (+15%) and a decrease in stratiform clouds (-10%) are observed between the two months."
"6603250042;","Optical thickness of stratiform clouds over the Baltic inferred from on-board irradiance measurements",2004,"10.1016/j.atmosres.2004.03.012","https://www.scopus.com/inward/record.uri?eid=2-s2.0-8644282722&doi=10.1016%2fj.atmosres.2004.03.012&partnerID=40&md5=6186eb866381436dc439f7ce52d0c988","The paper discusses the optical thickness of non-precipitating layer clouds retrieved from irradiance measurements collected during cruises of R/V Oceania to the Baltic Sea from 1994 to 2002. Pyranometer measurements were accompanied by standard meteorological observations. Cloud optical thickness was obtained by a comparison of downward irradiance in the visible part of the spectrum averaged over 1-min intervals with MODTRAN4 (MOD4v2r1) computations. For an individual cloud retrieval, the total statistical error varies from 28% to over 100%. This is mainly attributed to irradiance measurement error, uncertainties in aerosol optical thickness, the lack of information on cloud droplet radius and the assumption of cloud horizontal uniformity. The systematic error (bias) due to the plane-parallel assumption is negative (the cloud optical thickness is overestimated) and is estimated at several percent. Statistical analysis of the optical thickness of layer clouds over the Baltic was performed with two purposes in mind: to look for (1) seasonal variations in cloud optical thickness and (2) for differences in cloud optical thickness for various cloud 'classes'. The cloud 'classes' were distinguished with respect to the following: total cloud cover N and low-level cloud cover NL, and cloud type predominating in the sky (SHIP meteorological reports, WMO cloud classification and coding). Cloud optical thickness distributions for low-level layer clouds can be approximated by a lognormal distribution, the parameters of which depend on the cloud class. The mean base-10 logarithm of τ varies from about 1 (τ =10) for semi-transparent clouds and cloud classes with N =7 to 1.5 for the class defined by N = NL=8 (overcast sky) and CL=7 (low-level clouds before or after precipitation). The values obtained are consistent with findings by other researchers. For the joint class containing all cases with N =8, the lowest mean logarithm of τ was found for February (about 1, which corresponds to τ =10) and the highest for the spring months, from March to May (1.3-1.4, which correspond to τ =20-25). However, the low number of data available does not allow for an accurate description of the annual cycle. © 2004 Elsevier B.V. All rights reserved."
"6701607011;6701596624;","Cloud cover analysis with METEOSAT-5 during INDOEX",2001,"10.1029/2001JD900097","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0035688859&doi=10.1029%2f2001JD900097&partnerID=40&md5=f9fb76fec34523b4c0e1e4a61fab5d80","During the Indian Ocean Experiment (INDOEX), METEOSAT-5 positioned at 63°E provided observation of the visible and infrared radiance field over the Indian Ocean. A cloud classification process using the dynamic cluster method is applied to these data. For the 3 months of the experiment (January-March 1999), daily maps of the cloud cover type are built for 0730 and 0900 UTC. The occurrence frequency of clear sky, low-and high-level cloud cover is examined. These frequencies are compared to the International Satellite Cloud Climatology Program (ISCCP) D1 data set for the period 1984 to 1994. The Indian Ocean region can be classified in three zones. In the north subtropics, clear sky and small cumulus occur at least 90% of the time. Near the coast of India, clear sky is as frequent as 80 to 100%. The Intertropical Convergence Zone, characterized by the occurrence frequency of high-level clouds greater than 30%, spreads from Indonesia to North Madagascar. Near Indonesia, high-level cloud cover occurs more than 55% of the time. In the south subtropics, low cloud cover is the most frequent. In the eastern part the occurrence frequency reaches 80%. This percentage decreases along the western side of the ocean where low clouds break up. Between the African coast and Madagascar, high-level clouds are frequent. The mean spatial features found are in agreement with the ISCCP climatology, except for the eastern part of the south subtropics. A regional comparison shows the difficulty of making the analysis of interannual variations of cloud cover obtained from various cloud cover retrievals applied to different satellite data sets. This difficulty arises from the nonneglectable percentage of satellite pixels which can contain some very small low clouds. Copyright 2001 by the American Geophysical Union."
"6503868342;7402546593;7201914101;","An interactive hybrid expert system for polar cloud and surface classification",1992,"10.1002/env.3170030201","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84984474620&doi=10.1002%2fenv.3170030201&partnerID=40&md5=c1c5bdbbd751d51ea823f7a2d4d72436","An interactive hybrid expert system is developed to classify polar scenes using AVHRR LAC imagery. A total of 183 spectral and textural signatures are generated from which the 20 “best” are chosen using the Sequential Forward Selection procedure. These 20 features are used to populate the working memory of the expert system. A probabilistic neural network is used as the inference engine to make probabilistic estimates of class membership. As part of the inference engine, a sophisticated outlier test is performed to provide a measure of classification confidence. During a session, the user is provided with an extensive set of on‐screen aids to assist in labelling. The user may modify the knowledge base by adding new samples to existing classes or by including new classes. The expert system provides confidence measures and a distance measure from the proposed class cluster centre. The interactive environment allows the user to test the impact of class labelling upon the knowledge base before new data is entered. For users working with very large datasets and very complex scenes, the integrity of the knowledge base is the primary concern. A bootstrap method is used to validate classification accuracy. On the basis of 100 bootstrap samples, an overall classification accuracy of 87% is achieved, with a standard deviation of 1%. The result is that much more accurate cloud classification in polar regions now can be made, which will aid us in our monitoring of global climate changes. Copyright © 1992 John Wiley & Sons, Ltd"
"57216226987;57204289996;12781686200;","A geometry-attentional network for ALS point cloud classification",2020,"10.1016/j.isprsjprs.2020.03.016","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082856843&doi=10.1016%2fj.isprsjprs.2020.03.016&partnerID=40&md5=3ec981242fca7959535b62f34c96b98f","Airborne Laser Scanning (ALS) point cloud classification is a critical task in remote sensing and photogrammetry communities, which can be widely utilized in urban management, powerline surveying and forest monitoring, etc. In particular, the characteristics of ALS point clouds are distinctive in three aspects, (1) numerous geometric instances (e.g. tracts of roofs); (2) extreme scale variations between different categories (e.g. car v.s. roof); (3) discrepancy distribution along the elevation, which should be specifically focused on for ALS point cloud classification. In this paper, we propose a geometry-attentional network consisting of geometry-aware convolution, dense hierarchical architecture and elevation-attention module to embed the three characteristics effectively, which can be trained in an end-to-end manner. Evaluated on the ISPRS Vaihingen 3D Semantic Labeling benchmark, our method achieves the state-of-the-art performance in terms of average F1 score and overall accuracy (OA). Additionally, without retraining, our model trained on the above Vaihingen 3D dataset can also achieve a better result on the dataset of 2019 IEEE GRSS Data Fusion Contest 3D point cloud classification challenge (DFC 3D) than the baseline (i.e. PointSIFT), which verifies the stronger generalization ability of our model. © 2020 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS)"
"7006367445;8324804800;57189883537;","Automatic land-water classification using multispectral airborne LiDAR data for near-shore and river environments",2019,"10.1016/j.isprsjprs.2019.04.005","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85063991486&doi=10.1016%2fj.isprsjprs.2019.04.005&partnerID=40&md5=c79443e35057356ec1410e39b037c554","Rapid mapping of near-shore and coastal regions has become an indispensable task for the local authority to serve the purpose of coastal management and post-disaster monitoring. Aerial photogrammetry and satellite remote sensing have been utilized to fulfill such a task in the last few decades. Airborne LiDAR can further compensate the drawbacks of these image capturing approaches as a result of the direct geo-referenced 3D point cloud. The recent introduction of multispectral airborne LiDAR, such as the Teledyne Optech Titan, can potentially enhance the capability of water mapping, minimize the involvement of manual intervention and reduce the use of supplementary information or ancillary data. This study demonstrates the use of multispectral airborne LiDAR data for automatic land-water classification under different coastal and inland river environments. Two automatic training data selection methods are proposed. The first method utilizes Gaussian mixture model (GMM) to split preliminarily the land and water region based on the elevation/intensity histogram, and the second method is developed based on the use of scan line intensity-elevation ratio (SLIER). Subsequently, various LiDAR-derived feature sets, particularly based on the multispectral LiDAR intensity, are constructed in order to serve as an input for the log-likelihood classification model. Two optional post-classification enhancements can be implemented to further adjust the misclassified data points. The proposed workflow was evaluated with four Optech Titan datasets collected for different near-shore and river environments that are located nearby Lake Ontario, Ontario, Canada. Our experimental work demonstrated that the multispectral LiDAR intensity data was capable of enhancing the classification capability, where an overall accuracy better than 96% was achieved in most of the cases. © 2019"
"56059425100;7005523706;25228997600;7003836546;","Investigating Satellite Precipitation Uncertainty Over Complex Terrain",2018,"10.1029/2017JD027559","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85047652650&doi=10.1029%2f2017JD027559&partnerID=40&md5=51bc2d2221a5b5e5025428d1d6df1136","The estimation of heavy precipitation events is a particularly difficult task, especially over high mountainous terrain typically associated with scant availability of in situ observations. Therefore, quantification of precipitation variability in such data-limited regions relies on remote sensing estimates, due to their global coverage and near real-time availability. However, strong underestimation of precipitation associated with low-level orographic enhancement often limits the quantitative use of these data in applications. This study utilizes state-of-the-art numerical weather prediction simulations, toward the reduction of quantitative errors in satellite precipitation estimates and an insight on the nature of detection limitations. Satellite precipitation products based on different retrieval algorithms (Climate Prediction Center morphing method, Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Cloud Classification System, and Global Satellite Mapping of Precipitation) are evaluated for their performance in a number of storm events over mountainous areas with distinct storm characteristics: Upper Blue Nile in Ethiopia and Alto Adige in NE Italy. High-resolution (1 and 2 km) simulations from the Regional Atmospheric Modeling System/Integrated Community Limited Area Modeling System are used to derive adjustments to the magnitude of satellite estimates. Finally, a microphysical investigation is presented for occurrences of erroneous precipitation detection from the satellite instruments. Statistical indexes showcase improvement in numerical weather prediction-adjusted satellite products and microphysical commodities among cases of no detection are discussed. ©2018. American Geophysical Union. All Rights Reserved."
"57069455200;36064917000;14064055300;55292994700;57192190518;56222085000;","Joint discriminative dictionary and classifier learning for ALS point cloud classification",2018,"10.1109/TGRS.2017.2751061","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85032437600&doi=10.1109%2fTGRS.2017.2751061&partnerID=40&md5=5e31338f7e202ca286da7c517816e5bc","To efficiently recognize on-ground objects in airborne laser scanning (ALS) point clouds, we design a method that jointly learns a discriminative dictionary and a classifier. In the method, the point cloud is segmented into hierarchical point clusters, which are organized by a tree structure. Then, the feature of each point cluster is extracted. The feature of a leaf node is obtained by aggregating the features of all its parent nodes. The feature of the leaf node is called the hierarchical aggregation feature. The hierarchical aggregation features are encoded by sparse coding. We introduce a new label consistency constraint called ""discriminative sparse-code error,"" and combine it with the reconstruction error, the classification error, and L1-norm sparsity constraint to form a unified objective function. The objective function is efficiently solved by using the proposed label consistency feature sign method. We obtain an overcomplete discriminative dictionary and an optimal linear classifier. Experiments performed on different ALS point cloud scenes have shown that the hierarchical aggregation features combined with the learned classifier can significantly enhance the classification results, and also demonstrated the superior performance of our method over other techniques in point cloud classification. © 2017 IEEE."
"55199339300;55597088322;54789157400;54789185700;24485073500;","Watershed image segmentation and cloud classification from multispectral MSG-SEVIRI imagery",2012,"10.1016/j.asr.2011.09.023","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84155166853&doi=10.1016%2fj.asr.2011.09.023&partnerID=40&md5=2918f68799dbd9bbc7bcd00982b812f8","In this work a technique for cloud detection and classification from MSG-SEVIRI (Meteosat Second Generation-Spinning Enhanced Visible and Infra-red Imager) imagery is presented. It is based on the segmentation of the multispectral images using order-invariant watershed algorithms, which are applied to the corresponding gradient images, computed by a multi-dimensional morphological operator. To reduce the over-segmentation produced by the watershed method, a RAG (Region Adjacency Graph) based region merging technique is applied, using region dissimilarity functions. Once the objects present in the image have been segmented, they are classified using a multi-threshold method based on physical considerations that takes into account the statistical parameters inside each region. © 2011 COSPAR. Published by Elsevier Ltd. All rights reserved."
"56248808000;7102866124;6603778635;6603858896;6507174567;7103185017;57211811048;7003482642;","Basis for a rainfall estimation technique using IR-VIS cloud classification and parameters over the life cycle of mesoscale convective systems",2008,"10.1175/2007JAMC1684.1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-57649155193&doi=10.1175%2f2007JAMC1684.1&partnerID=40&md5=bcaae87c986c4e8a5adb62a30cc667b6","This paper discusses the basis for a new rainfall estimation method using geostationary infrared and visible data. The precipitation radar on board the Tropical Rainfall Measuring Mission satellite is used to train the algorithm presented (which is the basis of the estimation method) and the further intercomparison. The algorithm uses daily Geostationary Operational Environmental Satellite infrared-visible (IR-VIS) cloud classifications together with radiative and evolution properties of clouds over the life cycle of mesoscale convective systems (MCSs) in different brightness temperature (Tb) ranges. Despite recognition of the importance of the relationship between the life cycle of MCSs and the rainfall rate they produce, this relationship has not previously been quantified precisely. An empirical relationship is found between the characteristics that describe the MCSs' life cycle and the magnitude of rainfall rate they produce. Numerous earlier studies focus on this subject using cloud-patch or pixel-based techniques; this work combines the two techniques. The algorithm performs reasonably well in the case of convective systems and also for stratiform clouds, although it tends to overestimate rainfall rates. Despite only using satellite information to initialize the algorithm, satisfactory results were obtained relative to the hydroestimator technique, which in addition to the IR information uses extra satellite data such as moisture and orographic corrections. This shows that the use of IR-VIS cloud classification and MCS properties provides a robust basis for creating a future estimation method incorporating humidity Eta field outputs for a moisture correction, digital elevation models combined with low-level moisture advection for an orographic correction, and a nighttime cloud classification. © 2008 American Meteorological Society."
"6602840804;6604099174;55999772700;","Rain area delineation by means of multispectral cloud characterization from satellite",2008,"10.5194/adgeo-17-43-2008","https://www.scopus.com/inward/record.uri?eid=2-s2.0-47249134921&doi=10.5194%2fadgeo-17-43-2008&partnerID=40&md5=ead2bf6e07077be405539d0a0c8a3c68","The identification of precipitation areas by microwave based rain algorithms can be improved by means of cloud classification schemes based on multispectral observations. Several recent studies have demonstrated the potential of cloud microphysical and optical characterization for the improvement of passive microwave rain estimates, especially in detecting likely precipitating pixels over land. The multispectral sensing capabilities of MODIS onboard Aqua are exploited to characterize the cloudy scenario, using a twofold approach: a) an RGB technique to qualitatively identify the different cloud systems on the basis of the combination of radiances measured in three selected channels, and b) a quantitative description of cloud top in terms of optical thickness (τ), effective radius (Re) and top temperature (T c). The information gathered by the multispectral analysis of the cloud field from MODIS is contrasted with the rain intensity at the ground as derived from the AMSR-E operational algorithm, to assess the statistical relationships between microphysical parameters and the rain intensity for such nearly simultaneous and co-located observations."
"6603402971;6603265747;56213274700;7003426558;","Automatic detection of local cloud systems from MODIS data",2006,"10.1175/JAM2393.1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-33749380542&doi=10.1175%2fJAM2393.1&partnerID=40&md5=2508f246e347348747f4f8ceec1cd5a2","This paper describes an algorithm that is aimed at the identification of cloudy and clear pixels in Moderate-Resolution Imaging Spectroradiometer (MODIS) images to support earth science and nowcasting applications. The process from geolocated and calibrated data allows one to obtain cloud masks with four clear-sky confidence levels for five different cloud system types. The techniques has been developed using the MODIS cloud-mask algorithm heritage, but the threshold tests performed have been executed without comparing solar reflectances and thermal brightness temperatures with thresholds determined in advance, but instead with thresholds carried out from classification methods. The main advantage of this technique is that the thresholds are obtained directly from the images. Seventy-five percent of the spectral signatures (known as end members) derived from the winter images in the detection of the various cloud types and 80% of the summer end members can be considered as being well discriminated. Furthermore, it seems that the end members characterizing the different cloud systems are constant throughout the various seasons of the year (they vary with a confidence level of 60%). whereas those describing clear sky change in notable manner (the associated confidence level is 99%). The algorithm is able to produce cloud masks pertinent to limited regions at a mesoscale level, which may be a key factor for nowcasting purposes. This work shows that the use of end members and spectral angles, as opposed to spectral thresholds, should be carefully examined because of the fact that it might be simpler or that higher performances may be achieved at a regional scale. © 2006 American Meteorological Society."
"55628589750;13007660200;13007555900;36124109400;7005742190;","Characterization of low clouds with satellite and ground-based remote sensing systems",2006,"10.1127/0941-2948/2006/0100","https://www.scopus.com/inward/record.uri?eid=2-s2.0-33646054128&doi=10.1127%2f0941-2948%2f2006%2f0100&partnerID=40&md5=378a0fb64641804f331eba60388c04c5","Satellite and ground-based retrievals of a number of (low) cloud characteristics are compared in this paper in order to assess the performance of the techniques and identify potential synergies. Centred on the COST720 International Comparison Campaign for Temperature, hUmidity and Cloud profiling (TUC), four cases with different meteorological situations are analysed in detail. Parameter agreement (for cloud presence, liquid water path, cloud geometrical thickness and cloud top temperature) is good in general. It is shown that satellite retrievals of liquid water path and cloud thickness could be improved using liquid water content derived from ground-based measurements, while ground-based retrievals can profit from the spatial component in satellite data. Taken together, the combination of instruments and techniques presented in this paper allows for a detailed assessment of complex cloudy atmospheres. © Gebrüder Borntraeger, Berlin, Stuttgart 2006."
"12808931100;6701532356;12809430200;57211811043;","Modelling rainfall intensity from NOAA AVHRR data for operational flood forecasting in Malaysia",2006,"10.1080/01431160500192603","https://www.scopus.com/inward/record.uri?eid=2-s2.0-34250888015&doi=10.1080%2f01431160500192603&partnerID=40&md5=dcbcf95043269888476d5e19032ecbbf","Many empirical studies in numerical weather prediction have been carried out that establish the relationship between top-of-the-cloud brightness temperature and rainfall particularly in tropical and equatorial regions of the world. Malaysia is a tropical country that lies along the path of the north-east and south-west monsoon rainfall, which sometimes causes extensive flood disasters. Observations have generally shown that heavy cumulonimbus cloud formation and thunderstorms precede the usual heavy monsoon rains that cause flood disasters in the region. In this study, a model has been developed to process National Oceanic &amp; Atmospheric Administration Advanced Very High Resolution Radiometer (AVHRR) satellite data for rainfall intensity in an attempt to improve quantitative precipitation forecasting (QPF) as input to operational hydro-meteorological flood early warning. The thermal bands in the multispectral AVHRR data were processed for brightness temperature. Data were further processed to determine cloud height and classification performed to delineate clouds in three broad classes of low, middle, and high. A rainfall intensity of 3-12 mm h-1 was assigned to the 1-D cloud model to determine the maximum rain rate as a function of maximum cloud height and minimum cloud model temperature at a threshold level of 235 K. The result of establishing the rainfall intensity based on top of the cloud brightness temperature was very promising. It also showed a good areal coverage that delineated areas likely to receive intense rainfall on a regional scale. With a spatial resolution of 1.1 km, data are course but provide a good coverage for an average river catchment/basin. This raises the opportunity of simulating rainfall runoff for the river catchment through the coupling of a suitable hydro-dynamic model and GIS to provide early warning prior to the actual rainfall event."
"7007175473;7801440352;","Incorporating satellite observations of 'no rain' in an Australian daily rainfall analysis",1999,"10.1175/1520-0450(1999)038<0044:ISOONR>2.0.CO;2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0032599691&doi=10.1175%2f1520-0450%281999%29038%3c0044%3aISOONR%3e2.0.CO%3b2&partnerID=40&md5=208c0ca225ad85cc660703595b254362","Geostationary satellite observations can be used to distinguish potential rain-bearing clouds from nonraining areas, thereby providing surrogate observations of 'no rain' over large areas. The advantages of including such observations are the provision of data in regions void of conventional rain gauges or radars, as well as the improved delineation of raining from nonraining areas in gridded rainfall analyses. This paper describes a threshold algorithm for delineating nonraining areas using the difference between the daily minimum infrared brightness temperature and the climatological minimum surface temperature. Using a fixed difference threshold of -13 K, the accuracy of 'no rain' detection (defined as the percentage of no-rain diagnoses that was correct) was 98%. The average spatial coverage was 45%, capturing about half of the observed space-time frequency of no rain over Australia. By delineating cool, moderate, and warm threshold areas, the average spatial coverage was increased to 54% while maintaining the same level of accuracy. The satellite no-rain observations were sampled to a density consistent with the existing gauge network, then added to the real-time gauge observations and analyzed using the Bureau of Meteorology's operational three-pass Barnes objective rainfall analysis scheme. When verified against independent surface rainfall observations, the mean bias in the satellite-augmented analyses was roughly half of bias in the gauge-only analyses. The most noticeable impact of the additional satellite observations was a 66% reduction in the size of the data-void regions."
"57212075803;","Two automated methods to derive probability of precipitation fields over oceanic areas from satellite imagery",1989,"10.1175/1520-0450(1989)028<0913:TAMTDP>2.0.CO;2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0024876391&doi=10.1175%2f1520-0450%281989%29028%3c0913%3aTAMTDP%3e2.0.CO%3b2&partnerID=40&md5=1ec559429549da3e19e98b7832f1ab7f","The cloud fields are analyzed at the scale of ~130-150 km from satellite visible and infrared imagery and collocated with ship observations of present weather. Method 1 is based on a detailed cloud classification scheme in 20 classes: a mean PP is determined for each cloud class. Method 2 assigns a PP based on cloud top temperature and mean cloud albedo only. For both methods, a normalization with respect to cloud fraction is applied. For real-time applications, the two methods provide similar results except for some specific cloud classes where maximum differences reach 13%, due to the lower level of classification used in Method 2. On a monthly time scale, the absoluate accuracy of both methods is about 1.2% rms, based on independent data taken during the winters of 1984 (1064 samples) and 1986 (673 samples) over the northwestern Atlantic. -from Author"
"57195135675;6603146282;7004196511;8445905500;","Addressing overfitting on point cloud classification using Atrous XCRF",2019,"10.1016/j.isprsjprs.2019.07.002","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068556361&doi=10.1016%2fj.isprsjprs.2019.07.002&partnerID=40&md5=06762fba0e2ee5207b871cc8d7f41831","Advances in techniques for automated classification of point cloud data introduce great opportunities for many new and existing applications. However, with a limited number of labelled points, automated classification by a machine learning model is prone to overfitting and poor generalization. The present paper addresses this problem by inducing controlled noise (on a trained model) generated by invoking conditional random field similarity penalties using nearby features. The method is called Atrous XCRF and works by forcing a trained model to respect the similarity penalties provided by unlabeled data. In a benchmark study carried out using the ISPRS 3D labeling dataset, our technique achieves 85.0% in term of overall accuracy, and 71.1% in term of F1 score. The result is on par with the current best model for the benchmark dataset and has the highest value in term of F1 score. Additionally, transfer learning using the Bergen 2018 dataset, without model retraining, was also performed. Even though our proposal provides a consistent 3% improvement in term of accuracy, more work still needs to be done to alleviate the generalization problem on the domain adaptation and the transfer learning field. © 2019 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS)"
"22953309600;55705571600;7403079681;55135281000;","Cloud Classification and Distribution of Cloud Types in Beijing Using Ka-Band Radar Data",2019,"10.1007/s00376-019-8272-1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068755123&doi=10.1007%2fs00376-019-8272-1&partnerID=40&md5=b0a30077b2bd41d1ba18ce95f55cc87d","A cloud clustering and classification algorithm is developed for a ground-based Ka-band radar system in the vertically pointing mode. Cloud profiles are grouped based on the combination of a time-height clustering method and the k-means clustering method. The cloud classification algorithm, developed using a fuzzy logic method, uses nine physical parameters to classify clouds into nine types: cirrostratus, cirrocumulus, altocumulus, altostratus, stratus, stratocumulus, nimbostratus, cumulus or cumulonimbus. The performance of the clustering and classification algorithm is presented by comparison with all-sky images taken from January to June 2014. Overall, 92% of the cloud profiles are clustered successfully and the agreement in classification between the radar system and the all-sky imager is 87%. The distribution of cloud types in Beijing from January 2014 to December 2017 is studied based on the clustering and classification algorithm. The statistics show that cirrostratus clouds have the highest occurrence frequency (24%) among the nine cloud types. High-level clouds have the maximum occurrence frequency and low-level clouds the minimum occurrence frequency. © 2019, Institute of Atmospheric Physics/Chinese Academy of Sciences, and Science Press and Springer-Verlag GmbH Germany, part of Springer Nature."
"57206659449;57206131798;57210070038;36701458400;57206674904;57206669311;","Statistical characteristics of raindrop size distribution in south China summer based on the vertical structure derived from VPR-CFMCW",2019,"10.1016/j.atmosres.2019.01.022","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061805492&doi=10.1016%2fj.atmosres.2019.01.022&partnerID=40&md5=2b1e63016c165dff5bb519cec95d07af","The raindrop size distribution (DSD) characteristics during the precipitation season are analyzed using data collected by an OTT Particle Size Velocity (Parsivel) disdrometer and a Vertical Pointing Radar with C-band Frequency Modulation Continuous Wave (VPR-CFMCW) technology. The two datasets were collected at the same site in Longmen, Guangdong Province, which is the precipitation center of south China, from June to July in 2016 and 2017. We evaluate different fitting methods for the gamma model function and choose a nonlinear least-squares method to fit DSD. Based on the radar reflectance obtained by VPR-CFMCW, the precipitating clouds that occur during the summer precipitation season in south China are classified into four types (i.e., convective, stratiform, mixture, and shallow). The characteristic parameters and the gamma model parameters of different precipitation types are compared. Avoiding the limitations of rainfall classification at the surface, the new classification quantifies the characteristics of mixture and shallow precipitation. The results show that the stratiform precipitation makes up 43.1% of the summer precipitation process in south China, and the contribution of convective precipitation to total rainfall is 62.7%. The precipitation parameters of the four types of precipitation, such as the rain rate (R), the mass-weighted mean diameter (D m ), the radar reflectance (Z), and the liquid water content (LWC), follow the pattern: convective &gt; mixture &gt; stratiform &gt; shallow. The DSD characteristics of the four precipitating cloud types are investigated. For the DSD of convective and mixture precipitation, the spectra width is similar but the rain drop concentration of the mixture is smaller. For the DSD of stratiform and shallow clouds, the rain drop concentrations are similar, but the spectra width of the shallow clouds are smaller. In addition, the relationships between μ-Λ, D m -N w , D m -R, and Z-R are obtained. These new relationships will help improve the accuracy of precipitation estimation and deepen the understanding of the characteristics of surface precipitation microphysical parameters for different types of precipitating clouds in south China. © 2019"
"57202643803;6507811592;57203178537;","Machine learning approach to classify rain type based on thies disdrometers and cloud observations",2019,"10.3390/atmos10050251","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074069139&doi=10.3390%2fatmos10050251&partnerID=40&md5=ce95e1eefc2e2a67a2dac83153ab90d0","Rain microstructure parameters assessed by disdrometers are commonly used to classify rain into convective and stratiform. However, different types of disdrometer result in different values for these parameters. This in turn potentially deteriorates the quality of rain type classifications. Thies disdrometer measurements at two sites in Bavaria in southern Germany were combined with cloud observations to construct a set of clear convective and stratiform intervals. This reference dataset was used to study the performance of classification methods from the literature based on the rain microstructure. We also explored the possibility of improving the performance of these methods by tuning the decision boundary. We further identified highly discriminant rain microstructure parameters and used these parameters in five machine-learning classification models. Our results confirm the potential of achieving high classification performance by applying the concepts of machine learning compared to already available methods. Machine-learning classification methods provide a concrete and flexible procedure that is applicable regardless of the geographical location or the device. The suggested procedure for classifying rain types is recommended prior to studying rain microstructure variability or any attempts at improving radar estimations of rain intensity. © 2019 by the authors."
"55815878800;36641896200;6506901550;","Assessment of satellite and radar quantitative precipitation estimates for real time monitoring of meteorological extremes over the southeast of the Iberian Peninsula",2018,"10.3390/rs10071023","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050510329&doi=10.3390%2frs10071023&partnerID=40&md5=e4fa39aa2da5c8c1937d348f60e2bc41","Quantitative Precipitation Estimates (QPEs) obtained from remote sensing or ground-based radars could complement or even be an alternative to rain gauge readings. However, to be used in operational applications, a validation process has to be carried out, usually by comparing their estimates with those of a rain gauges network. In this paper, the accuracy of three QPEs are evaluated for three extreme precipitation events in the last decade in the southeast of the Iberian Peninsula. The first QPE is PERSIANN-CCS (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks - Cloud Classification System), a satellite-based QPE. The second and the third are QPEs from a meteorological radar with Doppler capabilities that works in the C band. Pixel-to-point comparisons are made between the values offered by the QPEs and those obtained by two networks of rain gauges. The results obtained indicate that all the QPEs were well below the rain gauge values in extreme rainfall time slots. There seems to be a weak linear association between the value of the discrepancies and the precipitation value of the QPEs. The main conclusion, assuming the information from the rain gauges as ground truth, is that neither PERSIANN-CCS nor radar, without empirical calibration, are acceptable QPEs for the real-time monitoring of meteorological extremes in the southeast of the Iberian Peninsula. © 2018 by the authors."
"21742642500;7102171439;7005528388;6603126554;15726427000;24367209100;15755633200;35265576100;","Horizontal and vertical scaling of cloud geometry inferred from CloudSat data",2018,"10.1175/JAS-D-17-0111.1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85049776239&doi=10.1175%2fJAS-D-17-0111.1&partnerID=40&md5=c2b092f192aa61997a661234dc831310","A method is described to characterize the scale dependence of cloud chord length using cloud-type classification reported with the 94-GHz CloudSat radar. The cloud length along the CloudSat track is quantified using horizontal and vertical structures of cloud classification separately for each cloud type and for all clouds independent of cloud type. While the individual cloud types do not follow a clear power-law behavior as a function of horizontal or vertical scale, a robust power-law scaling of cloud chord length is observed when cloud type is not considered. The exponent of horizontal length is approximated by β ≈ 1.66 ± 0.00 across two orders of magnitude (~10-1000 km). The exponent of vertical thickness is approximated by β ≈ 2.23 ± 0.03 in excess of one order of magnitude (~1-14 km). These exponents are in agreement with previous studies using numerical models, satellites, dropsondes, and in situ aircraft observations. These differences in horizontal and vertical cloud scaling are consistent with scaling of temperature and horizontal wind in the horizontal dimension and with scaling of buoyancy flux in the vertical dimension. The observed scale dependence should serve as a guide to test and evaluate scale-cognizant climate and weather numerical prediction models. © 2018 American Meteorological Society."
"56242287700;35789369900;35197884700;57202066728;7006808794;55585674700;8853393600;","Trace metals in cloud water sampled at the puy de Dôme Station",2017,"10.3390/atmos8110225","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85034263475&doi=10.3390%2fatmos8110225&partnerID=40&md5=6bfb1b01689c03690afe280cf6bcf234","Concentrations of 33 metal elements were determined by ICP-MS (Inductively Coupled Plasma Mass Spectrometry) analysis for 24 cloud water samples (corresponding to 10 cloud events) collected at the puy de Dôme station. Clouds present contrasted chemical composition with mainly marine and continental characteristics; for some cloud events, a further anthropogenic source can be superimposed on the background level. In this context, measurements of trace metals may help to evaluate the impact of anthropogenic and natural sources on the cloud and to better discriminate the origin of the air masses. The metal concentrations in the samples are low (between 16.4 μg L-1 and 1.46 mg L-1). This could be explained by the remoteness of the puy de Dôme site from local sources. Trace metals are then used to confirm and refine a previous sample classification. A principal component analysis (PCA) using the pH value and the concentrations of Cl-, NO3-, SO42-, Na+ and NH4+ is performed considering 24 cloud samples. This first analysis shows that 18 samples are of marine origin and 6 samples are classified as continental. The same statistical approach is used adding trace metal concentration. Zn and Mg elements are the most abundant trace metals for all clouds. A higher concentration of Cd is mainly associated to clouds from marine origins. Cu, As, Tl and Sb elements are rather found in the continental samples than in the marine ones. Mg, V, Mn and Rb elements mainly found in soil particles are also more concentrated in the samples from continental air mass. This new PCA including trace metal confirms the classification between marine and continental air masses but also indicates that one sample presenting low pH and high concentrations of SO4 2-, Fe, Pb and Cu could be rather attributed to a polluted event. © 2017 by the authors."
"56001571800;22133985200;25923565300;7201423091;","Determining stages of cirrus evolution: A cloud classification scheme",2017,"10.5194/amt-10-1653-2017","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85018743269&doi=10.5194%2famt-10-1653-2017&partnerID=40&md5=45d80f5387ba9db27359e0c41f7e5439","Cirrus clouds impose high uncertainties on climate prediction, as knowledge on important processes is still incomplete. For instance it remains unclear how cloud microphysical and radiative properties change as the cirrus evolves. Recent studies classify cirrus clouds into categories including in situ, orographic, convective and liquid origin clouds and investigate their specific impact. Following this line, we present a novel scheme for the classification of cirrus clouds that addresses the need to determine specific stages of cirrus evolution. Our classification scheme is based on airborne Differential Absorption and High Spectral Resolution Lidar measurements of atmospheric water vapor, aerosol depolarization, and backscatter, together with model temperature fields and simplified parameterizations of freezing onset conditions. It identifies regions of supersaturation with respect to ice (ice-supersaturated regions, ISSRs), heterogeneous and homogeneous nucleation, depositional growth, and ice sublimation and sedimentation with high spatial resolution. Thus, all relevant stages of cirrus evolution can be classified and characterized. In a case study of a gravity lee-wave-influenced cirrus cloud, encountered during the ML-CIRRUS flight campaign, the applicability of our classification is demonstrated. Revealing the structure of cirrus clouds, this valuable tool might help to examine the influence of evolution stages on the cloud's net radiative effect and to investigate the specific variability of optical and microphysical cloud properties in upcoming research. © Author(s) 2017."
"56640482500;7404765381;","Cloud impacts on pavement temperature and shortwave radiation",2016,"10.1175/JAMC-D-16-0094.1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84998591776&doi=10.1175%2fJAMC-D-16-0094.1&partnerID=40&md5=2da08c0ce586835a54679880ccb47e79","Forecast systems provide decision support for end users ranging from the solar energy industry to municipalities concerned with road safety. Pavement temperature is an important variable when considering vehicle response to various weather conditions. A complex relationship exists between tire and pavement temperatures that affects vehicle performance. Many forecast systems suffer from inaccurate radiation forecasts resulting in part from the inability to model different types of clouds and their influence on radiation. This research focuses on forecast improvement by determining how cloud type impacts pavement temperature and the amount of shortwave radiation reaching the surface. The study region is the Great Plains where surface radiation data were obtained from the High Plains Regional Climate Center's Automated Weather Data Network stations. Pavement temperature data were obtained from the Meteorological Assimilation Data Ingest System. Cloud-type identification was possible via the Naval Research Laboratory Cloud Classification algorithm, and clouds were subsequently sorted into five distinct groups: clear conditions, low clouds, middle clouds, high clouds, and cumuliform clouds. Statistical analyses during the daytime in June 2011 revealed that cloud cover lowered pavement temperatures by up to approximately 10°C and dampened downwelling shortwave radiation by up to 400 W m-2. These pavement temperatures and surface radiation observations were strongly correlated, with a maximum correlation coefficient of 0.83. A comparison between cloud-type group identified and cloud cover observed from satellite images provided a measure of confidence in the results and identified cautions with using satellite-based cloud detection. © 2016 American Meteorological Society."
"7005536590;","Impact of Moderate Resolution Imaging Spectroradiometer (MODIS) cloud mask interpretation on cloud amount estimation",2015,"10.1002/2015JD023277","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84943450621&doi=10.1002%2f2015JD023277&partnerID=40&md5=956c341a4412a2ca83187cd4a949a447","Cloud masks serve as a basis for estimates of cloud amount, which is an essential parameter for studying the Earth’s radiation budget. The most commonly used cloud mask is a simple thematic classification, which includes qualitative information on the presence of clouds in the satellite’s instantaneous field of view (IFOV). Cloud mask classes have to be “translated” into a quantitative measure, in order to be used for cloud amount calculations. The assignment of cloud fractions to cloud mask classes is a subjective process and increases uncertainty in cloud amount estimates. We evaluated this degree of uncertainty using the Moderate Resolution Imaging Spectroradiometer (MODIS) cloud mask product. Together with the operational MODIS cloud mask interpretation, we investigated two extreme alternatives: “rigorous” (only “confident cloudy” IFOVs were 100% cloudy) and “tolerant” (only “confident clear” IFOVs were 0% cloudy). Results showed that the range of uncertainty was 14.3% in Europe and controlled by the frequency of small convective clouds. Comparison with surface-based observations suggests that the rigorous interpretation of the cloud mask is more accurate than that used operationally for MODIS level 3 product generation. The rigorous approach resulted in the smallest bias (-0.7%), the smallest root-mean-square error (4.6%), the small standard deviation (6%), and the strongest correlation (0.935). These results suggest that for climatological applications the rigorous scenario should be considered as a more accurate “best guess” over land. © 2015. American Geophysical Union. All Rights Reserved."
"24329085500;7003620360;35614095500;6601922531;6505768455;7005126327;7003431244;","Observation of polar stratospheric clouds over McMurdo (77.85°S, 166.67°E) (2006-2010)",2014,"10.1002/2013JD019892","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84901707487&doi=10.1002%2f2013JD019892&partnerID=40&md5=7505ca16f7caf65168017a7b8ed1befc","Polar stratospheric clouds (PSCs) have been observed in the Antarctic winter from2006 to 2010 at the Antarctic base of McMurdo Station using a newly developed Rayleigh lidar. Total backscatter ratio and volume depolarization at 532 nm have been measured from 9 km up to 30 km with an average of 90 measurements per winter season. The data set was analyzed in order to evaluate the occurrence of PSCs based on their altitude, seasonal variability, geometrical thickness, and cloud typology derived from observed optical parameters. We have adopted the latest version of the scheme used to classify PSCs detected by the CALIPSO satellite-based lidar in order to facilitate comparison of ground-based and satellite-borne lidars. This allowed us to approximately identify how processes acting at different spatial scales might affect the formation of different PSC particles. The McMurdo lidar observations are dominated by PSC layers during the Antarctic winter. A clear difference between the different type of PSCs classified according to the observed optical parameters and their geometrical thickness was observed. Ice and supercooled ternary solution PSCs are observed predominantly as thin layers, while thicker layers are associated with nitric acid trihydrate particles. The same classification scheme has been adopted to reanalyze the 1995-2001 McMurdo lidar data in order to compare both data sets (1995-2001 versus 2006-2010). © 2014. American Geophysical Union. All Rights Reserved."
"7005354212;6602397757;22234180300;6507518658;7005236944;6602128668;55465604600;","Differentiating between clouds and heavy aerosols in sun-glint regions",2010,"10.1175/2010JTECHA1368.1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-77955531761&doi=10.1175%2f2010JTECHA1368.1&partnerID=40&md5=c4061d21572708591e18d9d1dfac6157","An approach is presented to distinguish between clouds and heavy aerosols in sun-glint regions with automated cloud classification algorithms developed for the National Polar-orbiting Operational Environmental Satellite System (NPOESS) program. The approach extends the applicability of an algorithm that has already been applied successfully in areas outside the geometric and wind-induced sun-glint areas of the earth over both land and water surfaces. The successful application of this approach to include sun-glint regions requires an accurate cloud phase analysis, which can be degraded, especially in regions of sun glint, because of poorly calibrated radiances of the National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. Consequently, procedures have been developed to replace bad MODIS level 1B (L1B) data, which may result from saturation, dead/noisy detectors, or data dropouts, with radiometrically reliable values to create the Visible Infrared Imager Radiometer Suite (VIIRS) proxy sensor data records (SDRs). Cloud phase analyses produced by the NPOESS VIIRS cloud mask (VCM) algorithm using these modified VIIRS proxy SDRs show excellent agreement with features observed in color composites of MODIS imagery. In addition, the improved logic in the VCM algorithm provides a new capability to differentiate between clouds and heavy aerosols within the sun-glint cone. This ability to differentiate between clouds and heavy aerosols in strong sunglint regions is demonstrated using MODIS data collected during the recent fires that burned extensive areas in southern Australia. Comparisons between heavy aerosols identified by the VCM algorithm with imagery and heritage data products show the effectiveness of the new procedures using the modified VIIRS proxy SDRs. It is concluded that it is feasible to accurately detect clouds, identify cloud phase, and distinguish between clouds and heavy aerosol using a single cloud mask algorithm, even in extensive sun-glint regions. © 2010 American Meteorological Society."
"23013116500;15056438000;6602791724;7404147874;","Application of a cloud-texture analysis scheme to the cloud cluster structure recognition and rainfall estimation in a mesoscale rainstorm process",2006,"10.1007/s00376-006-0767-x","https://www.scopus.com/inward/record.uri?eid=2-s2.0-33750705644&doi=10.1007%2fs00376-006-0767-x&partnerID=40&md5=e4483a2c41169dd909f2036616af2921","It is thought that satellite infrared (IR) images can aid the recognition of the structure of the cloud and aid the rainfall estimation. In this article, the authors explore the application of a classification method relevant to four texture features, viz. energy, entropy, inertial-quadrature and local calm, to the study of the structure of a cloud cluster displaying a typical meso-scale structure on infrared satellite images. The classification using the IR satellite images taken during 4-5 July 2003, a time when a meso-scale torrential rainstorm was occurring over the Yangtze River basin, illustrates that the detailed structure of the cloud cluster can be obviously seen by means of the neural network classification method relevant to textural features, and the relationship between the textural energy and rainfall indicates that the structural variation of a cloud cluster can be viewed as an exhibition of the convection intensity evolvement. These facts suggest that the scheme of following a classification method relevant to textural features applied to cloud structure studies is helpful for weather analysis and forecasting."
"55738957800;","Lagrangian study of cloud properties and their relationships to meteorological parameters over the U.S. southern Great Plains",2003,"10.1175/1520-0442(2003)016<2700:LSOCPA>2.0.CO;2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0141856431&doi=10.1175%2f1520-0442%282003%29016%3c2700%3aLSOCPA%3e2.0.CO%3b2&partnerID=40&md5=7d623390602a62d4ff5a951b9a0d2c7a","Hourly satellite cloud data from 18 June to 18 July 1997 over the U.S. southern Great Plains are analyzed to study the scale-dependent cloud properties and their relationships to atmospheric conditions. The observed clouds are classified into high, midlevel, and low clouds according to their top heights. For each cloud type, contribution to the total cloud amount from clouds of different sizes is determined using a Lagrangian cloud classification scheme. It is found that in this continental, convectively active environment, more than half of the total cloud amount is from high clouds, of which 80% comes from clouds with area &gt;4 × 104 km2. For midlevel clouds, more than 50% of the contribution to cloud amount is from small clouds (e.g., cloud area &lt;4 × 104 km2). Almost all of the low clouds with significant contribution to cloud amount have spatial scales &lt;4 × 104 km2. This suggests that most of the midlevel and low clouds are of subgrid scale to a typical GCM resolution (T42 or T63). It is further found that cloud radiative properties, such as cloud albedo, outgoing longwave radiation, and cloud radiative forcing, have strong scale dependence. Bigger clouds are brighter and have lower outgoing longwave radiation. These results indicate that contributions to the observed cloud radiative forcing are dominated by large cloud systems. The diurnal variation of the cloud properties is also examined. Using concurrent meteorological analysis from NCEP, possible relationships between cloud properties and prevailing meteorological conditions were sought. It is found that clear relationships exist between cloud properties, such as cloud amount and albedo, and the layer-averaged relative humidity, and the relationships vary with cloud scale. In addition, cloud properties for high clouds are well correlated to vertical velocity in the upper troposphere. More large and highly reflective clouds tend to occur in regions of upward motion. Low clouds have a clear correspondence with the lower-tropospheric static stability and temperature. Large and thick clouds prefer to exist where the lower-tropospheric air is cold, statically more stable, and has high relative humidity."
"6603452942;","Cloud predictions diagnosed from mesoscale weather model forecasts",1999,"10.1175/1520-0493(1999)127<2465:CPDFMW>2.0.CO;2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0033384921&doi=10.1175%2f1520-0493%281999%29127%3c2465%3aCPDFMW%3e2.0.CO%3b2&partnerID=40&md5=1de3d59c3a914af0fda7b2bfcea8aa9a","Observed cloud characteristics, such as cloud cover, type, and base and top altitude, are of interest to the U.S. Air Force operational community for mission support. Predictions of such cloud characteristics are useful in support of a variety of mission activities. In this paper, a model output statistics approach to diagnosing these cloud characteristics from a forecast field generated by a mesoscale numerical weather prediction model is presented. Cloud characteristics information obtained from the air force RTNEPH cloud analysis supplied the cloud predictands, and forecast fields from the MM5 mesoscale numerical weather prediction model provided the weather variable predictors. Multiple linear regression (MLR) and multiple discriminant analysis (MDA) were used to develop the predictand-predictor relationships using 10 days of twice-daily cloud analyses and corresponding forecasts over a theater-scale grid. The consequent relationships were then applied to subsequent gridded forecast fields to obtain estimates of the distribution of the cloud characteristics at the forecast times. The methods used the most recent 10 days of cloud analyses and weather forecasts to develop the relationship for each successive application day. The gridded cloud characteristics were diagnosed for 10 days in each of January and July of 1992 over a theater-scale region in southern Europe. The resulting diagnosed cloud predictions were verified against the RTNEPH analyses for forecast durations of 6-36 h at 6-h intervals. It is found that both the MLR and the MDA methods produced small mean errors in all the cloud variables. When compared with persistence, MLR showed skill in rmse in January, while MDA did not. On the other hand, MDA obtained a better score than MLR in percent diagnosed in the correct cloud amount category. Furthermore, the category selection method used with the MDA scheme effectively reproduced the cloud variables' category frequency distribution compared with that of the verification data, while MLR did not. In July, both methods showed skill with respect to persistence in cloud amount. Verification results for cloud type, base altitude, and thickness did not show appreciable skill with respect to persistence. Cloud-ceiling altitude diagnoses showed consistent skill compared to persistence for both methods in both months. Visual depictions of the total cloud amount distribution as diagnosed by the methods showed that the MDA algorithm is capable of generating useful cloud prediction products. The images produced by the MLR scheme had unrealistically flat gradients of total cloud amount and too many occurrences of partly cloudy skies. The multiple discriminant analysis scheme is considered to be a useful short-term solution to the U.S. Air Force need for predictions of cloud characteristics in theater-scale areas."
"7004384155;6701607011;7202746102;7003597653;57196396429;7005516084;","Cloud field identification for earth radiation budget studies. Part II: Cloud field classification for the ScaRaB radiometer",1996,"10.1175/1520-0450(1996)035<0428:CFIFER>2.0.CO;2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0030481239&doi=10.1175%2f1520-0450%281996%29035%3c0428%3aCFIFER%3e2.0.CO%3b2&partnerID=40&md5=8089a667fe7ce299b1fc2ecf59de704c","Gaining a better understanding of the influence of clouds on the earth's energy budget requires a cloud classification that takes into account cloud height, thickness, and cloud cover. The radiometer ScaRaB (scanner for radiation balance), which was launched in January 1994, has two narrowband channels (0.5-0.7 and 10.5-12.5 μm) in addition to the two broadband channels (0.2-4 and 0.2-50 μm) necessary for earth radiation budget (ERB) measurements in order to improve cloud detection. Most automatic cloud classifications were developed with measurements of very good spatial resolution (200 m to 5 km). Earth radiation budget experiments (ERBE), on the other hand, work at a spatial resolution of about 50 km (at nadir), and therefore a cloud field classification adapted to this scale must be investigated. For this study, ScaRaB measurements are simulated by collocated Advanced Very High Resolution Radiometer (AVHRR) ERBE data. The best-suited variables for a global cloud classification are chosen using as a reference cloud types determined by an operationally working threshold algorithm applied to AVHRR measurements at a reduced spatial resolution of 4 km over the North Atlantic. Cloud field types are then classified by an algorithm based on the dynamic clustering method. More recently, the authors have carried out a global cloud field identification using cloud parameters extracted by the 3I (improved initialization inversion) algorithm, from High-Resolution Infrared Sounder (HIRS) - Microwave Sounding Unit (MSU) data. This enables the authors first to determine mean values of the variables best suited for cloud field classification and then to use a maximum-likelihood method for the classification. The authors find that a classification of cloud fields is still possible at a spatial resolution of ERB measurements. Roughly, one can distinguish three cloud heights and two effective cloud amounts (combination of cloud emissivity and cloud cover). However, only by combining flux measurements (ERBE) with cloud field classifications from sounding instruments (HIRS/MSU) can differences in radiative behavior of specific cloud fields be evaluated accurately."
"6602944180;57216164914;","Classification of cloud types based on spatial textural measures using NOAA-AVHRR data",1991,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-0026385976&partnerID=40&md5=38a59161f085ac461a7d4f0ccb848c6e","The United States Navy has a requirement for real-time cloud analysis and classification as part of a nowcasting capability. The use of texture measures in addition to standard Advanced Very High Resolution Radiometer (AVHRR) channel radiances is explored to provide an improved cloud analysis. Nowcasting delivers a very short term (2 to 4 h) weather forecast for operational use. Therefore, speed and accuracy of computation are both critical. The research effort discussed resulted in the development of a multispectral textural cloud type detection algorithm. Several statistical textural measures were investigated in order to select the most appropriate subset of textures suitable for cloud classification. The algorithm was tested on a NOAA-AVHRR (Advanced Very High Resolution Radiometer) data set over the Pacific ocean near the coast of California. It is shown that texture values computed from one AVHRR 512 by 512 scene do provide additional information for use in classification and labeling of clouds and other features."
"57196396429;6701607011;","Use of space and time sampling to produce representative satellite cloud classifications.",1984,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-0021622242&partnerID=40&md5=a6e2b1450b95e68aea79aaed195f9ae9","The methods of cloud classification using objective thresholding or histogram partitions produce results which depend strongly on the clouds which are present in the studied area. As the classes are changing, it is difficult to study variations of the cloud cover. Different ways of extending the learning set to produce more representative classes are investigated here. The effect of the different sampling methods on the bi-dimensional VIS-IR histograms and on the results of the LMD (Laboratoire de Meteorologie Dynamique) classification technique are studied on several sets of data. It is shown that extending the space sampling does not allow us to obtain well defined classes, especially for middle and high latitude regions, whereas constructing a 'learning set' from time sampling of the same restricted area results in histogram clusters more representative of the great cloud types passing over the region.-Authors"
"6602209517;","Atlas of Meteosat imagery.",1981,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85058718370&partnerID=40&md5=ea74bf22f1a95f21e8a6b864fc1ea5f1","Provides a set of images showing cloud, temperature and water vapour distributions taken during the first 2 yeas of Meteosat-1 operation. Contains 9 sections, all with illustrative and explanatory material: the Meteosat system; characteristics of Meteosat images; cloud classification; surface features; mesoscale cloud formations; synoptic scale cloud patterns; planetary scale cloud systems; tropical cloud systems; miscellaneous. A bibliography is also included. -R.Harris"
"57194694881;6507122674;","Machine learning generalisation across different 3D architectural heritage",2020,"10.3390/ijgi9060379","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086237519&doi=10.3390%2fijgi9060379&partnerID=40&md5=93a39656d49651816dd13739b376dfc7","The use of machine learning techniques for point cloud classification has been investigated extensively in the last decade in the geospatial community, while in the cultural heritage field it has only recently started to be explored. The high complexity and heterogeneity of 3D heritage data, the diversity of the possible scenarios, and the different classification purposes that each case study might present, makes it difficult to realise a large training dataset for learning purposes. An important practical issue that has not been explored yet, is the application of a single machine learning model across large and different architectural datasets. This paper tackles this issue presenting a methodology able to successfully generalise to unseen scenarios a random forest model trained on a specific dataset. This is achieved looking for the best features suitable to identify the classes of interest (e.g., wall, windows, roof and columns). © 2020 by the authors."
"57213925208;56108179600;56181559400;55420675800;6602211600;","Multi-Scale Local Context Embedding for LiDAR Point Cloud Classification",2020,"10.1109/LGRS.2019.2927779","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082883727&doi=10.1109%2fLGRS.2019.2927779&partnerID=40&md5=f7dde2806ebafbf5ee57a49026a61cd7","The semantic interpretation using point clouds, especially regarding light detection and ranging (LiDAR) point cloud classification, has attracted a growing interest in the fields of photogrammetry, remote sensing, and computer vision. In this letter, we aim at tackling a general and typical feature learning problem in 3-D point cloud classification-how to represent geometric features by structurally considering a point and its surroundings in a more effective and discriminative fashion? Recently, enormous efforts have been made to design the geometric features, yet it is less investigated to fully explore the potentials of the features. For that, there have been many filter-based studies proposed by selecting a subset of the whole feature space for better representing the local geometry structure. However, such a hard-threshold selection strategy inevitably suffers from information loss. In addition, the construction of the geometric features is relatively sensitive to the size of the neighborhood. To this end, we propose to extract multi-scaled feature representations and locally embed them into a low-dimensional and robust subspace where a more compact representation with the intrinsic structure preservation of the data is expected to be obtained, thereby further yielding a better classification performance. In our case, we apply a popular manifold learning approach, that is, locality-preserving projections, for the task of learning low-dimensional embedding. Experimental results conducted on one LiDAR point cloud data set provided by the 2018 IEEE Data Fusion Contest demonstrate the effectiveness of the proposed method in comparison with several commonly used state-of-the-art baselines. © 2004-2012 IEEE."
"57214136113;56543170700;6701729341;6602452091;","Full-Waveform Airborne LiDAR Data Classification Using Convolutional Neural Networks",2019,"10.1109/TGRS.2019.2919472","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078226951&doi=10.1109%2fTGRS.2019.2919472&partnerID=40&md5=26297bd70b352f25c886c7888e4dbcd4","Point cloud classification is one of the most important and time-consuming stages of airborne LiDAR (Light Detection and Ranging) data processing, playing a key role in the generation of cartographic products. This paper describes an innovative algorithm to perform LiDAR point-cloud classification, which relies on Convolutional Neural Networks (CNNs) and takes advantage of full-waveform data registered by modern laser scanners. The proposed method consists of two steps. First, a simple CNN is used to preprocess each waveform, providing a compact representation of the data. By exploiting the coordinates of the points associated with the waveforms, output vectors generated by the first CNN are then mapped into an image that is subsequently segmented by a Fully Convolutional Network (FCN): a label is assigned to each pixel and, consequently, to the point falling in the pixel. In this way, spatial positions and geometrical relationships between neighboring data are taken into account. These particular architectures allow to accurately identify even challenging classes such as power line and transmission tower. © 1980-2012 IEEE."
"12769875100;55747560500;55268661300;57209911739;","Arctic cloud annual cycle biases in climate models",2019,"10.5194/acp-19-8759-2019","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85069055042&doi=10.5194%2facp-19-8759-2019&partnerID=40&md5=ccec384ed0fe1c3babaf16e4fdce4a5b","Arctic clouds exhibit a robust annual cycle with maximum cloudiness in fall and minimum cloudiness in winter. These variations affect energy flows in the Arctic with a large influence on the surface radiative fluxes. Contemporary climate models struggle to reproduce the observed Arctic cloud amount annual cycle and significantly disagree with each other. The goal of this analysis is to quantify the cloud-influencing factors that contribute to winter-summer cloud amount differences, as these seasons are primarily responsible for the model discrepancies with observations. We find that differences in the total cloud amount annual cycle are primarily caused by differences in low, rather than high, clouds; the largest differences occur between the surface and 950&thinsp;hPa. Grouping models based on their seasonal cycles of cloud amount and stratifying cloud amount by cloud-influencing factors, we find that model groups disagree most under strong lower tropospheric stability, weak to moderate mid-tropospheric subsidence, and cold lower tropospheric air temperatures. Intergroup differences in low cloud amount are found to be a function of lower tropospheric thermodynamic characteristics. Further, we find that models with a larger low cloud amount in winter have a larger ice condensate fraction, whereas models with a larger low cloud amount in summer have a smaller ice condensate fraction. Stratifying model output by the specifics of the cloud microphysical scheme reveals that models treating cloud ice and liquid condensate as separate prognostic variables simulate a larger ice condensate fraction than those that treat total cloud condensate as a prognostic variable and use a temperature-dependent phase partitioning. Thus, the cloud microphysical parameterization is the primary cause of inter-model differences in the Arctic cloud annual cycle, providing further evidence of the important role that cloud ice microphysical processes play in the evolution and modeling of the Arctic climate system. © 2019 Author(s). This work is distributed under the Creative Commons Attribution 4.0 License."
"56803886700;7801611878;35096575300;57200043734;","POINTNET for the AUTOMATIC CLASSIFICATION of AERIAL POINT CLOUDS",2019,"10.5194/isprs-annals-IV-2-W5-445-2019","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85067436995&doi=10.5194%2fisprs-annals-IV-2-W5-445-2019&partnerID=40&md5=b6d35c5bc45a912a7b6364ba4c572c7b","During the last couple of years, there has been an increased interest to develop new deep learning networks specifically for processing 3D point cloud data. In that context, this work intends to expand the applicability of one of these networks, PointNet, from the semantic segmentation of indoor scenes, to outdoor point clouds acquired with Airborne Laser Scanning (ALS) systems. Our goal is to of assist the classification of future iterations of a national wide dataset such as the Actueel Hoogtebestand Nederland (AHN), using a classification model trained with a previous iteration. First, a simple application such as ground classification is proposed in order to prove the capabilities of the proposed deep learning architecture to perform an efficient point-wise classification with aerial point clouds. Then, two different models based on PointNet are defined to classify the most relevant elements in the case study data: Ground, vegetation and buildings. While the model for ground classification performs with a F-score metric above 96%, motivating the second part of the work, the overall accuracy of the remaining models is around 87%, showing consistency across different versions of AHN but with improvable false positive and false negative rates. Therefore, this work concludes that the proposed classification of future AHN iterations is feasible but needs more experimentation. © Authors 2019."
"57195412150;7401526171;7005052907;55273498900;7404970050;","Effective cloud detection and segmentation using a gradient-based algorithm for satellite imagery: Application to improve PERSIANN-CCS",2019,"10.1175/JHM-D-18-0197.1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85066289137&doi=10.1175%2fJHM-D-18-0197.1&partnerID=40&md5=70a393d8b77c2e37a5d7abb763907cc7","The effective identification of clouds and monitoring of their evolution are important toward more accurate quantitative precipitation estimation and forecast. In this study, a new gradient-based cloud-image segmentation algorithm is developed using image processing techniques. This method integrates morphological image gradient magnitudes to separate cloud systems and patches boundaries. A varying scale kernel is implemented to reduce the sensitivity of image segmentation to noise and to capture objects with various finenesses of the edges in remote sensing images. The proposed method is flexible and extendable from single to multispectral imagery. Case studies were carried out to validate the algorithm by applying the proposed segmentation algorithm to synthetic radiances for channels of the Geostationary Operational Environmental Satellite (GOES-16) simulated by a high-resolution weather prediction model. The proposed method compares favorably with the existing cloud-patch-based segmentation technique implemented in the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS) rainfall retrieval algorithm. Evaluation of event-based images indicates that the proposed algorithm has potentials comparing to the conventional segmentation technique used in PERSIANN-CCS to improve rain detection and estimation skills with an accuracy rate of up to 98% in identifying cloud regions. © 2019 American Meteorological Society."
"7201431739;","Spectral cumulus parameterization based on cloud-resolving model",2019,"10.1007/s00382-018-4137-z","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85042209301&doi=10.1007%2fs00382-018-4137-z&partnerID=40&md5=39eb5f014aceb2d49fe18bf2d5c0261b","We have developed a spectral cumulus parameterization using a cloud-resolving model. This includes a new parameterization of the entrainment rate which was derived from analysis of the cloud properties obtained from the cloud-resolving model simulation and was valid for both shallow and deep convection. The new scheme was examined in a single-column model experiment and compared with the existing parameterization of Gregory (2001, Q J R Meteorol Soc 127:53–72) (GR scheme). The results showed that the GR scheme simulated more shallow and diluted convection than the new scheme. To further validate the physical performance of the parameterizations, Atmospheric Model Intercomparison Project (AMIP) experiments were performed, and the results were compared with reanalysis data. The new scheme performed better than the GR scheme in terms of mean state and variability of atmospheric circulation, i.e., the new scheme improved positive bias of precipitation in western Pacific region, and improved positive bias of outgoing shortwave radiation over the ocean. The new scheme also simulated better features of convectively coupled equatorial waves and Madden–Julian oscillation. These improvements were found to be derived from the modification of parameterization for the entrainment rate, i.e., the proposed parameterization suppressed excessive increase of entrainment, thus suppressing excessive increase of low-level clouds. © 2018, The Author(s)."
"55684703600;56803413300;7202054134;6602871885;57216482500;7005189183;57208298810;","Cloud cover in the Australian region: Development and validation of a cloud masking, classification and optical depth retrieval algorithm for the advanced Himawari imager",2019,"10.3389/fenvs.2019.00020","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85064400071&doi=10.3389%2ffenvs.2019.00020&partnerID=40&md5=4da537ab0d7db6c016e720e5a3aed7f5","This paper presents a cloud masking, cloud classification and optical depth retrieval algorithm and its application to the Advanced Himawari Imager (AHI) on the Himawari-8/9 satellites using visible, near infrared and thermal infrared bands. A time-series-based approach was developed for cloud masking which was visually assessed and quantitatively validated over 1 year of daytime data for both land and ocean against the level 2 Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) 1 km cloud layer product (version 4.10). An overall hit rate (the proportion of pixels identified by both sensors as either clear or cloudy) of 87% was found. However, analysis revealed that, when partially cloudy conditions were experienced, the small footprint of the CALIOP sensor (70 meters beam size sampling every 330 meters along the ground track) had a major impact on the hit rate. When partially cloudy pixels are excluded a hit rate of ~98% was found, even for thin clouds with optical depth less than 0.25. A two-way confidence index for the cloud mask was developed which could be used to reclassify the pixels depending on applications, either biasing toward clearness or cloudiness. On the basis of the cloud masking, classification and optical depth retrieval was performed based on radiative transfer modeling. Small modeling error was found, and inspection of typical cloud classification examples showed that the results were consistent with cloud texture and cloud top temperatures. While difficult to validate retrieved cloud properties directly, an indirect quantitative validation was performed by comparing surface-level solar flux computed from the retrieved cloud properties with in-situ measurements at 11 sites across Australia for up to 3 years. Excellent agreement between calculated and measured solar flux was found, with a mean monthly bias of 2.96 W/m 2 and RMSE of 8.91 W/m 2 , and the correlation coefficient exceeding 0.98 at all sites. Further assessment was conducted by comparing seasonal and annual cloud fraction with that of ISCCP (International Satellite Cloud Climatology Project) over Australia and surrounding region. It showed high degree of resemblance between the two datasets in their total cloud fraction. The geographical distribution of cloud classes also showed broad resemblance, though detailed differences exist, especially for high clouds, which is probably due to the use of different cloud classification systems in the two datasets. The products generated from this study are being used in several applications including ocean color remote sensing, solar energy, vegetation monitoring and detection of smoke for the study of their health impacts, and aerosol and land surface bidirectional reflectance distribution function (BRDF) retrieval. The method developed herein can be applied to other geostationary sensors. © 2019 Qin, Steven, Schroeder, McVicar, Huang, Cope and Zhou."
"56319173800;23004944100;56037301900;","3D micro-mapping: Towards assessing the quality of crowdsourcing to support 3D point cloud analysis",2018,"10.1016/j.isprsjprs.2018.01.009","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85041377409&doi=10.1016%2fj.isprsjprs.2018.01.009&partnerID=40&md5=81ca9444762a9ad8accf69c7d949693e","In this paper, we propose a method to crowdsource the task of complex three-dimensional information extraction from 3D point clouds. We design web-based 3D micro tasks tailored to assess segmented LiDAR point clouds of urban trees and investigate the quality of the approach in an empirical user study. Our results for three different experiments with increasing complexity indicate that a single crowdsourcing task can be solved in a very short time of less than five seconds on average. Furthermore, the results of our empirical case study reveal that the accuracy, sensitivity and precision of 3D crowdsourcing are high for most information extraction problems. For our first experiment (binary classification with single answer) we obtain an accuracy of 91%, a sensitivity of 95% and a precision of 92%. For the more complex tasks of the second Experiment 2 (multiple answer classification) the accuracy ranges from 65% to 99% depending on the label class. Regarding the third experiment – the determination of the crown base height of individual trees – our study highlights that crowdsourcing can be a tool to obtain values with even higher accuracy in comparison to an automated computer-based approach. Finally, we found out that the accuracy of the crowdsourced results for all experiments is hardly influenced by characteristics of the input point cloud data and of the users. Importantly, the results’ accuracy can be estimated using agreement among volunteers as an intrinsic indicator, which makes a broad application of 3D micro-mapping very promising. © 2018 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS)"
"56068624000;57200294912;56188627800;14625770800;54402367600;","Cross-domain ground-based cloud classification based on transfer of local features and discriminative metric learning",2018,"10.3390/rs10010008","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85040689691&doi=10.3390%2frs10010008&partnerID=40&md5=c3480efa7895bc8d67e3d170e4d027e9","Cross-domain ground-based cloud classification is a challenging issue as the appearance of cloud images from different cloud databases possesses extreme variations. Two fundamental problems which are essential for cross-domain ground-based cloud classification are feature representation and similarity measurement. In this paper, we propose an effective feature representation called transfer of local features (TLF), and measurement method called discriminative metric learning (DML). The TLF is a generalized representation framework that can integrate various kinds of local features, e.g., local binary patterns (LBP), local ternary patterns (LTP) and completed LBP (CLBP). In order to handle domain shift, such as variations of illumination, image resolution, capturing location, occlusion and so on, the TLF mines the maximum response in regions to make a stable representation for domain variations. We also propose to learn a discriminant metric, simultaneously. We make use of sample pairs and the relationship among cloud classes to learn the distancemetric. Furthermore, in order to improve the practicability of the proposedmethod,we replace the original cloud images with the convolutional activation maps which are then applied to TLF and DML. The proposed method has been validated on three cloud databases which are collected in China alone, provided by Chinese Academy ofMeteorological Sciences (CAMS),Meteorological Observation Centre (MOC), and Institute of Atmospheric Physics (IAP). The classification accuracies outperform the state-of-the-art methods."
"57190116702;35454141800;55495496100;8067118800;30667558200;9838847000;57208601312;7005602760;6603819181;","Development of Algorithm for Discriminating Hydrometeor Particle Types With a Synergistic Use of CloudSat and CALIPSO",2017,"10.1002/2017JD027113","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85031696647&doi=10.1002%2f2017JD027113&partnerID=40&md5=485eb7ab7555c2007e83ee9dab3398c6","We developed a method for classifying hydrometeor particle types, including cloud and precipitation phase and ice crystal habit, by a synergistic use of CloudSat/Cloud Profiling Radar (CPR) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO)/Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP). We investigated how the cloud phase and ice crystal habit characterized by CALIOP globally relate with radar reflectivity and temperature. The global relationship thus identified was employed to develop an algorithm for hydrometeor type classification with CPR alone. The CPR-based type classification was then combined with CALIPSO-based type characterization to give CPR-CALIOP synergy classification. A unique aspect of this algorithm is to exploit and combine the lidar's sensitivity to thin ice clouds and the radar's ability to penetrate light precipitation to offer more complete picture of vertically resolved hydrometeor type classification than has been provided by previous studies. Given the complementary nature of radar and lidar detections of hydrometeors, our algorithm delivers 13 hydrometeor types: warm water, supercooled water, randomly oriented ice crystal (3D-ice), horizontally oriented plate (2D-plate), 3D-ice + 2D-plate, liquid drizzle, mixed-phase drizzle, rain, snow, mixed-phase cloud, water + liquid drizzle, water + rain, and unknown. The global statistics of three-dimensional occurrence frequency of each hydrometeor type revealed that 3D-ice contributes the most to the total cloud occurrence frequency (53.8%), followed by supercooled water (14.3%), 2D-plate (9.2%), rain (5.9%), warm water (5.7%), snow (4.8%), mixed-phase drizzle (2.3%), and the remaining types (4.0%). This hydrometeor type classification provides observation-based insight for climate model diagnostics in representation of cloud phase and their microphysical characteristics. ©2017. The Authors."
"8324596700;57192702359;57192702451;","A classification method of unmanned-aerial-systems-derived point cloud for generating a canopy height model of Farm Forest",2016,"10.1109/IGARSS.2016.7729186","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85007495050&doi=10.1109%2fIGARSS.2016.7729186&partnerID=40&md5=37c29582c0d9b094287d1ffbf37384ce","During the last decade unmanned aerial systems (UAS) have been intensively applied to create 3-dimensional models of land surface features for various applications. Although, UAS data can be jointly used with airborne LiDAR data to generate a canopy height model (CHM) of forest stands, it is rare to find research concerned the generation of forest CHM using only UAS data. This paper investigate a suitable method to classify UAS point cloud to create CHM data for forest inventory. Results showed that ground points over building areas, open land, and forestland can be successfully collected by appropriate terrain angles which define a threshold value of the angle between a point, its projection on the plane of a triangle, and the closest vertex of a TIN surface model. A conservative threshold value of 5 degrees was suggested due to its allowing critical ground points whilst excluding crown points being collected. The UAS-derived CHM was evaluated with an RMSE accuracy of 0.01, 0.20, and 0.42 m for road, buildings, and trees respectively. © 2016 IEEE."
"54902712400;8835568200;23490592400;55861417200;55480762900;24437727500;56215353100;","CLOUDET: A Cloud Detection and Estimation Algorithm for Passive Microwave Imagers and Sounders Aided by Naïve Bayes Classifier and Multilayer Perceptron",2015,"10.1109/JSTARS.2014.2321559","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84902734508&doi=10.1109%2fJSTARS.2014.2321559&partnerID=40&md5=e9f5cf8f5ee2ec28ad84f7c9375a8af3","CLOUDET, a cloud detection and estimation algorithm for passive microwave imagers and sounders is presented. CLOUDET is based on a naïve Bayes classifier and multilayer perceptron. It is applied to the special sensor microwave imager/sounder (SSMIS), and the ECMWF integrated forecast system (IFS) cloud liquid water information has been used to train the algorithm. CLOUDET is applicable to both ocean and land-surface types. CLOUDET has been developed and evaluated by employing two different groups of radiometric information, namely, the humidity channels near 183 GHz (humidity algorithm) and the window channels between 19 and 91 GHz (window algorithm). It has been revealed that both humidity and window algorithms can detect cloudy scenes over ocean at a confidence level of more than 90% (80% over land). The analysis indicates that the humidity algorithm has a better ability in detecting cloudy scenes over ocean than the window algorithm (CSI=0.98 vs. CSI=0.93). The opposite is true over land-surface type, revealing a CSI of 0.85 by humidity algorithm as opposed to CSI of 0.88 by window algorithm. The estimation of cloud by the CLOUDET algorithm has also been very promising during the validation effort. In particular, the correlation coefficient obtained over ocean through the use of the window algorithm is 0.70 (MAE 0.04 mm and RMSE 0.09 mm). The presented algorithm CLOUDET can be served as a stand-alone tool to reject and identify the cloudy scenes as well as to estimate the cloud liquid water path amount prior to assimilating the radiances into numerical weather prediction model. © 2015 IEEE."
"24376765700;6603582339;","Cloud field segmentation via multiscale convexity analysis",2008,"10.1029/2007JD009369","https://www.scopus.com/inward/record.uri?eid=2-s2.0-50949112307&doi=10.1029%2f2007JD009369&partnerID=40&md5=5ecf154a02c089d28121e2b0b58d9806","Cloud fields retrieved from remotely sensed satellite data resemble functions depicting spectral values at each spatial position (x,y). Segmenting such cloud fields through a simple thresholding technique may not provide any structurally significant information about each segmented category. An approach based on the use of multiscale convexity analysis to derive structurally significant regions from cloud fields is addressed in this paper. This analysis requires (1) the generation of cloud fields at coarser resolutions and (2) the construction of convex hulls of cloud fields, at corresponding resolutions by employing multiscale morphologic opening transformation and half-plane closings with certain logical operations. The three basic parameters required from these generated multiscale phenomena in order to accomplish the structure-based segmentation include (1) the areas of multiscale cloud fields, (2) the areas of corresponding convex hulls, and (3) the estimation of convexity measures at corresponding resolutions by employing the areas of cloud fields and areas of corresponding convex hulls. These convexity measures computed for multiscale cloud fields are plotted as a function of the resolution imposed owing to multiscale opening to derive a causal relationship. The scaling exponents derived from these graphical plots are taken as the basis for (1) determining the transition zones between the regimes and (2) segmenting the cloud fields into morphologically significant regions. We demonstrated this approach on two different cloud fields retrieved from Moderate Resolution Imaging Spectroradiometer (MODIS) data. The segmented regions from these cloud fields possess different degrees of spatial complexities. As many macroscale and microscale atmospheric fields are classified according to spatial variability indexes, the framework proposed here would supplement those existing atmospheric field classification methodologies. Copyright 2008 by the American Geophysical Union."
"7402198312;35121795500;","Classification of clouds in the Japan Sea area using NOAA AVHRR satellite images and self-organizing map",2007,"10.1109/IGARSS.2007.4423236","https://www.scopus.com/inward/record.uri?eid=2-s2.0-79960639131&doi=10.1109%2fIGARSS.2007.4423236&partnerID=40&md5=1d546a3b4cb5cdbcd6cef957d4ad4a0a","This paper introduces a method for cloud classification using NOAA AVHRR satellite images. AVHRR (Advanced Very High Resolution Radiometer) data consists of five-channel multi-spectral images. To reduce the dimensionality of the data, principal component analysis (PCA) is calculated for each channel separately. The most significant principal component values are then composed into an image feature vector. Finally, the feature vectors are clustered using self-organizing map (SOM). This method is applied for the study of winter season clouds in the Japan Sea area. © 2007 IEEE."
"6604053026;13007466900;","1D-VAR retrieval of temperature and humidity profiles from ground-based microwave radiometers",2006,"10.1109/MICRAD.2006.1677095","https://www.scopus.com/inward/record.uri?eid=2-s2.0-34250774878&doi=10.1109%2fMICRAD.2006.1677095&partnerID=40&md5=c1f220d49bd55e363511e393a07a1ff3","A variational retrieval method is described to combine observations from microwave and infrared radiometers and surface sensors with background from short-range Numerical Weather Prediction (NWP) forecasts in an optimal way, accounting for their error characteristics. The required forward models are described. Observation errors are found to be dominated by representativeness, due to their sensitivity to atmospheric variability on smaller scales than the NWP model gird. Their effect can be reduced by evaluating this dynamically. Profiles of temperature and total water content are retrieved from synthetic data using Newtonian iteration. An error analysis shows these are expected to improve mesoscale NWP, retrieving temperature profiles with an uncertainty of <1 K up to 5 km and humidity with <40% up to 3 km, albeit both with poor vertical resolution. A cloud classification scheme is introduced to address convergence problems and constrain the retrievals. This Bayesian method can be extended to form a basis for future Integrated Observing Systems. ©2006 IEEE."
"12808931100;56276992000;12809430200;57211811043;","AVHRR data for real-time operational flood forecasting in Malaysia",2005,"10.1007/3-540-27468-5_93","https://www.scopus.com/inward/record.uri?eid=2-s2.0-80052230756&doi=10.1007%2f3-540-27468-5_93&partnerID=40&md5=e70f6280e2d1ba96c3cb8ef932e6e3a9","Flash floods strike quickly and in most cases without warning. They are usually observed before any warning can be issued and usually persons and property have been affected before the warning reaches them. Such are the conditions prevalent in Malaysia's extreme monsoon weather that occasionally causes floods and results in the extensive damage to property and sometimes loss of lives. Over the years variously hydrological and structural engineering measures have been implemented for flood monitoring and forecasting. These measures have only yielded limited success as may be seen in the recurring flood situation. Yearly financial and property loss estimates have increased and an estimated cost of over 2.5 billion RM is projected for the year 2004 according to sources from the drainage and irrigation department of Malaysia. It has thus become apparent that Malaysia institutes an effective operational flood forecasting to arrest the persisting flood problem. In this paper we will expound on current flood management and forecasting system being implemented in the country, particularly the Klang Valley that includes Kuala Lumpur where there has been tremendous urban growth and development in the last one and half decades. The paper further discusses where current flood management systems have been lacking in the absence of real-time hydro-meteorological forecasts. Where as hydrodynamic simulations and structural control measures have been emphasized in many flood management systems in Malaysia, the integration of real-time hydro-meteorological forecasts have been conspicuously absent, rendering most in-situ flood forecasts and early warnings ineffective in address the flood problem in the country. Malaysia is a tropical country that lies along the path of the northeast and southwest monsoon. Although satellite image based NWP have proved useful for the tropical and equatorial regions of the world in flood forecasting, they have yet to be applied in Malaysia. Observations have generally shown heavy cumulonimbus clouds formation and thunderstorms precede the usual heavy monsoon rains that cause floods in the region. This makes quantitative precipitation forecast a must be input to any flood early warning design. Numerous empirical studies have determined that cloud top temperatures less that 235k in the tropics are generally expected to produce convective rainfall at the rate of 3mm/hr. In this study we thus investigate monsoon cloud formation that has the propensity to precipitate using NOAA-AVHRR data for real-time operational flood early warning in Malaysia. The AVHRR data has been preferred for its relatively high temporal resolution of at most 6/hours, its easy acquisition and cost effectiveness and its ability for automated geometric rectification when compared to GEOS and GMS data. Cloud cover and types are processed using cloud indexing and pattern recognition techniques on the AVHRR data. The cloud indexing technique was initially developed for NOAA but was later also adapted for Geostationary satellite images. The technique assigns rainfall levels to each cloud type identifies in an image based on the relationship between cold and bright clouds top temperature and the high probability of precipitation. We discuss how visible (VIS) and infrared (IR) techniques are applied to bi-spectral cloud classification and rain areas are determined by classifying pixel clusters in the VIS/IR histogram. Precipitation probability is evaluated based on the relationship between cold and brightness temperature of clouds. The near infrared (NIR) and infrared (IR) channels 3, 4, and 5 of the data are processed for temperature and brightness. Cold clouds with temperature below 235k threshold value are taken as indication of rain. Rainfall is estimated based on the assumption that every cloud pixel has a constant unit rain-rate of 3mmh-1, which is appropriate for tropical precipitation over 2.5° × 2.5° areas around the equator. The paper finally discusses current developments in nowcasting that utilizes latest satellite observations together with numerical weather prediction models and how this system can be adapted to the needs of very short term forecast for flood early warnings in Malaysia. © 2005 Springer-Verlag Berlin Heidelberg."
"7007047895;","More observations of small funnel clouds and other tubular clouds",2005,"10.1175/MWR3080.1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-31344453426&doi=10.1175%2fMWR3080.1&partnerID=40&md5=d34f91af412f6292c304726d9a9f2bc7","In this brief contribution, photographic documentation is provided of a variety of small, tubular-shaped clouds and of a small funnel cloud pendant from a convective cloud that appears to have been modified by flow over high-altitude mountains in northeast Colorado. These funnel clouds are contrasted with others that have been documented, including those pendant from high-based cumulus clouds in the plains of the United States. It is suggested that the mountain funnel cloud is unique in that flow over high terrain is probably responsible for its existence; other types of small funnel clouds are seen both over elevated, mountainous terrain and over flat terrain at lower elevations. © 2005 American Meteorological Society."
"7004337580;55999772700;35615424000;7006172186;7004463365;6506940684;7004575340;","Cloud fraction within GOME footprint using a refined cloud clearing algorithm",2000,"10.1016/S0273-1177(99)00462-7","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0034092960&doi=10.1016%2fS0273-1177%2899%2900462-7&partnerID=40&md5=22c51d11a8936bc285f0f38a9d5ed878","The Global Ozone Monitoring Experiment (GOME) flies on-board the ERS2 satellite since 1995 and its main mission is the retrieval of total ozone at the nominal ground resolution of 320x40 km 2 . Cloud detection and characterization, an interesting result in itself, are needed to analyse spectral data prior to the retrieval of columnar ozone as well as other atmospheric constituents. The Cloud Clearing Algorithm (CCA) available in literature was developed based on a simple thresholding method: cloud detection is obtained within the Polarisation Measurements Devices (PMDs) ground pixel (20x40 km 2 , one-sixteenth of the GOME spatial resolution) using thresholds that depend primarily on surface type and reflection, and solar zenith angles. A refinement of the CCA is presented. Thresholds over the ocean have been computed by comparing PMD detection results with the ERS2 Along Track Scanning Radiometer 2 (ATSR2) cloud masks, being ATSR2 measurements coincident in time and space with GOME ones. Refined CCA performances have been compared with a totally independent cloud classification algorithm that uses visible-infrared, high resolution full disk METEOSAT images. Case studies are presented, and differences between the two methods are discussed on the PMD and spectral GOME ground pixel sizes."
"57198563635;6602918477;","Improved man-computer interactive classification of clouds based on bispectral satellite imagery",1998,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-0345451573&partnerID=40&md5=b0d2e1dc617213e5b0732871e69ef7a2","In this paper, improvement on man-computer interactive classification of clouds based on bispectral satellite imagery has been synthesized by using the maximum likelihood automatic clustering (MLAC) and the unit feature space classification (UFSC) approaches. The improved classification not only shortens the time of sample-training in UFSC method, but also eliminates the inevitable shortcomings of the MLAC method, (e.g., 1. sample selecting and training is confined only to one cloud image: 2. the result of clustering is pretty sensitive to the selection of initial cluster center; 3. the actual classification basically can not satisfy the supposition of normal distribution required by MLAC method; 4. errors in classification are difficult to be modified.) Moreover, it makes full use of the professionals' accumulated knowledge and experience of visual cloud classifications and the cloud report of ground observation, having ensured both the higher accuracy of classification and its wide application as well."
"7201361035;","Inference of the climatic efficiency of clouds from satellite measurements",1995,"10.1080/01431169508954598","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0029513129&doi=10.1080%2f01431169508954598&partnerID=40&md5=0e86c6720442a0792b60ee895dd197c8","The influence of clouds over the North Sea on the radiation field and on climate is investigated by analysing satellite measurements. The main interest is on high clouds due to their ambivalent behaviour in the radiation field. To quantify the influence of clouds on climate, the cloud-climate efficiency is introduced. The cloud-climate efficiency allows us to estimate the gain or respectively loss of energy of the earth/atmosphere system in the presence of a cloud, which can be specified by a cloud classification. The spatial integration of the cloud-climate efficiencies results in the cloud forcing defined by the difference of the radiative fluxes within a clear sky and a cloudy satellite image pixel. The first step is an accurate detailed cloud classification based on the maximum likelihood method. The method developed for National Oceanograph and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA AVHRR) data and Meteosat data can be used to discriminate 24 clouds, especially high clouds with different optical depths. Evaluating these results, a good agreement between the cloud types inferred from satellite data and from synoptical observations could be achieved. In the following step transmittances of high clouds could be determined by using NOAA AVHRR data, where for the solar spectrum a simple radiative transfer scheme is applied. For the longwave spectrum, an equation after Platt is used. Comparing these transmittances with groand based observations during field experiments (ICE’87, ICE’89. IGPB’90), good agreements could be foand. Using NOAA AVHRR data, the derived information is applied to compute the cloud-climate efficiency at the top of the atmosphere. It can be seen that in the shortwave spectrum the cloud-climate efficiency shows a general cooling effect of the earth/atmosphere system for all clouds and a strong dependence on the insolation. Regarding the cloud-climate efficiency in the longwave spectrum, a heating of the earth/atmosphere system due to clouds was always observed. Thus, high clouds with the same optical properties may lead to different effects in the earth/atmosphere system depending on the anderlying surface, on the optical depths of that cloud, and on the geographical appearance related to the insolation. An approach to compare the increasing cloud forcing at the top of the atmosphere with an analysis of the relative topography 300/850 hPa shows that the increase of the cloud forcing is well correlated with an increase of the temperature in this layer. © 1995 Taylor & Francis Group, LLC."
"57200555819;57211430607;57126877800;56506111400;7005693588;","Directionally constrained fully convolutional neural network for airborne LiDAR point cloud classification",2020,"10.1016/j.isprsjprs.2020.02.004","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079552935&doi=10.1016%2fj.isprsjprs.2020.02.004&partnerID=40&md5=9e82c0e57ce7b67b4a9e2ac64403189c","Point cloud classification plays an important role in a wide range of airborne light detection and ranging (LiDAR) applications, such as topographic mapping, forest monitoring, power line detection, and road detection. However, due to the sensor noise, high redundancy, incompleteness, and complexity of airborne LiDAR systems, point cloud classification is challenging. Traditional point cloud classification methods mostly focus on the development of handcrafted point geometry features and employ machine learning-based classification models to conduct point classification. In recent years, the advances of deep learning models have caused researchers to shift their focus towards machine learning-based models, specifically deep neural networks, to classify airborne LiDAR point clouds. These learning-based methods start by transforming the unstructured 3D point sets to regular 2D representations, such as collections of feature images, and then employ a 2D CNN for point classification. Moreover, these methods usually need to calculate additional local geometry features, such as planarity, sphericity and roughness, to make use of the local structural information in the original 3D space. Nonetheless, the 3D to 2D conversion results in information loss. In this paper, we propose a directionally constrained fully convolutional neural network (D-FCN) that can take the original 3D coordinates and LiDAR intensity as input; thus, it can directly apply to unstructured 3D point clouds for semantic labeling. Specifically, we first introduce a novel directionally constrained point convolution (D-Conv) module to extract locally representative features of 3D point sets from the projected 2D receptive fields. To make full use of the orientation information of neighborhood points, the proposed D-Conv module performs convolution in an orientation-aware manner by using a directionally constrained nearest neighborhood search. Then, we design a multiscale fully convolutional neural network with downsampling and upsampling blocks to enable multiscale point feature learning. The proposed D-FCN model can therefore process input point cloud with arbitrary sizes and directly predict the semantic labels for all the input points in an end-to-end manner. Without involving additional geometry features as input, the proposed method demonstrates superior performance on the International Society for Photogrammetry and Remote Sensing (ISPRS) 3D labeling benchmark dataset. The results show that our model achieves a new state-of-the-art performance on powerline, car, and facade categories. Moreover, to demonstrate the generalization abilities of the proposed method, we conduct further experiments on the 2019 Data Fusion Contest Dataset. Our proposed method achieves superior performance than the comparing methods and accomplishes an overall accuracy of 95.6% and an average F1 score of 0.810. © 2020 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS)"
"55935063200;25928285500;","Simulation of mixed-phase clouds with the ICON large-eddy model in the complex Arctic environment around Ny-Ålesund",2020,"10.5194/acp-20-475-2020","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078280749&doi=10.5194%2facp-20-475-2020&partnerID=40&md5=65d22280348c39576d5d709f8d77b47b","Low-level mixed-phase clouds have a substantial impact on the redistribution of radiative energy in the Arctic and are a potential driving factor in Arctic amplification. To better understand the complex processes around mixed-phase clouds, a combination of long-term measurements and high-resolution modeling able to resolve the relevant processes is essential. In this study, we show the general feasibility of the new high-resolution icosahedral nonhydrostatic large-eddy model (ICON-LEM) to capture the general structure, type and timing of mixed-phase clouds at the Arctic site Ny-Ålesund and its potential and limitations for further detailed research. To serve as a basic evaluation, the model is confronted with data streams of single instruments including a microwave radiometer and cloud radar and also with value-added products like the CloudNet classification. The analysis is based on a 11&thinsp;d long time period with selected periods studied in more detail focusing on the representation of particular cloud processes, such as mixed-phase microphysics. In addition, targeted statistical evaluations against observational data sets are performed to assess (i) how well the vertical structure of the clouds is represented and (ii) how much information is added by higher horizontal resolutions. The results clearly demonstrate the advantage of high resolutions. In particular, with the highest horizontal model resolution of 75&thinsp;m, the variability of the liquid water path can be well captured. By comparing neighboring grid cells for different subdomains, we also show the potential of the model to provide information on the representativity of single sites (such as Ny-Ålesund) for a larger domain. © Author(s) 2020."
"57202621367;57189296641;55946208600;7103129103;11839001400;35319628300;36018685200;","Daytime rainy cloud detection and convective precipitation delineation based on a deep neural network method using GOES-16 ABI images",2019,"10.3390/rs11212555","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074652871&doi=10.3390%2frs11212555&partnerID=40&md5=a87d5d6056d68cf9edfdb80e10ac6fd2","Precipitation, especially convective precipitation, is highly associated with hydrological disasters (e.g., floods and drought) that have negative impacts on agricultural productivity, society, and the environment. To mitigate these negative impacts, it is crucial to monitor the precipitation status in real time. The new Advanced Baseline Imager (ABI) onboard the GOES-16 satellite provides such a precipitation product in higher spatiotemporal and spectral resolutions, especially during the daytime. This research proposes a deep neural network (DNN) method to classify rainy and non-rainy clouds based on the brightness temperature differences (BTDs) and reflectances (Ref) derived from ABI. Convective and stratiform rain clouds are also separated using similar spectral parameters expressing the characteristics of cloud properties. The precipitation events used for training and validation are obtained from the IMERG V05B data, covering the southeastern coast of the U.S. during the 2018 rainy season. The performance of the proposed method is compared with traditional machine learning methods, including support vector machines (SVMs) and random forest (RF). For rainy area detection, the DNN method outperformed the other methods, with a critical success index (CSI) of 0.71 and a probability of detection (POD) of 0.86. For convective precipitation delineation, the DNN models also show a better performance, with a CSI of 0.58 and POD of 0.72. This automatic cloud classification system could be deployed for extreme rainfall event detection, real-time forecasting, and decision-making support in rainfall-related disasters. © 2019 by the authors."
"57201197300;55668678300;57190943208;55807201200;","Empirical stochastic model of detected target centroids: Influence on registration and calibration of terrestrial laser scanners",2019,"10.1515/jag-2018-0032","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85063464834&doi=10.1515%2fjag-2018-0032&partnerID=40&md5=d549a68356b1e2afdaf244f812a15015","The target-based point cloud registration and calibration of terrestrial laser scanners (TLSs) are mathematically modeled and solved by the least-squares adjustment. However, usual stochastic models are simplified to a large amount: They generally employ a single point measurement uncertainty based on the manufacturers' specifications. This definition does not hold true for the target-based calibration and registration due to the fact that the target centroid is derived from multiple measurements and its uncertainty depends on the detection procedure as well. In this study, we empirically investigate the precision of the target centroid detection and define an empirical stochastic model in the form of look-up tables. Furthermore, we compare the usual stochastic model with the empirical stochastic model on several point cloud registration and TLS calibration experiments. There, we prove that the values of usual stochastic models are underestimated and incorrect, which can lead to multiple adverse effects such as biased results of the estimation procedures, a false a posteriori variance component analysis, false statistical testing, and false network design conclusions. In the end, we prove that some of the adverse effects can be mitigated by employing the a priori knowledge about the target centroid uncertainty behavior. © 2019 Walter de Gruyter GmbH, Berlin/Boston."
"57192669754;56260361400;23466744600;6603934441;","Detecting cloud contamination in passive microwave satellite measurements over land",2019,"10.5194/amt-12-1531-2019","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062857831&doi=10.5194%2famt-12-1531-2019&partnerID=40&md5=2dd18a9e4c28d1a4437bcecf1ae7ed4f","Remotely sensed brightness temperatures from passive observations in the microwave (MW) range are used to retrieve various geophysical parameters, e.g. near-surface temperature. Cloud contamination, although less of an issue at MW than at visible to infrared wavelengths, may adversely affect retrieval quality, particularly in the presence of strong cloud formation (convective towers) or precipitation. To limit errors associated with cloud contamination, we present an index derived from stand-alone MW brightness temperature observations, which measure the probability of residual cloud contamination. The method uses a statistical neural network model trained with the Global Precipitation Microwave Imager (GMI) observations and a cloud classification from Meteosat Second Generation-Spinning Enhanced Visible and Infrared Imager (MSG-SEVIRI). This index is available over land and ocean and is developed for multiple frequency ranges to be applicable to successive generations of MW imagers. The index confidence increases with the number of available frequencies and performs better over the ocean, as expected. In all cases, even for the more challenging radiometric signatures over land, the model reaches an accuracy of ≥ 70% in detecting contaminated observations. Finally an application of this index is shown that eliminates grid cells unsuitable for land surface temperature estimation. © 2019 Author(s)."
"55544219500;57205747739;57205572365;57203102527;57198563843;7401754145;","Intensity data correction for long-range terrestrial laser scanners: A case study of target differentiation in an intertidal zone",2019,"10.3390/rs11030331","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061372900&doi=10.3390%2frs11030331&partnerID=40&md5=035c826672146b9b897e7c1a16e08f60","The intensity data recorded by a terrestrial laser scanner (TLS) contain spectral characteristics of a scanned target and are mainly influenced by incidence angle and distance. In this study, an improved implementable method is proposed to empirically correct the intensity data of long-distance TLSs. Similar to existing methods, the incidence angle-intensity relationship is estimated using some reference targets scanned in the laboratory. By contrast, due to the length limit of indoor environments and the laborious data processing, the distance-intensity relationship is derived by selecting some natural homogeneous targets with distances covering the entire distance scale of the adopted long-distance TLS. A case study of intensity correction and point cloud classification in an intertidal zone in Chongming Island, Shanghai, China, is conducted to validate the feasibility of the improved method by using the intensity data of a long-distance TLS (Riegl VZ-4000). Results indicate that the improved method can accurately eliminate the effects of incidence angle and distance on the intensity data of long-distance TLSs; the coefficient of variation of the intensity data for the targets in the study intertidal zone can be reduced by approximately 54%. The classification results of the study intertidal zone show that the improved method can effectively eliminate the variations caused by the incidence angle and distance in the original intensity data of the same target to obtain a corrected intensity that merely depends on target characteristics for improving classification accuracy by 49%. © 2019 by the authors."
"56250119900;55796882100;","A Comparison of Cloud Classification Methodologies: Differences Between Cloud and Dynamical Regimes",2018,"10.1029/2018JD028595","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85054692208&doi=10.1029%2f2018JD028595&partnerID=40&md5=58569ac832d7119ef6c9ee1fef143246","Classifications of cloud data into Cloud Regimes (CRs) and compositing based on meteorological parameters, Dynamic Regimes (DRs), are often used in the analysis of clouds. We compare CR and DR classifications to understand the relative merits of these approaches and develop a comparison methodology for future studies. We apply the Self-Organizing Map technique to International Satellite Cloud Climatology Project (ISCCP) D1 joint histograms to produce a CR and ERA-Interim pressure vertical velocity output to produce a DR. The CR created improves the separation between high-level CRs compared to previous work. Composites of ISCCP joint histogram data using the DR produce coherent groupings similar to those in the CR scheme particularly in regions of ascent. Both classifications display coherent geographical patterns and reproduce relationships between vertical velocity and cloud properties. However, the CR produces more coherent clusters with higher intracluster similarity and a greater range of independent cloud classes. Independent tests of composites using ISCCP FD output show that the regional variability of longwave cloud radiative effect for particular nodes are significantly higher in the DR than the CR scheme suggesting a poorer classification. Composite mean CloudSat reflectivity-altitude joint histograms represent all major cloud types in the CR scheme, while the current DR grouping is less coherent and misses classes. This suggests that the CR scheme is a more useful classification than the DR scheme based solely on vertical velocity data. Contingency table analysis indicates a low association between these classifications, suggesting combining these schemes would be valuable. ©2018. American Geophysical Union. All Rights Reserved."
"56769708700;35099690000;56422351400;24829272400;8557497200;7004040532;","Large-scale supervised learning for 3D point cloud labeling: Semantic3d.net",2018,"10.14358/PERS.84.5.297","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85047402490&doi=10.14358%2fPERS.84.5.297&partnerID=40&md5=d02677fc489743386ec2bbd11dfeeaf0","In this paper we review current state-of-the-art in 3D point cloud classification, present a new 3D point cloud classification benchmark data set of single scans with over four billion manually labeled points, and discuss first available results on the benchmark. Much of the stunning recent progress in 2D image interpretation can be attributed to the availability of large amounts of training data, which have enabled the (supervised) learning of deep neural networks. With the data set presented in this paper, we aim to boost the performance of CNNs also for 3D point cloud labeling. Our hope is that this will lead to a breakthrough of deep learning also for 3D (geo-) data. The semantic3D.net data set consists of dense point clouds acquired with static terrestrial laser scanners. It contains eight semantic classes and covers a wide range of urban outdoor scenes, including churches, streets, railroad tracks, squares, villages, soccer fields, and castles. We describe our labeling interface and show that, compared to those already available to the research community, our data set provides denser and more complete point clouds, with a much higher overall number of labeled points. We further provide descriptions of baseline methods and of the first independent submissions, which are indeed based on CNNs, and already show remarkable improvements over prior art. We hope that semantic3D. net will pave the way for deep learning in 3D point cloud analysis, and for 3D representation learning in general. © 2018 American Society for Photogrammetry and Remote Sensing."
"56999946500;8602890000;55548014200;55886067800;13404268000;7003995144;6508390183;55704991000;","Multisatellite multisensor observations of a sub-plinian volcanic eruption: The 2015 calbuco explosive event in Chile",2018,"10.1109/TGRS.2017.2769003","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85043393472&doi=10.1109%2fTGRS.2017.2769003&partnerID=40&md5=3bbe13ed8c4ff3411fd59f3277d858ac","A-train satellite data, acquired during the Calbuco volcano (Chile) sub-Plinian eruption in April 2015, are discussed to explore the complementarity of spaceborne observations in the microwave (MW), thermal infrared (TIR), and visible wavelengths for both near-source plume and distal ash clouds. The analysis shows that TIR-based detection techniques are not suitable near the volcanic vent where rising convective columns are associated with large optical depths. Detection and parametric estimates of near-source tephra mass loading and plume height from MW radiometric data, available 69 min after the eruption onset, are proposed. Results indicate a maximum plume altitude of about 21 km above the sea level and an ash mass of 3.65 × 1010 kg, in agreement with mass values obtained from empirical formulas, but less than proximal- distal mass deposit of 1.86 × 1011 kg. This discrepancy may be explained by extrapolating Advanced Technology Microwave Sounder-based estimates to 6 h, thus obtaining a total mass of about 1.90×1011 kg. Distal volcanic cloud retrievals are derived from TIR imagery and results show a good agreement between Moderate-Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) retrievals of total mass taking into account the overpass time shift. If only the overlapping pixels between MODIS and VIIRS are considered, the respective estimates are 1.90 × 109 kg and 1.80 × 109 kg. TIR radiometric estimates of distal ash cloud height and mass loadings are also compared with Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations lidar retrievals. For low-to-medium optically thick ash cloud, average Cloud-Aerosol Lidar with Orthogonal Polarization-derived mass loading is about 0.8 g/m2 against 0.4 g/m2 from VIIRS and 1.4 g/m2 from MODIS. © 2018 IEEE."
"57200088117;57195102154;57204491819;56217320100;8261815100;","PEDESTRIAN PATHFINDING in URBAN ENVIRONMENTS: PRELIMINARY RESULTS",2017,"10.5194/isprs-annals-IV-5-W1-35-2017","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85039456348&doi=10.5194%2fisprs-annals-IV-5-W1-35-2017&partnerID=40&md5=ce14d4251fc61b9fab660d4bd6c43c41","With the rise of urban population, many initiatives are focused upon the <i>smart city</i> concept, in which mobility of citizens arises as one of the main components. Updated and detailed spatial information of outdoor environments is needed to accurate path planning for pedestrians, especially for people with reduced mobility, in which physical barriers should be considered. This work presents a methodology to use point clouds to direct path planning. The starting point is a classified point cloud in which ground elements have been previously classified as roads, sidewalks, crosswalks, curbs and stairs. The remaining points compose the obstacle class. The methodology starts by individualizing ground elements and simplifying them into representative points, which are used as nodes in the graph creation. The region of influence of obstacles is used to refine the graph. Edges of the graph are weighted according to distance between nodes and according to their accessibility for wheelchairs. As a result, we obtain a very accurate graph representing the as-built environment. The methodology has been tested in a couple of real case studies and Dijkstra algorithm was used to pathfinding. The resulting paths represent the optimal according to motor skills and safety."
"7007088807;23988450000;","CMSAF products Cloud Fraction Coverage and Cloud Type used for solar global irradiance estimation",2016,"10.1007/s00703-015-0424-y","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84955589371&doi=10.1007%2fs00703-015-0424-y&partnerID=40&md5=dcf3db6f61ee2f405e46cc85a120f6d5","Two products provided by the climate monitoring satellite application facility (CMSAF) are the instantaneous Cloud Fractional Coverage (iCFC) and the instantaneous Cloud Type (iCTY) products. Previous studies based on the iCFC product show that the simple solar radiation models belonging to the cloudiness index class nCFC = 0.1–1.0 have rRMSE values ranging between 68 and 71 %. The products iCFC and iCTY are used here to develop simple models providing hourly estimates for solar global irradiance. Measurements performed at five weather stations of Romania (South-Eastern Europe) are used. Two three-class characterizations of the state-of-the-sky, based on the iCTY product, are defined. In case of the first new sky state classification, which is roughly related with cloud altitude, the solar radiation models proposed here perform worst for the iCTY class 4–15, with rRMSE values ranging between 46 and 57 %. The spreading error of the simple models is lower than that of the MAGIC model for the iCTY classes 1–4 and 15–19, but larger for iCTY classes 4–15. In case of the second new sky state classification, which takes into account in a weighted manner the chance for the sun to be covered by different types of clouds, the solar radiation models proposed here perform worst for the cloudiness index class nCTY = 0.7–0.1, with rRMSE values ranging between 51 and 66 %. Therefore, the two new sky state classifications based on the iCTY product are useful in increasing the accuracy of solar radiation models. © 2016, Springer-Verlag Wien."
"26655075300;7102171439;56219284300;6603631763;","Subpixel characterization of HIRS spectral radiances using cloud properties from AVHRR",2016,"10.1175/JTECH-D-15-0187.1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84981278166&doi=10.1175%2fJTECH-D-15-0187.1&partnerID=40&md5=f0f320b7ce5438f6e062db35726b2738","This paper describes a cloud type radiance record derived from NOAA polar-orbiting weather satellites using cloud properties retrieved from the Advanced Very High Resolution Radiometer (AVHRR) and spectral brightness temperatures (Tb) observed by the High Resolution Infrared Radiation Sounder (HIRS). The authors seek to produce a seamless, global-scale, long-term record of cloud type and Tb statistics intended to better characterize clouds from seasonal to decadal time scales. Herein, the methodology is described in which the cloud type statistics retrieved from AVHRR are interpolated onto each HIRS footprint using two cloud classification methods. This approach is tested over the northeast tropical and subtropical Pacific Ocean region, which contains a wide variety of cloud types during a significant ENSO variation from 2008 to 2009. It is shown that the Tb histograms sorted by cloud type are realistic for all HIRS channels. The magnitude of Tb biases among spatially coincident satellite intersections over the northeast Pacific is a function of cloud type and wavelength. While the sign of the bias can change, the magnitudes are generally small for NOAA-18 and NOAA-19, and NOAA-19 and MetOp-A intersections. The authors further show that the differences between calculated standard deviations of cloud-typed Tb well exceed intersatellite calibration uncertainties. The authors argue that consideration of higher-order statistical moments determined from spectral infrared observations may serve as a useful long-term measure of small-scale spatial changes, in particular cloud types over the HIRS-AVHRR observing record. © 2016 American Meteorological Society."
"56503083100;56502764100;26536952300;7103333830;","A cloud detection algorithm using the downwelling infrared radiance measured by an infrared pyrometer of the ground-based microwave radiometer",2015,"10.5194/amt-8-553-2015","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84922228119&doi=10.5194%2famt-8-553-2015&partnerID=40&md5=f11baf8f856bd036f6f2071878ef36f9","For better utilization of the ground-based microwave radiometer, it is important to detect the cloud presence in the measured data. Here, we introduce a simple and fast cloud detection algorithm by using the optical characteristics of the clouds in the infrared atmospheric window region. The new algorithm utilizes the brightness temperature (Tb) measured by an infrared radiometer installed on top of a microwave radiometer. The two-step algorithm consists of a spectral test followed by a temporal test. The measured Tb is first compared with a predicted clear-sky Tb obtained by an empirical formula as a function of surface air temperature and water vapor pressure. For the temporal test, the temporal variability of the measured Tb during one minute compares with a dynamic threshold value, representing the variability of clear-sky conditions. It is designated as cloud-free data only when both the spectral and temporal tests confirm cloud-free data. Overall, most of the thick and uniform clouds are successfully detected by the spectral test, while the broken and fast-varying clouds are detected by the temporal test. The algorithm is validated by comparison with the collocated ceilometer data for six months, from January to June 2013. The overall proportion of correctness is about 88.3% and the probability of detection is 90.8%, which are comparable with or better than those of previous similar approaches. Two thirds of discrepancies occur when the new algorithm detects clouds while the ceilometer does not, resulting in different values of the probability of detection with different cloud-base altitude, 93.8, 90.3, and 82.8% for low, mid, and high clouds, respectively. Finally, due to the characteristics of the spectral range, the new algorithm is found to be insensitive to the presence of inversion layers. © Author(s) 2015."
"6603858896;6701633085;","Daytime cloud classification over South American region using multispectral GOES-8 imagery",2015,"10.1080/01431161.2014.978953","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84920990249&doi=10.1080%2f01431161.2014.978953&partnerID=40&md5=eeb01bc55528d8a6c7034fe7e71fcac8","Information about a minimal set of characteristic variables and types of scene was searched for Geostationary Operational Environmental Satellite (GOES)-8 Imager multispectral imagery over an extended area of South America, September 2002, 1600 UTC (near local noon over most parts of the area). Thirteen variables were considered for each pixel: five of them described reflectance and brightness temperature in four channels, three variables assessed temperature difference related to channel 4; finally, five variables assessed local homogeneity (texture) in each channel. Thirty-two clusters were determined by a classification scheme (‘dynamic cluster’) based on minimal Euclidean distance. Factor analysis in principal components (PCs) applied to cluster centroids shows that only five variables might be taken as non-redundant, namely reflectance in channel 1 and brightness temperature in channel 4 as well as their textures, together with difference between channels 5 and 4. Although factor analysis suggests defining about seven clusters (which in turn are consistent with image nephanalysis), PC analysis makes it evident that an objective minimal number of scenes is actually loosely defined but provides some useful criteria for definition of proper centroids, depending on the user’s convenience. © 2014 Taylor & Francis."
"56728284900;56210962100;7409077047;55448001800;","Automatic Tracking and Characterization of Cumulonimbus Clouds from FY-2C Geostationary Meteorological Satellite Images",2014,"10.1155/2014/478419","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84934997683&doi=10.1155%2f2014%2f478419&partnerID=40&md5=eced9354645de7ef05b442edc2a3fa7a","This paper presents an automated method to track cumulonimbus (Cb) clouds based on cloud classification and characterizes Cb behavior from FengYun-2C (FY-2C). First, a seeded region growing (SRG) algorithm is used with artificial neural network (ANN) cloud classification as preprocessing to identify consistent homogeneous Cb patches from infrared images. Second, a cross-correlation-based approach is used to track Cb patches within an image sequence. Third, 7 pixel parameters and 19 cloud patch parameters of Cb are derived. To assess the performance of the proposed method, 8 cases exhibiting different life stages and the temporal evolution of a single case are analyzed. The results show that (1) the proposed method is capable of locating and tracking Cb until dissipation and can account for the eventual splitting or merging of clouds; (2) compared to traditional brightness temperature (TB) thresholds-based cloud tracking methods, the proposed method reduces the uncertainty stemming from TB thresholds by classifying clouds with multichannel data in an advanced manner; and (3) the configuration and developmental stages of Cb that the method identifies are close to reality, suggesting that the characterization of Cb can provide detailed insight into the study of the motion and development of thunderstorms. © 2014 Yu Liu et al."
"35511486700;54792944100;36806577600;","Morphological classification pertaining to validate the climatology and category of thunderstorms over Kolkata, India",2014,"10.1007/s00704-013-0936-7","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84896394660&doi=10.1007%2fs00704-013-0936-7&partnerID=40&md5=75f730c312bf3524a4fb06bf42dbcd61","Devastation due to thunderstorms ensues every year during the pre-monsoon months of April and May over Kolkata (22°32′ N, 88°20′ E), India. Such thunderstorms emerge from large vertical extent of cumulonimbus cloud and are associated with high speed wind squall, at times, exceeding 100 km h-1, deadly lightning flashes and heavy rainfall. The analyses of 102 such thunderstorms have been carried out in this study for morphological classification of thunderstorms over Kolkata, India, during the pre-monsoon season with bulk Richardson number and Byers and Braham theory of 0-6 km wind shear. The result reveals that according, to bulk Richardson number, 85 multicell and 17 supercell thunderstorms, whereas according to Byers and Braham theory, 33 single-cell, 59 multicell and 10 supercell thunderstorms prevailed over Kolkata during the period from 1997 to 2008 in the pre-monsoon season. The coupling of the two approaches together lead to classify the thunderstorms of Kolkata during the pre-monsoon season in two categories: super- and multicell thunderstorms. The result reveals that, out of 102 thunderstorms during the period from 1997 to 2008 in the pre-monsoon season, only 4 are of supercell category and the rest of 98 thunderstorms are of multicell category, depicting the dominance of multicell thunderstorms over Kolkata in the pre-monsoon season. The stability indices and other meteorological parameters are computed and compared for both the categories to identify the threshold ranges pertaining to reveal the climatology and to assess the predictability of both the categories. The result is validated with the observations of India Meteorological Department and Doppler Weather Radar imageries for the years 2009, 2010, 2011, and 2012. © 2013 Springer-Verlag Wien."
"36816070800;8278450900;7004671182;7102643810;8408994300;6507294227;","Infrared satellite precipitation estimate using wavelet-based cloud classification and radar calibration",2010,"10.1109/IGARSS.2010.5649049","https://www.scopus.com/inward/record.uri?eid=2-s2.0-78650904655&doi=10.1109%2fIGARSS.2010.5649049&partnerID=40&md5=bae70d55a7747e1111f278df9c6e3c6d","We have developed a methodology to enhance an infrared-based high resolution rainfall retrieval algorithm by intelligently calibrating the rainfall estimates using space-based observations. Our approach involves the following four steps: 1) segmentation of infrared cloud images into patches; 2) feature extraction using a wavelet-based method; 3) clustering and classification of cloud patches; and 4) dynamic application of brightness temperature (Tb) and rain rate relationships, derived using satellite observations. The results show that using wavelet features along with other features increase the performance of rainfall estimate in terms of quantitative rain/no rain area estimates. In addition, using lightning data as a feature improves the estimates as well. © 2010 IEEE."
"6603156896;7102667512;7102129545;6602578899;20434970400;36130396100;36130514600;","Study of dynamics of the cumulonimbus anvil of large vertical extent",2009,"10.3103/S1068373909120012","https://www.scopus.com/inward/record.uri?eid=2-s2.0-77950473454&doi=10.3103%2fS1068373909120012&partnerID=40&md5=64445ce8d76df982614ceae6fd6e1667","Results are presented of analysis of development of a cumulonimbus cloud anvil with the top height exceeding 18 km, as observed in Central India. The anvil development is studied using the measurements from Meteosat-5 and with the WR-100 radar. Minimum air temperature measured from the satellite at the cloud top level is -105°C. The rate of cloud top height growth is, on average, 0.5 m/s. Horizontal extent of the anvil in the steering flow direction reached 85 km, and rate of the anvil propagation in horizontal direction was equal to 32.4 km/h. The anvil area, after the satellite data, reached 3675 km2. After the radar data, horizontal extent of the anvil reached 50 km, maximum area being 600 km2. Analysis of discrepancies between the satellite and radar measurements of the anvil area shows that a significant part of the anvil cannot be detected by the WR-100 radar because its potential is not enough high; the satellite measurement data improve significantly quality of information on the Cb characteristics. © Allerton Press, Inc., 2009."
"6602107874;6601988721;","Assessment of clouds characteristics from satellite observations by means of self-organized neural networks",1998,"10.1016/S0034-4257(98)00056-X","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0032183195&doi=10.1016%2fS0034-4257%2898%2900056-X&partnerID=40&md5=034d06ef8d903a048bbb80cf7783f4ae","The classification of pixels making up a satellite image is seen here, not only in order to cluster or discriminate these pixels, but exactly as an inversion procedure. The implemented adjustable combination of neural networks (ACNN) technique is built as a combination of self-organized neural networks in order to be particularly robust. After a sensitivity study, this method was considered for the extraction of cloud optical thickness and droplet effective radius from AVHRR imagery. The validation of this algorithm teas conducted in well-documented cases issued from the EUCREX campaigns by comparisons against the CRTVL inversion package developed by Nakajima and Nakajima (1995) and also in situ measurements.The classification of pixels making up a satellite image is seen here, not only in order to cluster or discriminate these pixels, but exactly as an inversion procedure. The implemented adjustable combination of neural networks (ACNN) technique is built as a combination of self-organized neural networks in order to be particularly robust. After a sensitivity study, this method was considered for the extraction of cloud optical thickness and droplet effective radius from AVHRR imagery. The validation of this algorithm was conducted in well-documented cases issued from the EUCREX campaigns by comparisons against the CRTVL inversion package developed by Nakajima and Nakajima (1995) and also in situ measurements."
"6506653130;7004225436;7005995192;57204486611;","A numerical method for synthesizing atmospheric temperature and humidity profiles",1998,"10.1175/1520-0450(1998)037<0718:ANMFSA>2.0.CO;2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0032427244&doi=10.1175%2f1520-0450%281998%29037%3c0718%3aANMFSA%3e2.0.CO%3b2&partnerID=40&md5=b74490175d19e2afe9ab6905c98b96a7","A numerical technique is described for synthesizing realistic atmospheric temperature and humidity profiles. The method uses an ensemble of radiosonde measurements collected at a site of interest. Erroneous profiles are removed by comparing their likelihood with prevailing meteorological conditions. The remaining profiles are decomposed using the method of empirical orthogonal functions. The corresponding eigenprofiles and the statistics of the expansion coefficients are used to numerically generate synthetic profiles that obey the same statistics (i.e., have the same mean, variability, and vertical correlation) as the initial dataset. The technique was applied to a set of approximately 1000 temperature and humidity soundings made in Denver, Colorado, during the winter months of 1991-95. This dataset was divided into four cloud classification categories and daytime and nighttime launches to better characterize typical profiles for the eight cases considered. It was found that 97% of the variance in the soundings could be accounted for by using only five eigenprofiles in the reconstructions. Ensembles of numerically generated profiles can be used to test the accuracy of various retrieval algorithms under controlled conditions not usually available in practice."
"7005780974;7007085866;6603038945;7801535993;","Precipitation estimation with satellites and radar",1997,"10.1016/S0273-1177(97)00055-0","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0030708534&doi=10.1016%2fS0273-1177%2897%2900055-0&partnerID=40&md5=baf51b6c10e804a8cde5676189b562e4","Radar and satellite measurements were simultaneously used for the detailed description of the properties of precipitating clouds. Existing raindrops were detected by digitised data of an MRL-5 radar measuring at 3 and 10 cms. This information was merged with the physical characteristics of precipitating clouds retrieved from the radiance data of the operational meteorological satellites. First, we used infrared and visible METEOSAT data for cloud classification and for a qualitative estimation of precipitation. Second, from the multispectral measurements of the Advanced Very High Resolution Radiometer (AVHRR) on board the polar orbiting NOAA satellites we explicitly derived cloud-top temperature, optical thickness and effective cloud droplet size. We also determined a cloud flag using a thresholding technique. The radar and satellite images were transformed into the same geographic projection format and displayed simultaneously for qualitative evaluation. Empirical relationships were established between coincident ground precipitation measurements, radar data and satellite cloud parameters. The effect of the additional information provided by AVHRR on the quality of the precipitation estimates was evaluated. © 1997 COSPAR."
"7801535993;6602001043;","Cloud classification derived from Meteosat data involving the standard deviation fields of the brightness values",1995,"10.1016/0273-1177(95)00377-Q","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0028794144&doi=10.1016%2f0273-1177%2895%2900377-Q&partnerID=40&md5=6fcf38c7db5fbd53446c5d15b04d1264","Ongoing operational requirements at the Hungarian Meteorological Service (HMS) require the implementation of cloud classification techniques based on satellite data. For use in an operational context these techniques should be fast, accurate and objective. An improved implementation of a fast cloud classification technique based on Meteosat data is described. The main cloud classes are determined on the two-dimensional (IR, VIS) histogram by a dynamic cluster analysis while the cumuliform and layer clouds are separated by a threshold in the standard deviation field. The method was tested for the data of May and September, 1992 comparing the results to surface observations and visual nephanalyses. © 1995."
"7005983909;57217376979;","Tests of spectral cloud classification using DMSP fine mode satellite data.",1980,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-84952075184&partnerID=40&md5=24aa5cc9ee572931e4cd9dac0d2f6dad","A computer-based processor for satellite imagery was tested on samples of DMSP visible and IR imagery data smoothed to 0.6n mi resolution. The data were displayed on the AFGL Man-computer Interactive Data Access System so that meteorologists could label small areas (25 x 25n mi) with one of nine possible cloud categories from AF 3D Nephanalysis Program (3DNEPH). The computer-based processor labeled the same areas by computing a two dimensional fast Fourier transform (FFT) and comparing the results to average wavenumber spectra for the cloud categories. Classification accuracies were 65% for visible, 65% for IR and 81% for combined data. -from STAR, 19(9), 1981"
"7101714152;","Cloud classification from visible and infrared SMS-1 data",1978,"10.1016/0034-4257(78)90011-1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-28344435841&doi=10.1016%2f0034-4257%2878%2990011-1&partnerID=40&md5=5ee9843779474df65d633cded21848e5","The success of a statistical classification-design sample model in discriminating cloud-type samples of visible and infrared meteorological satellite data depends on the selection of the design parameters for the system and the ability of the labelled design samples to characterize and discriminate class patterns within the given geographical region. In a companion study by Parikh (1977), pattern recognition design parameters were examined for a four-class problem and a three-class problem for NOAA-1 cloud data. The purpose of this study was to evaluate pattern recognition systems designed in the previous study on SMS-1 design and test sets. Experiments were conducted for both a four-class problem (separation of ""low"", ""mix"", ""cirrus"", and ""cumulonimbus"" samples) and a three-class problem (separation of ""low"", ""cirrus"", and ""cumulonimbus"" samples). For the four-class problem, decreases in classification accuracy ranging from 4% to 11% occurred when the pattern recognition systems were designed and tested on two different data sets selected from the same satellite orbit. A similar decrease was not observed for the well-defined three-class problem. © 1978."
"57206567978;55535780800;35310657500;","Cloud enabled SDI architecture: a review",2020,"10.1007/s12145-020-00446-9","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079446429&doi=10.1007%2fs12145-020-00446-9&partnerID=40&md5=f60d7fdedafb8655b5cec4bf5356d7ba","With the advancement of GIS technology since its inception in 1960’s, many educational institutions, government departments, public/ private sectors and individuals have started its use for the production and management of spatial data. Spatial Data Infrastructure (SDI) concept was introduced in the early1990’s and provides a set of technologies, standards, protocols, policies and guidelines on the whole cycle of geospatial data production and distributions, i.e., from data capture to storage and to sharing. SDI initiative at national level, termed as National Spatial Data Infrastructure (NSDI), has been taken by different countries including India. Geospatial community is facing various challenges like handling of large volumes of geospatial data, requirement of high computing resources to process geospatial data, scalability and interoperability. Therefore, need of advanced technologies in the form of SDI and cloud computing is realized to resolve the above challenges. Cloud computing has several characteristics like scalability, elasticity and self-provisioning that offers high-performance computing resources to perform geoprocessing efficiently. The main aim of the present paper is to study SDI and its components along with analysis and comparison of NSDI of various countries as well as to conceptualize and discuss service oriented architecture of cloud enabled SDI. Several challenges of the spatial data handling and processing that occurred due to the high intensity of data and lack of processing capability can be solved by adopting proposed cloud enabled SDI architecture. This research will help geospatial community and SDI developers in various perspectives including data sharing and management, interoperability, security and reliability, fault tolerance, mass market solution, upfront cost and global collaboration. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature."
"57211167864;35262555900;57201452442;6508275164;","Investigating the use of satellite-based precipitation products for monitoring water quality in the Occoquan Watershed",2019,"10.1016/j.ejrh.2019.100630","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072860784&doi=10.1016%2fj.ejrh.2019.100630&partnerID=40&md5=c884ea05f7d8a62ce0b152d66b7fe028","Study Region: The Washington D.C area. Study Focus: This work investigates the potential of using satellite-based precipitation products in a hydrological model to estimate water quality indicators in the Occoquan Watershed, located in the suburban Washington D.C area. Three (3) satellite-based precipitation products based on different retrieval algorithms (the Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis, TMPA 3B42-V7; the Climate Prediction Center's CMORPH product; and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Cloud Classification System, PERSIANN−CCS) are compared to gauge-based records over a 5-year period across the study region. The 3 satellite-based precipitation products and the gauge-based dataset are used as input to the Hydrologic Simulation Program FORTRAN (HSPF) hydrology and water quality model. Each satellite precipitation-forced simulation is compared to the reference model simulation forced with the gauge-based observations, in terms of streamflow and water quality indicators, i.e., stream temperature (TW), total suspended solids (TSS), dissolved oxygen (DO), and biological oxygen demand (BOD). New Hydrological Insights for the Region: Results indicate that the spatiotemporal variability observed in the satellite-based precipitation products has a quantifiable impact on both modeled streamflow and water quality indicators. All 3 satellite products present moderate agreements with the reference precipitation and simulation; CMORPH presenting the best overall performance followed closely by TMPA, and PERSIANN presenting a comparatively inferior performance in terms of correlation, root-mean-square error and bias for streamflow and water quality indicators, such as TW, TSS, DO and BOD concentrations. © 2019 The Authors"
"57089599900;54790498500;56452028600;57214139779;","Supervised Fine-Grained Cloud Detection and Recognition in Whole-Sky Images",2019,"10.1109/TGRS.2019.2917612","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078294789&doi=10.1109%2fTGRS.2019.2917612&partnerID=40&md5=70d617446112a975128e1774e6669732","The whole-sky imager has been increasingly used for ground-based cloud automatic observation. Many approaches based on image processing have been applied to detect or classify clouds in whole-sky images (WSIs). However, most of the studies only focus on image segmentation for cloud detection or image classification for cloud recognition separately. The cloud detection only does the binary segmentation (sky and cloud) without cloud types, while the cloud recognition only gives the single image-level label without cloud coverage. In this paper, a fine-grained cloud detection and recognition task with a solution is proposed to fill the gap, which can simultaneously detect and classify clouds in a WSI. It can be regarded as a pixel-level fine-grained dense prediction for images. First, a new data set is built with pixel-level annotation of nine different types. Then, a solution based on supervised learning is proposed, in which the pixel-level prediction problem is converted to a superpixel classification problem. Multiview features are extracted, including color, inside texture, neighbor texture, and global relation, to represent the superpixels. Moreover, a class-specific feature space transformation method based on metric learning and subspace alignment is proposed to overcome the challenge brought by the high similarity among cloud types and the feature shifting. Finally, several experiments have verified that our approach is effective to the challenging new task and also outperforms some other methods in the normal tasks of cloud detection and cloud classification, respectively. © 1980-2012 IEEE."
"57206278550;55695331800;36194585400;56580854000;","LPCCNet: A Lightweight Network for Point Cloud Classification",2019,"10.1109/LGRS.2018.2889472","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85066409297&doi=10.1109%2fLGRS.2018.2889472&partnerID=40&md5=8dd55b368382f7a53ee58489e455cb91","Deep learning has achieved much in image and natural language processing, and related research has also expanded into the field of point cloud processing, and deep networks dedicated to point clouds have emerged; however, in many points cloud applications, networks with fewer parameters and lower computational burdens are required to satisfy the requirements of miniaturisation of devices, so research into lightweight point cloud deep networks is justified. In this letter, we described the design of a lightweight point cloud classification deep learning architecture, lightweight point cloud classification network, with the self-designed block structure as a unit, using a novel index fully dense connectivity method that interconnects all convolutional layers of the network, improving the utilisation of features, thereby reducing the parameter size of each layer; at the same time, the network applies grouping convolution and pruning to the convolutional layer and the fully connected layer, further reducing the amount of parameters and calculation burden. The experimental results show the effectiveness of the network that can achieve comparable results with existing large networks, such as PointNet++, with a smaller amount of parameters. The proposed network offers great promise in mobile device deployment and real-time processing. © 2019 IEEE."
"57190801454;57196021613;57188582312;23004944100;","FEATURE RELEVANCE ANALYSIS for 3D POINT CLOUD CLASSIFICATION USING DEEP LEARNING",2019,"10.5194/isprs-annals-IV-2-W5-373-2019","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85067480190&doi=10.5194%2fisprs-annals-IV-2-W5-373-2019&partnerID=40&md5=e9dc173480ecc83d1aa8eff868e91321","3D point clouds acquired by laser scanning and other techniques are difficult to interpret because of their irregular structure. To make sense of this data and to allow for the derivation of useful information, a segmentation of the points in groups, units, or classes fit for the specific use case is required. In this paper, we present a non-end-to-end deep learning classifier for 3D point clouds using multiple sets of input features and compare it with an implementation of the state-of-the-art deep learning framework PointNet++. We first start by extracting features derived from the local normal vector (normal vectors, eigenvalues, and eigenvectors) from the point cloud, and study the result of classification for different local search radii. We extract additional features related to spatial point distribution and use them together with the normal vector-based features. We find that the classification accuracy improves by up to 33% as we include normal vector features with multiple search radii and features related to spatial point distribution. Our method achieves a mean Intersection over Union (mIoU) of 94% outperforming PointNet++'s Multi Scale Grouping by up to 12%. The study presents the importance of multiple search radii for different point cloud features for classification in an urban 3D point cloud scene acquired by terrestrial laser scanning. © Authors 2019."
"55969140000;55948466000;55247565600;14625770800;","A Selection Criterion for the Optimal Resolution of Ground-Based Remote Sensing Cloud Images for Cloud Classification",2019,"10.1109/TGRS.2018.2866206","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85053597393&doi=10.1109%2fTGRS.2018.2866206&partnerID=40&md5=8de095532fdd10c37a783e7f98798926","In ground-based remote sensing cloud image observation, images with the highest possible resolution are captured to obtain sufficient information about clouds. However, when features are extracted and classification is performed on the basis of the original images, a high-resolution probably means a high (or even more, unacceptable) computation cost. In practical application, a simple and commonly adopted method is to appropriately resize the original image to a version with a decreased resolution. An inevitable problem is whether useful information is lost in this resizing operation. This paper demonstrates that information loss is inevitable and poor classification results may be obtained from the analysis of local binary pattern (LBP) histogram features. However, this problem has been always neglected in previous studies, and the original image is arbitrarily resized without any criterion. In particular, the histogram features based on LBPs actually reflect the distribution of features. Thus, a criterion based on the Kullback-Leibler divergence between LBP histograms from the original and resized images and a penalty term imposed on the resolution are proposed to select the resolution of the resized image. The optimal resolution of the resized image can be selected by minimizing this criterion. Furthermore, experiments based on three ground-based remote sensing cloud image data sets with different original resolutions validate this criterion by analyzing the LBP histogram features. © 1980-2012 IEEE."
"55446881000;55490109900;57196238829;57200697528;57200697860;57200694284;","Performance of near real-time Global Satellite Mapping of Precipitation estimates during heavy precipitation events over northern China",2019,"10.1007/s00704-018-2391-y","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85042225989&doi=10.1007%2fs00704-018-2391-y&partnerID=40&md5=3d6bbeac7d87fa106916e2031134c5c4","This study assesses the performance of near real-time Global Satellite Mapping of Precipitation (GSMaP_NRT) estimates over northern China, including Beijing and its adjacent regions, during three heavy precipitation events from 21 July 2012 to 2 August 2012. Two additional near real-time satellite-based products, the Climate Prediction Center morphing method (CMORPH) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS), were used for parallel comparison with GSMaP_NRT. Gridded gauge observations were used as reference for a performance evaluation with respect to spatiotemporal variability, probability distribution of precipitation rate and volume, and contingency scores. Overall, GSMaP_NRT generally captures the spatiotemporal variability of precipitation and shows promising potential in near real-time mapping applications. GSMaP_NRT misplaced storm centers in all three storms. GSMaP_NRT demonstrated higher skill scores in the first high-impact storm event on 21 July 2015. GSMaP_NRT passive microwave only precipitation can generally capture the pattern of heavy precipitation distributions over flat areas but failed to capture the intensive rain belt over complicated mountainous terrain. The results of this study can be useful to both algorithm developers and the scientific end users, providing a better understanding of strengths and weaknesses to hydrologists using satellite precipitation products. © 2018, Springer-Verlag GmbH Austria, part of Springer Nature."
"57205743850;57069455200;56222085000;55724964400;57205747574;57205736992;57205740579;12140276700;","Content-sensitive multilevel point cluster construction for ALS point cloud classification",2019,"10.3390/rs11030342","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061404369&doi=10.3390%2frs11030342&partnerID=40&md5=cf0f79ce234e4d0879b060c9333bb4c0","Airborne laser scanning (ALS) point cloud classification is a challenge due to factors including complex scene structure, various densities, surface morphology, and the number of ground objects. A point cloud classification method is presented in this paper, based on content-sensitive multilevel objects (point clusters) in consideration of the density distribution of ground objects. The space projection method is first used to convert the three-dimensional point cloud into a two-dimensional (2D) image. The image is then mapped to the 2D manifold space, and restricted centroidal Voronoi tessellation is built for initial segmentation of content-sensitive point clusters. Thus, the segmentation results take the entity content (density distribution) into account, and the initial classification unit is adapted to the density of ground objects. The normalized cut is then used to segment the initial point clusters to construct content-sensitive multilevel point clusters. Following this, the point-based hierarchical features of each point cluster are extracted, and the multilevel point-cluster feature is constructed by sparse coding and latent Dirichlet allocation models. Finally, the hierarchical classification framework is created based on multilevel point-cluster features, and the AdaBoost classifiers in each level are trained. The recognition results of different levels are combined to effectively improve the classification accuracy of the ALS point cloud in the test process. Two scenes are used to experimentally test the method, and it is compared with three other state-of-the-art techniques. © 2019 by the authors."
"57202790343;16206935600;56005218700;57204147598;","Accuracy assessment of deep learning based classification of LiDAR and UAV points clouds for DTM creation and flood risk mapping",2019,"10.3390/geosciences9070323","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85071247538&doi=10.3390%2fgeosciences9070323&partnerID=40&md5=f0389f6ad73a4f17cda7927ccc47c5d6","Digital elevation model (DEM) has been frequently used for the reduction and management of flood risk. Various classification methods have been developed to extract DEM from point clouds. However, the accuracy and computational efficiency need to be improved. The objectives of this study were as follows: (1) to determine the suitability of a new method to produce DEM from unmanned aerial vehicle (UAV) and light detection and ranging (LiDAR) data, using a raw point cloud classification and ground point filtering based on deep learning and neural networks (NN); (2) to test the convenience of rebalancing datasets for point cloud classification; (3) to evaluate the effect of the land cover class on the algorithm performance and the elevation accuracy; and (4) to assess the usability of the LiDAR and UAV structure from motion (SfM) DEM in flood risk mapping. In this paper, a new method of raw point cloud classification and ground point filtering based on deep learning using NN is proposed and tested on LiDAR and UAV data. The NN was trained on approximately 6 million points from which local and global geometric features and intensity data were extracted. Pixel-by-pixel accuracy assessment and visual inspection confirmed that filtering point clouds based on deep learning using NN is an appropriate technique for ground classification and producing DEM, as for the test and validation areas, both ground and non-ground classes achieved high recall (>0.70) and high precision values (>0.85), which showed that the two classes were well handled by the model. The type of method used for balancing the original dataset did not have a significant influence in the algorithm accuracy, and it was suggested not to use any of them unless the distribution of the generated and real data set will remain the same. Furthermore, the comparisons between true data and LiDAR and a UAV structure from motion (UAV SfM) point clouds were analyzed, as well as the derived DEM. The root mean square error (RMSE) and the mean average error (MAE) of the DEM were 0.25 m and 0.05 m, respectively, for LiDAR data, and 0.59 m and –0.28 m, respectively, for UAV data. For all land cover classes, the UAV DEM overestimated the elevation, whereas the LIDAR DEM underestimated it. The accuracy was not significantly different in the LiDAR DEM for the different vegetation classes, while for the UAV DEM, the RMSE increased with the height of the vegetation class. The comparison of the inundation areas derived from true LiDAR and UAV data for different water levels showed that in all cases, the largest differences were obtained for the lowest water level tested, while they performed best for very high water levels. Overall, the approach presented in this work produced DEM from LiDAR and UAV data with the required accuracy for flood mapping according to European Flood Directive standards. Although LiDAR is the recommended technology for point cloud acquisition, a suitable alternative is also UAV SfM in hilly areas. © 2019 by the authors. Licensee MDPI, Basel, Switzerland."
"57202317272;57161909900;56267759500;55705174800;8529014500;","Cloud classification of ground-based infrared images combining manifold and texture features",2018,"10.5194/amt-11-5351-2018","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85053927284&doi=10.5194%2famt-11-5351-2018&partnerID=40&md5=5b6ac6b9da69f1511798bd2233684a88","Automatic cloud type recognition of ground-based infrared images is still a challenging task. A novel cloud classification method is proposed to group images into five cloud types based on manifold and texture features. Compared with statistical features in Euclidean space, manifold features extracted on symmetric positive definite (SPD) matrix space can describe the non-Euclidean geometric characteristics of the infrared image more effectively. The proposed method comprises three stages: pre-processing, feature extraction and classification. Cloud classification is performed by a support vector machine (SVM). The datasets are comprised of the zenithal and whole-sky images taken by the Whole-Sky Infrared Cloud-Measuring System (WSIRCMS). Benefiting from the joint features, compared to the recent two models of cloud type recognition, the experimental results illustrate that the proposed method acquires a higher recognition rate with an increase of 2%-10% on the ground-based infrared datasets.. © Author(s) 2018."
"55173596300;6602600408;55366700000;36339753800;56533742600;","An automated cirrus classification",2018,"10.5194/acp-18-6157-2018","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85046698491&doi=10.5194%2facp-18-6157-2018&partnerID=40&md5=4afecb1df7d1b1fd7cf31db8cd1c6b64","Cirrus clouds play an important role in determining the radiation budget of the earth, but many of their properties remain uncertain, particularly their response to aerosol variations and to warming. Part of the reason for this uncertainty is the dependence of cirrus cloud properties on the cloud formation mechanism, which itself is strongly dependent on the local meteorological conditions. In this work, a classification system (Identification and Classification of Cirrus or IC-CIR) is introduced to identify cirrus clouds by the cloud formation mechanism. Using reanalysis and satellite data, cirrus clouds are separated into four main types: orographic, frontal, convective and synoptic. Through a comparison to convection-permitting model simulations and back-trajectory-based analysis, it is shown that these observation-based regimes can provide extra information on the cloud-scale updraughts and the frequency of occurrence of liquid-origin ice, with the convective regime having higher updraughts and a greater occurrence of liquid-origin ice compared to the synoptic regimes. Despite having different cloud formation mechanisms, the radiative properties of the regimes are not distinct, indicating that retrieved cloud properties alone are insufficient to completely describe them. This classification is designed to be easily implemented in GCMs, helping improve future model-observation comparisons and leading to improved parametrisations of cirrus cloud processes. © 2018 Author(s)."
"57196326681;15064788200;","Fuzzy statistics-based affinity propagation technique for clustering in satellite cloud image",2018,"10.1080/22797254.2018.1482731","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050098292&doi=10.1080%2f22797254.2018.1482731&partnerID=40&md5=c34cd3acf0b5b318bf0a6d004815fc6f","Satellite-remote-sensing technologies have set off improvements in atmospheric research and developments of new tools in prospect of discovery to monitor. Classification of cloud image through satellite is well recognized as a valid approach in many climatic and environmental analyses. A multispectral cloud classifier was implemented to automate the interpretation of Kalpana-1 satellite image. In this paper, a novel image-clustering method, grounded on fuzzy statistics-based affinity propagation (FS-AP), has been proposed. It entails two steps: feature extraction and clustering. The objective is to study the volatility of the FS-AP for the classification of satellite cloud images optimally. Methods for classifying cloud type from satellite images are difficult in terms of efficiency and accuracy. Results show the effectiveness of the proposed technique for classification of satellite cloud image by comparing with fuzzy K-means and affinity propagation. © 2018, © 2018 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group."
"6603699402;57193243349;6701360428;","Methods for automatic cloud classification from MODIS data",2016,"10.1134/S0001433816090061","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85012009183&doi=10.1134%2fS0001433816090061&partnerID=40&md5=db432d72c97ea99e3e5ea11f703d8d25","In this paper, different texture-analysis methods are used to describe different cloud types in MODIS satellite images. A universal technique is suggested for the formation of efficient sets of textural features using the algorithm of truncated scanning of the features for different classifiers based on neural networks and cluster-analysis methods. Efficient sets of textural features are given for the considered classifiers; the cloud-image classification results are discussed. The characteristics of the classification methods used in this work are described: the probabilistic neural network, K-nearest neighbors, self-organizing Kohonen network, fuzzy C-means, and density clustering algorithm methods. It is shown that the algorithm based on a probabilistic neural network is the most efficient. It provides for the best classification reliability for 25 cloud types and allows the recognition of 11 cloud types with a probability greater than 0.7. As an example, the cloud classification results are given for the Tomsk region. The classifications were carried out using full-size satellite cloud images and different methods. The results agree with each other and agree well with the observational data from ground-based weather stations. © 2016, Pleiades Publishing, Ltd."
"23026715600;6701363691;","Statewide mapping and estimation of vegetation aboveground biomass using airborne lidar",2016,"10.1109/IGARSS.2016.7730157","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85007487819&doi=10.1109%2fIGARSS.2016.7730157&partnerID=40&md5=1893266654baedb69fb8c4305348bb48","Airborne lidar has emerged as the most powerful remote sensing technology for mapping vegetation structure, but its application has been limited to landscape level due to the sheer data volume and daunting demand for computation. This study is to break this stereotype and conduct a statewide mapping study of aboveground biomass (AGB) by integrating airborne lidar data and FIA (Forest Inventory and Analysis) plot data for the whole state of Minnesota. We will discuss the challenges and present our solutions for processing big airborne lidar data for point cloud classification, vegetation information extraction, statistical modeling, and uncertainty analysis of forest AGB. © 2016 IEEE."
"38361985300;57206340899;7401516165;","MAPPING URBAN TREE CANOPY COVER USING FUSED AIRBORNE LIDAR and SATELLITE IMAGERY DATA",2016,"10.5194/isprs-annals-III-7-181-2016","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84988037113&doi=10.5194%2fisprs-annals-III-7-181-2016&partnerID=40&md5=0af154d5437e171b51715b171d1f8528","Urban green spaces, particularly urban trees, play a key role in enhancing the liveability of cities. The availability of accurate and up-to-date maps of tree canopy cover is important for sustainable development of urban green spaces. LiDAR point clouds are widely used for the mapping of buildings and trees, and several LiDAR point cloud classification techniques have been proposed for automatic mapping. However, the effectiveness of point cloud classification techniques for automated tree extraction from LiDAR data can be impacted to the point of failure by the complexity of tree canopy shapes in urban areas. Multispectral imagery, which provides complementary information to LiDAR data, can improve point cloud classification quality. This paper proposes a reliable method for the extraction of tree canopy cover from fused LiDAR point cloud and multispectral satellite imagery data. The proposed method initially associates each LiDAR point with spectral information from the co-registered satellite imagery data. It calculates the normalised difference vegetation index (NDVI) value for each LiDAR point and corrects tree points which have been misclassified as buildings. Then, region growing of tree points, taking the NDVI value into account, is applied. Finally, the LiDAR points classified as tree points are utilised to generate a canopy cover map. The performance of the proposed tree canopy cover mapping method is experimentally evaluated on a data set of airborne LiDAR and WorldView 2 imagery covering a suburb in Melbourne, Australia."
"57077110300;56365053700;","Spatial-response matched filter and its application in radiometric accuracy improvement of FY-2 satellite thermal infrared band",2015,"10.1109/TGRS.2014.2359935","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84920990143&doi=10.1109%2fTGRS.2014.2359935&partnerID=40&md5=5848855a2d02d721bd06b8a9dbae03bd","The adjacent effect caused by a nonideal system point spread function (PSF) is an important source generating additional errors in radiometric measurements. Traditional recovery methods, i.e., inverse and Wiener filters, are attempted to restore the scene without the influence of RSF, which is inexistent in real observations and usually leads to the ringing artifact as well as uncertainties in noise amplification (NA). In this paper, a novel spatial-response matched filter (SRMF) and its processing method are established, where the aimed scene is supposed to be observed by instruments with higher PSF performance. Also, NA effects triggered by SRMF are precisely modeled and used for the further evaluation of radiometric accuracy (RA) variation after SRMF processing. Simulation results indicate that the SRMF method is more suitable for images with lower noise level and greater variation in the targets' radiation, where RA performance could be improved in a certain extent. Meanwhile, when the imager in a Multifunctional Transport Satellite is selected as the reference one with high-performance PSF, the SRMF for Fengyun-2 satellite is set up and applied for observations in thermal infrared band during April and May 2013. After SRMF processing, the recovered images show significant improvements in both visual effects and RA, where the increase of RA is expected to be 2-6 K at 190 K in statistics. Such a progress is believed to be beneficial to tropical cyclone intensity estimation as well as for other relevant products, i.e., cloud classification generation. © 1980-2012 IEEE."
"7006802750;43561848300;","Cluster analysis of A-Train data: Approximating the vertical cloud structure of oceanic cloud regimes",2015,"10.1175/JAMC-D-14-0227.1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84943774328&doi=10.1175%2fJAMC-D-14-0227.1&partnerID=40&md5=ea93917c68356d380942a020bd13cd5c","Moderate Resolution Imaging Spectroradiometer (MODIS) data continue to provide a wealth of two-dimensional, cloud-top information and derived environmental products. In addition, the A-Train constellation of satellites presents an opportunity to combine MODIS data with coincident vertical-profile data collected from sensors on CloudSat and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). Approximating the vertical structure of clouds in data-sparse regions can be accomplished through a two-step process that consists of cluster analysis of MODIS data and quantitative analysis of coincident vertical-profile data. Daytime data over the eastern North Pacific Ocean are used in this study for both the summer (June-August) and winter (December-February) seasons in separate cluster analyses. A-Train data from 2006 to 2009 are collected, and a K-means cluster analysis is applied to selected MODIS data that are coincident with single-layer clouds found in the CloudSat/CALIPSO (""GEOPROF-lidar"") data. The resultant clusters, 16 in both summer and winter, are quantified in terms of average cloud-base height, cloud-top height, and normalized cloud water content profile. A cluster and its quantified characteristics can then be assigned to a given pixel in near real-time MODIS data, regardless of its proximity to the observed vertical-profile data. When applied to a two-dimensional MODIS dataset, these assigned clusters can provide an approximate three-dimensional representation of the cloud scene. © 2015 American Meteorological Society."
"56011074300;57216328909;24765645500;","Monitoring drought using multi-sensor remote sensing data in cropland of Gansu Province",2014,"10.1088/1755-1315/17/1/012017","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84902189925&doi=10.1088%2f1755-1315%2f17%2f1%2f012017&partnerID=40&md5=567f3d19729b31708416247a1cc9743d","Various drought monitoring models have been developed from different perspectives, as drought is impacted by various factors (precipitation, evaporation, runoff) and usually reflected in various aspects (vegetation condition, temperature). Cloud not only plays an important role in the earth's energy balance and climate change, but also directly impacts the regional precipitation and evaporation. As a result, the change of cloud cover and cloud type can be used to monitor drought. This paper proposes a new drought composite index, the Drought Composite Index (DCI), for drought monitoring based on multi-sensor remote sensing data in cropland of Gansu Province. This index combines the cloud classification data (CLS) from FY satellite and Vegetation Condition Index (VCI) which was calculated using the maximum and minimum NDVI values for the same time period from Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. Pearson correlation was performed to correlate NDVI, VCI, CLS and DCI values to precipitation data and soil moisture (SM) data collected from 20 meteorological stations during the growing season of 2011 and 2012. Better agreement was observed between DCI and precipitation as compared with that between NDVI/VCI and precipitation, especially the one-month precipitation, and there is an obvious time lag in the response of vegetation to precipitation. In addition, the results indicated that DCI well reflected precipitation fluctuations in the study area promising a possibility for early drought awareness necessary and near real-time drought monitoring."
"56567603500;23004944100;57202620858;57219529090;23090598300;55795804400;7003736832;","Radiometric Correction of Terrestrial LiDAR Data for Mapping of Harvest Residues Density",2013,"10.5194/isprsannals-II-5-W2-133-2013","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84979591135&doi=10.5194%2fisprsannals-II-5-W2-133-2013&partnerID=40&md5=a975caf023e2e60153e90b0f4901215a","In precision agriculture detailed geoinformation on plant and soil properties plays an important role. Laser scanning already has been used to describe in-field variations of plant growth in 3D and over time and can serve as valuable complementary topographic data set for remote sensing, such as deriving soil properties from hyperspectral sensors. In this study full-waveform laser scanning data acquired with a Riegl VZ-400 instrument is used to classify 3D point clouds into post-harvest straw residues and bare soil. A workflow for point cloud based classification is presented using radiometric and geometric point features. A radiometric correction is performed by using a range-correction function <i>f(r)</i>, which is derived from lab experiments with a reference target of known reflectance. Thereafter, the corrected signal amplitude and local height features are explored with respect to the target classes. The following procedure includes feature calculation, decision tree analysis, point cloud classification and finally result validation using detailed classified reference RGB images. The classification tree separates the classes of harvest residues and bare soil with an accuracy of 96% by using geometric and radiometric features. The LiDAR-derived harvest residue coverage value of 75% lies in accordance with the image-based reference (coverage of 68%). The results indicate the high potential of radiometric features for natural surface classification, particularly in combination with geometric features."
"36816070800;7004671182;8278450900;6507294227;","High resolution satellite precipitation estimate using cluster ensemble cloud classification",2011,"10.1109/IGARSS.2011.6049746","https://www.scopus.com/inward/record.uri?eid=2-s2.0-80955136716&doi=10.1109%2fIGARSS.2011.6049746&partnerID=40&md5=a7655000155e8bb55bde44701f403caf","The link-based cluster ensemble (LCE) method is applied to a high resolution satellite precipitation estimation (HSPE) algorithm, a modified form of the Precipitation Estimation from Remotely Sensed Imagery using an Artificial Neural Network Cloud Classification (PERSIANN-CCS) algorithm. The HSPE involves the following four steps: 1) segmentation of infrared cloud images into patches; 2) cloud patch feature extraction; 3) clustering and classification of cloud patches using cluster ensemble technique; and 4) dynamic application of brightness temperature (Tb) and rain rate relationships, derived using satellite observations. The LCE method combines multiple data partitions from different clustering in order to cluster the cloud patches. The results show that using the cluster ensemble increase the performance of rainfall estimates if compared to the HSPE algorithm using Self Organizing Map (SOM). The Heidke Skill Score (HSS) is improved 5% to 7% at medium and high level of rainfall thresholds. © 2011 IEEE."
"55417853000;55386235300;8439180500;7005650812;","The varying response of microwave signatures to different types of overland rainfall found over the korean peninsula",2010,"10.1175/2009JTECHA1364.1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-77953519947&doi=10.1175%2f2009JTECHA1364.1&partnerID=40&md5=8dcfdde926db930b788f07198e8ae588","The Tropical Rainfall Measuring Mission (TRMM) precipitation radar and ground rain measurements were used to investigate the performance of the TRMM Microwave Imager (TMI) land algorithm. In particular, data from a dense network of rain gauges being operated over the Korean Peninsula were utilized. To retrieve information related to the rainfall rate over land, the TRMM land algorithm relies mainly on brightness temperature TB depression at vertically polarized 85(V) GHz because of scattering by ice particles. It refers to the relationships between 85(V)-GHz TBs and rain rates in its predefined database. By comparing the TMI rain rates with the surface rain gauge and TRMM radar measurements, it was found that there are a variety of relationships between 85(V)-GHz TBs and rainfall rates resulting from the various types of precipitating clouds. The TMI land algorithm, therefore, could not resolve some raining clouds such as warm clouds as well as cold clouds having small amounts of ice particles above the rain layer. The rainfall amounts for those missed rain events are significant. As a result, rain rates produced by the land algorithm show systematic biases, which are a function of raining cloud types. Meanwhile, it is found that the 37-GHz TMI channels contain additional information on surface rain; the uncertainties in retrieving rain rates from TBs at TMI frequencies can be reduced up to 11% if all polarized 37-and 85-GHz TBs are used as predictors. © 2010 American Meteorological Society."
"55716181900;7003668116;7403931916;7101677832;37025370400;","Global distribution of instantaneous daytime radiative effects of high thin clouds observed by the cloud profiling radar",2010,"10.1117/1.3491858","https://www.scopus.com/inward/record.uri?eid=2-s2.0-80155147904&doi=10.1117%2f1.3491858&partnerID=40&md5=a7538ed1f6e6e96ebd8a0a96d5d8bf8b","The instantaneous daytime geographical distribution and radiative effects of high thin clouds (optical thickness &lt; 5) are investigated on the basis of the CloudSat Cloud Profiling Radar (CPR) radiative flux and cloud classification products. The regional features of the fraction and radiative effects of high thin clouds are associated with ITCZ, SPCZ and mid-latitude storm track regions. High thin clouds have positive net cloud-induced radiative effect (CRE) at the top of the atmosphere (TOA) and negative net CRE at the bottom of the atmosphere (BOA). The magnitudes of TOA and BOA CREs depend on cloud optical thickness, cloud fraction and geographical location. The magnitude of the net CRE of high thin clouds increases at both TOA and BOA as cloud optical thickness increases. Net CRE at both TOA and BOA contributes to a positive net CRE in-atmosphere and warms the atmosphere regardless of cloud fraction. The global annual mean of the net CRE multiplied by cloud fraction is 0.49 W/m2 at TOA,-0.54 W/m2 at BOA and 1.03 W/m2in-atmosphere. The most radiatively effective cloud optical thickness of a high thin cloud is between 1-2 for the TOA and in-atmosphere CREs or 3-4 for the BOA CRE. © 2010 Society of Photo-Optical Instrumentation Engineers."
"7006444483;6508349802;7102063144;33667732300;","Effects of cloud types on cloud-radiation interaction over the Asian monsoon region",2009,"10.1007/s00704-008-0064-y","https://www.scopus.com/inward/record.uri?eid=2-s2.0-68349096173&doi=10.1007%2fs00704-008-0064-y&partnerID=40&md5=17de5d443c9dffa6d1e8dd0f0eca9aa5","This paper quantifies the sensitivity of radiation budget quantities to different cloud types over the Asian monsoon region using the International Satellite Cloud Climatology Project. Multiple regression was used to estimate the radiative effects of individual cloud type. It was observed that the regression performed better when the solution was constrained with clear sky fluxes, which is evident by an improvement in R2 statistics. The sensitivity coefficients calculated for the Asian monsoon region reveal that, while the LWCRCF and SWCRF will be most vulnerable to changes in cloud cover of deep convective clouds, NETCRF will be susceptible to changes in the nimbostratus clouds. Although the cloud radiative forcing of individual cloud types are found to be similar in sign to previous global findings, their magnitudes are found to vary. It is seen that cirrus clouds play an important role in governing the radiative behavior of this region. © Springer-Verlag 2008."
"6507866559;7006499081;","A method for classification accuracy evaluation for a precipitation-oriented classifier",2005,"10.1080/01431160500166367","https://www.scopus.com/inward/record.uri?eid=2-s2.0-33745105442&doi=10.1080%2f01431160500166367&partnerID=40&md5=3d6ad4bce9a2555455e2c628638b3823","A method of classification accuracy evaluation for a cloud and precipitation classifier applied to geostationary meteorological satellite data is presented. The method has been developed to evaluate the accuracy of a rather precise classification algorithm. The algorithm produces nine classes, four of which involve precipitation. The classes are: (1) clear or insignificant cloud, (2) low thin cloud with no rain, (3) low or middle thin cloud with no rain, (4) low or middle thick cloud with no rain, (5) middle or high cloud with no rain, (6) middle or high cloud with the possibility of rain, (7) middle or high cloud with light-moderate precipitation, (8) middle-high cloud with moderate-heavy precipitation, (9) heavy thunderstorm. The evaluation classifier has been tested for its accuracy (ground truth) using comparison between actual meteorological weather reports and classification results derived from the algorithm applied. For the estimation of classification accuracy, the omission/commission method is applied between the observed and the classification-produced values. The classifier used has proved to be very reliable for classifying major cloud types and precipitation, tested during the synoptic situation of depression systems approaching the south Balkan Peninsula from the west. In that synoptic situation, different intensities of rainfall as well as heavy thunderstorm were present, and the results are very satisfactory. The method can be used to evaluate classification results produced by algorithms applied to meteorological satellite data, classifying precipitation areas as well as the heaviness of precipitation. © 2005 Taylor & Francis."
"8213763800;6701607011;6507014409;8318546000;7004972676;","Charcaterization of the cloud-topped boundary layer at the synoptic scale using AVHRR observations during the SEMAPHORE experiment",2003,"10.1175/1520-0450(2003)042<1720:COTCBL>2.0.CO;2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-16544367589&doi=10.1175%2f1520-0450%282003%29042%3c1720%3aCOTCBL%3e2.0.CO%3b2&partnerID=40&md5=5a2f7e9cb682d22852df09183351e99e","Satellite platforms NOAA-11 and -12 Advanced Very High Resolution Radiometer (AVHRR) data are used during the daytime to study large sheets of stratocumulus over the North Atlanic Ocean. The application concerns an anticyclonic period of the Structure des Echanges Mer-Atmosphère, Propriétés des Hétérogénéités Océaniques: Recherché Expérimentale (SEMAPHORE) campaign (10-17 November 1993). In the region of interest, the satellite images are recorded under large solar zenith angles. Extending the SEMAPHORE area, a region of about 3000 × 3000 km2 is studied to characterize the atmospheric boundary layer. A statistical cloud classification method is applied to discriminate for low-level and optically thick clouds. For AVHRR pixels covered with thick clouds, brightness temperatures are used to evaluate the boundary layer cloud-top temperature (CTT). The objective is to obtain accurate CTT maps for evaluation of a global model. In this application, the full-resolution fields are reduced to match model grid size. An estimate of overall temperature uncertainty associated with each grid point is also derived, which incorporates subgrid variability of the fields and quality of the temperature retrieval. Results are compared with the SEMAPHORE campaign measurements, A comparison with ""DX "" products obtained with the same dataset, but at lower resolution, is also presented. The authors claim that such instantaneous CTT maps could be as intensively used as classical SST maps, and both could be efficiently complemented with gridpoint error-bar maps. They may be used for multiple applications: (i) to provide a means to improve numerical weather prediction and climatological reanalyses, (ii) to represent a boundary layer global characterization to analyze the synoptic situation of field experiments, and (iii) to allow validation and to test development of large-scale and mesoscale models."
"35501489600;7402866314;57206170169;7402635385;","A multilayer fuzzy neural network approach for cloud classification",2003,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-20544459328&partnerID=40&md5=f835c09d0a0d6c2da1515e3641679ade","Physical processes affecting cloud detection have been analyzed considering both cloud segmentation and cloud labeling in satellite data. Application of neural networks to cloud screening is examined with a special emphasis to cloud segmentation in AVHRR data. Some improved methods for analyzing and comparing satellite and surface observations of cloud patterns have been critically discussed. Finally, multilayer perceptron of neural network has been proposed as a possible better model for cloud label classification."
"6507277791;","The seasonal variation of cloud parameters over Central Europe: A fuzzy approach for the analysis of NOAA-APT data",1997,"10.1016/S0169-8095(97)00023-9","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0031420310&doi=10.1016%2fS0169-8095%2897%2900023-9&partnerID=40&md5=d183243843eec31669e247e0c644db77","A method for the retrieval and analysis of cloud parameters from NOAA satellite data is presented. The cloud classification method (FLOP) is a threshold algorithm with fuzzy class boundaries. The class definitions are not based on training data, but on the radiative properties of the cloud types. Three cloud layers are separated: low clouds, middle clouds and high clouds. The clouds in each layer are separated into layered and convective clouds by means of spatial variance analysis in the visible and thermal infrared band. The classification results are verified by comparison to ground observations. FLOP is applied to a time series of NOAA-11 data in APT format which has been recorded and processed daily at the University of Basel between October 1990 and December 1991. The major improvement of FLOP compared to traditional classification methods is the better handling of the uncertainty connected with the classification of clouds with variable radiative properties, such as Cumulus, semi-transparent clouds, and cloud edges. This is achieved by the fuzzification of the cloud classes. The degree of class membership for each pixel is derived in each of the four input bands separately (visible, thermal infrared, visible variance, thermal infrared variance). A fuzzy operator is then applied to obtain the overall degree of class membership for each cloud class in each NOAA scene. For each cloud class, one image per day is stored, the pixel values corresponding to the degree of class membership in percent. From these daily images, mean images for months and seasons are obtained and converted to sharp results. The climatological interpretation of the results shows that the cloud cover patterns over Central Europe are closely related to the general circulation. The temperature gradients between land and ocean surfaces as well as the dynamic and thermal pressure systems have a significant influence not only on cloud cover, but also on cloud fragmentation and top height. In summer and winter, when the meteorological conditions tend to be relatively stable over longer periods, certain weather types are characterized by typical patterns of cloud cover. © 1997 Elsevier Science B.V."
"6508155509;6701403438;","Cloud classification using passive microwave satellite measurements from the SSM/I radiometer",1995,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-0029224219&partnerID=40&md5=39725f056b930a36f856bbe4602e4802","A cloud classification scheme is developed to be used with the SSM/I measurements in association with the METEOSAT classification. Before classifying SSM/I data, an objective technique is applied to enhance spatial resolution of measurements to the resolution of the 37 GHz channel. Different classification algorithms have been performed. The Fuzzy C-Mean algorithm seems to provide the best match with the METEOSAT classification, used as the ground truth."
"7004072935;7006499081;26322992200;","Implementation of self organised neural networks for cloud classification in digital satellite images",1995,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-0029204256&partnerID=40&md5=b6c3978916bec1765909d51a91c6ab06","This paper presents the results of a study where a Self Organised Map (SOM) was used to classify a NOAA-AVHRR satellite image. The neural network was fed with both spectral and spatial features. All five bands were used to extract the median and range of small 'floating' subwindows as well as the average entropy and the range of average entropy. This procedure resulted in a vector with four components per band for each pixel. These vectors were processed by the neural network to obtain a projection into a two-dimensional Kohonen map."
"7401760335;7202997063;","Textural and spectral features as an aid to cloud classification",1988,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-0024185801&partnerID=40&md5=81bb9534deae6f1b0f47ba7912288de4","The problem of classifying clouds seen on meteorological satellite images into different types is considered. Several textural features are studied to determine their discriminating power across a number of cloud classes, including those which have previously been found difficult to separate. The data selected for the study were two 2048 × 1024 subsets of AVHRR (Advanced Very High Resolution Radiometer) images covering a large area (0°W to 20°W and 45°N to 60°N). Although several features in the spatial frequency domain were tested, they were found to be less useful than those in the spatial domain with only one exception. In an effort to minimize the number of features used in any cloud-type classification, several features are recommended on the basis of a sample area of 32 × 32 pixels on NOAA/TIROS images."
"6508111513;","Automated recognition of cloudtypes from satellites and its application to nowcasting",1987,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-0023587110&partnerID=40&md5=e1d1313dd0d2e6a96c642e79af69eec9","An automated cloud classification technique using AVHRR data intended as a tool in the foreasting service is reported. The following objectives are regarded as most essential in the satellite imagery data processing: a) classification of cloud types and precipitation for the diagnosis of current weather development; b) reduction of data volume in order to enable quick transmission to regional weather offices and to other uses; c) analysis of cloud parameters for their effects on radiation, convergence, precipitation, condensation etc in high resolution numerical models. This paper describes possibilities of multispectral classification of AVHRR-data from the polar orbiting NOAA-satellites and how images should influence on the forecast production. -from Author"
"7101714152;7401813766;","Analysis of cloud type and cloud amount during GATE from SMS infrared data",1980,"10.1016/0034-4257(80)90031-0","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0018919237&doi=10.1016%2f0034-4257%2880%2990031-0&partnerID=40&md5=3887e3f219776543d1a1282beaf613ae","Earth-located and edited infrared data from the Synchronous Meteorological Satellite-1 were used to analyze cloud amount and cloud type. A two-threshold weighted histogram model was developed for analysis of cloud amount. Five different cloud-type classes were identified from use of a maximum-likelihood classifier, in which the covariance matrix for each cloud-type class was approximated by the pooled within-groups dispersion matrix. The analysis techniques were developed and tested using satellite and ship data from three days during Phase 3 of the GARP Atlantic Tropical Experiment (GATE). Cloud observations from GATE ships within the A/B scale area were compared with the processed SMS-1 IR data. The cloud coverage derived from the IR data was in very good agreement with cloud amounts obtained from 599 daytime GATE ship observations. The results obtained with the cloud classification technique reflect known limitations of satellite data in specifying cloud systems, and also are limited by problems inherent in the derivation of cloud information from standard meteorological surface observations. © 1980."
"55969097400;57087474200;55577971100;35235290700;57210640789;7402140088;","Accurate cloud detection in high-resolution remote sensing imagery by weakly supervised deep learning",2020,"10.1016/j.rse.2020.112045","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089704906&doi=10.1016%2fj.rse.2020.112045&partnerID=40&md5=7045bd683f689546c2ab3a8014b005f9","Cloud cover is a common and inevitable phenomenon that often hinders the usability of optical remote sensing (RS) image data and further interferes with continuous cartography based on RS image interpretation. In the literature, the off-the-shelf cloud detection methods either require various hand-crafted features or utilize data-driven features using deep networks. Overall, deep networks achieve much better performance than traditional methods using hand-crafted features. However, the current deep networks used for cloud detection depend on massive pixel-level annotation labels, which require a great deal of manual annotation labor. To reduce the labor needed for annotating the pixel-level labels, this paper proposes a weakly supervised deep learning-based cloud detection (WDCD) method using block-level labels indicating only the presence or the absence of cloud in one RS image block. In the training phase, a new global convolutional pooling (GCP) operation is proposed to enhance the ability of the feature map to represent useful information (e.g., spatial variance). In the testing phase, the trained deep networks are modified to generate the cloud activation map (CAM) via the local pooling pruning (LPP) strategy, which prunes the local pooling layers of the deep networks that are trained in the training phase to improve the quality (e.g., spatial resolution) of CAM. One large RS image is cropped into multiple overlapping blocks by a sliding window, and then the CAM of each block is generated by the modified deep networks. Based on the correspondence between the image blocks and CAMs, multiple corresponding CAMs are collected to mosaic the CAM of the large image. By segmenting the CAM using a statistical threshold against a clear-sky surface, the pixel-level cloud mask of the testing image can be obtained. To verify the effectiveness of our proposed WDCD method, we collected a new global dataset, for which the training dataset contains over 200,000 RS image blocks with block-level labels from 622 large GaoFen-1 images from all over the world; the validation dataset contains 5 large GaoFen-1 images with pixel-level annotation labels, and the testing dataset contains 25 large GaoFen-1 and ZiYuan-3 images with pixel-level annotation labels. Even under the extremely weak supervision, our proposed WDCD method could achieve excellent cloud detection performance with an overall accuracy (OA) as high as 96.66%. Extensive experiments demonstrated that our proposed WDCD method obviously outperforms the state-of-the-art methods. The collected datasets have been made publicly available online (https://github.com/weichenrs/WDCD). © 2020 Elsevier Inc."
"57204531700;6603485637;13204996700;55331455800;7005052907;","Post and near real-time satellite precipitation products skill over Karkheh River Basin in Iran",2020,"10.1080/01431161.2020.1739352","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085546158&doi=10.1080%2f01431161.2020.1739352&partnerID=40&md5=84eff89a4b26fe2cf3f8d37ac4f60f5b","Due to high spatial and temporal resolution and near real-time accessibility of satellite precipitation data, the necessity of using these data in the hydrological application seems to be more pressing than ever. In this study, the skill of six post real-time (Climate Hazards Group Infrared Precipitation with Station data (CHIRPS); CPC MORPHing technique (CMORPH); Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN); PERSIANN Climate Data Record (PERSIANN-CDR); precipitation produced from the inversion of the satellite soil moisture (SM) observations derived from the European Space Agency (ESA) Climate Change Initiative (SM2RAIN); Tropical Rainfall Measuring Mission (TRMM 3B42-V7)) and two near real-time (PERSIANN Cloud Classification System (PERSIANN-CCS); TRMM real-time (TRMM 3B42-RT)) satellite daily precipitation products are evaluated by comparing with 28 rain gauges in Karkheh River Basin, located in the semi-arid region of Iran. The evaluation is performed for two types of quantiles (lower quantile (< Q10 and < Q25) and upper quantile (> Q50, > Q75, and > Q95)) and rainy seasons using categorical and quantitative metrics for the period March 2003 to December 2014. The spatial analysis indicated that there is not remarkable variation in the skill of satellite precipitation products across the study area. Results showed that the satellite precipitation estimates are more accurate in lower than upper quantile. The seasonal analysis presented that the skill of satellite precipitation products for fall and spring is slightly higher than winter. For post real-time satellite, in terms of POD (VHI), PERSIANN-CDR in spring (winter and spring), SM2RAIN in winter and spring (fall) shows the best skill, and according to FAR and CSI, CMORPH is the best in all seasons. In addition, VHI and POD of PERSIANN-CCS have better skill than 3B42-RT for near real-time satellite for all seasons. Generally, PERSIANN-CCS (PERSIANN-CDR and SM2RAIN) shows the best skill for near (post) real-time satellite precipitation estimations when whole data are included in the analysis. © 2020, © 2020 Informa UK Limited, trading as Taylor & Francis Group."
"6601958644;57217361489;7102305139;57190175483;55936805100;56543590100;","Hybrid Georeferencing, Enhancement and Classification of Ultra-High Resolution Uav Lidar and Image Point Clouds for Monitoring Applications",2020,"10.5194/isprs-annals-V-2-2020-727-2020","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087135750&doi=10.5194%2fisprs-annals-V-2-2020-727-2020&partnerID=40&md5=17de78e59bcbaef02d92f35e4df23ea2","This paper presents a study on the potential of ultra-high accurate UAV-based 3D data capture by combining both imagery and LiDAR data. Our work is motivated by a project aiming at the monitoring of subsidence in an area of mixed use. Thus, it covers built-up regions in a village with a ship lock as the main object of interest as well as regions of agricultural use. In order to monitor potential subsidence in the order of 10 mm/year, we aim at sub-centimeter accuracies of the respective 3D point clouds. We show that hybrid georeferencing helps to increase the accuracy of the adjusted LiDAR point cloud by integrating results from photogrammetric block adjustment to improve the time-dependent trajectory corrections. As our main contribution, we demonstrate that joint orientation of laser scans and images in a hybrid adjustment framework significantly improves the relative and absolute height accuracies. By these means, accuracies corresponding to the GSD of the integrated imagery can be achieved. Image data can also help to enhance the LiDAR point clouds. As an example, integrating results from Multi-View Stereo potentially increases the point density from airborne LiDAR. Furthermore, image texture can support 3D point cloud classification. This semantic segmentation discussed in the final part of the paper is a prerequisite for further enhancement and analysis of the captured point cloud. © 2020 Copernicus GmbH. All rights reserved."
"55365870900;26423624600;57216636852;57216328909;35234146100;","Semantic Labeling of ALS Point Cloud via Learning Voxel and Pixel Representations",2020,"10.1109/LGRS.2019.2931119","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084158638&doi=10.1109%2fLGRS.2019.2931119&partnerID=40&md5=b45b536db895f94ad405d9f5659e05fa","Semantic labeling is a fundamental task that can provide useful semantics for many other 3-D processing tasks. To tackle the challenge of airborne laser scanning (ALS) point cloud classification, current state-of-the-art methods leverage the capabilities of deep learning. However, they are limited due to the weaknesses of the isolated use of individual representations of point clouds. To address this issue, this letter presents a novel network, VPNet, which ensembles voxel and pixel representation-based networks, to predict class probabilities for each light detection and ranging (LiDAR) point. A fully connected conditional random field-based global refinement is then performed over each point in the point cloud to produce a fine-grained classification result. On the ISPRS 3-D Semantic Labeling Contest, our solution sets a new state of the art by improving the highest average F1-score and the highest average per-class accuracy from 69.3% to 73.9%, and 69.0% to 74.9%, respectively. The overall accuracy of our approach is 84.0%. © 2019 IEEE."
"57191430389;56210720700;57204436627;57211576350;57203553986;26026749200;7003361863;7403247998;7101846027;16308514000;","Stratocumulus cloud clearings: Statistics from satellites, reanalysis models, and airborne measurements",2020,"10.5194/acp-20-4637-2020","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083902538&doi=10.5194%2facp-20-4637-2020&partnerID=40&md5=1c943dc4c0d1800f2336d7e1389e7519","This study provides a detailed characterization of stratocumulus clearings off the US West Coast using remote sensing, reanalysis, and airborne in situ data. Ten years (2009 2018) of Geostationary Operational Environmental Satellite (GOES) imagery data are used to quantify the monthly frequency, growth rate of total area (GRArea), and dimensional characteristics of 306 total clearings. While there is interannual variability, the summer (winter) months experienced the most (least) clearing events, with the lowest cloud fractions being in close proximity to coastal topographical features along the central to northern coast of California, including especially just south of Cape Mendocino and Cape Blanco. From 09:00 to 18:00 (PST), the median length, width, and area of clearings increased from 680 to 1231, 193 to 443, and ~ 67000 to ~ 250000 km2, respectively. Machine learning was applied to identify the most influential factors governing the GRArea of clearings between 09:00 and 12:00 PST, which is the time frame of most rapid clearing expansion. The results from gradient-boosted regression tree (GBRT) modeling revealed that air temperature at 850 hPa (T850), specific humidity at 950 hPa (q950), sea surface temperature (SST), and anomaly in mean sea level pressure (MSLPanom) were probably most impactful in enhancing GRArea using two scoring schemes. Clearings have distinguishing features such as an enhanced Pacific high shifted more towards northern California, offshore air that is warm and dry, stronger coastal surface winds, enhanced lower-tropospheric static stability, and increased subsidence. Although clearings are associated obviously with reduced cloud fraction where they reside, the domain-averaged cloud albedo was actually slightly higher on clearing days as compared to non-clearing days. To validate speculated processes linking environmental parameters to clearing growth rates based on satellite and reanalysis data, airborne data from three case flights were examined. Measurements were compared on both sides of the clear cloudy border of clearings at multiple altitudes in the boundary layer and free troposphere, with results helping to support links suggested by this study s model simulations. More specifically, airborne data revealed the influence of the coastal low-level jet and extensive horizontal shear at cloud-relevant altitudes that promoted mixing between clear and cloudy air. Vertical profile data provide support for warm and dry air in the free troposphere, additionally promoting expansion of clearings. Airborne data revealed greater evidence of sea salt in clouds on clearing days, pointing to a possible role for, or simply the presence of, this aerosol type in clearing areas coincident with stronger coastal winds. © 2020 Copernicus GmbH. All rights reserved."
"57216783694;7402327199;56765122600;","Evaluation of multi-satellite precipitation products and their ability in capturing the characteristics of extreme climate events over the Yangtze River Basin, China",2020,"10.3390/W12041179","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084665481&doi=10.3390%2fW12041179&partnerID=40&md5=ee3a79f79f97ffc7602ecab1e8a6d4cf","Against the background of global climate change and anthropogenic stresses, extreme climate events (ECEs) are projected to increase in both frequency and intensity. Precipitation is one of the main climate parameters for ECE analysis. However, accurate precipitation information for extreme climate events research from dense rain gauges is still diffcult to obtain in mountainous or economically disadvantaged regions. Satellite precipitation products (SPPs) with high spatial and temporal resolution offer opportunities to monitor ECE intensities and trends on large spatial scales. In this study, the accuracies of seven SPPs on multiple spatiotemporal scales in the Yangtze River Basin (YRB) during the period of 2003-2017 are evaluated, along with their ability to capture ECE characteristics. The seven products are the Tropical Rainfall Measuring Mission, Climate Hazards Group InfraRed Precipitation with Station Data (CHIRPS) (25), CHIRPS (05), Climate Prediction Center Morphing (CMORPH), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN)-Climate Data Record, PERSIANN-Cloud Classification System, and Global Precipitation Measurement (GPM) IMERG. Rain gauge precipitation data provided by the China Meteorological Administration are adopted as reference data. Various statistical evaluation metrics and different ECE indexes are used to evaluate and compare the performances of the selected products. The results show that CMORPH has the best agreement with the reference data on the daily and annual scales, but GPM IMERG performs relatively well on the monthly scale. With regard to ECE monitoring in the YRB, in general, GPM IMERG and CMORPH provide higher precision. As regards the spatial heterogeneity of the SPP performance in the YRB, most of the examined SPPs have poor accuracy in themountainous areas of the upper reach. Only CMORPHand GPMIMERG exhibit superior performance; this is because they feature an improved inversion precipitation algorithm for mountainous areas. Furthermore, most SPPs have poor ability to capture extreme precipitation in the estuaries of the lower reach and to monitor drought in the mountainous areas of the upper reach. This study can provide a reference for SPP selection for ECE analysis. © 2020 by the authors. Licensee MDPI, Basel, Switzerland."
"57195625203;12781613300;57216639492;57202621598;","Using training samples retrieved from a topographic map and unsupervised segmentation for the classification of airborne laser scanning data",2020,"10.3390/rs12050877","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081918474&doi=10.3390%2frs12050877&partnerID=40&md5=99abdbcfcfc31d7a555d68d48334087e","The labeling of point clouds is the fundamental task in airborne laser scanning (ALS) point clouds processing. Many supervised methods have been proposed for the point clouds classification work. Training samples play an important role in the supervised classification. Most of the training samples are generated by manual labeling, which is time-consuming. To reduce the cost of manual annotating for ALS data, we propose a framework that automatically generates training samples using a two-dimensional (2D) topographic map and an unsupervised segmentation step. In this approach, input point clouds, at first, are separated into the ground part and the non-ground part by a DEM filter. Then, a point-in-polygon operation using polygon maps derived from a 2D topographic map is used to generate initial training samples. The unsupervised segmentation method is applied to reduce the noise and improve the accuracy of the point-in-polygon training samples. Finally, the super point graph is used for the training and testing procedure. A comparison with the point-based deep neural network Pointnet++ (average F1 score 59.4%) shows that the segmentation based strategy improves the performance of our initial training samples (average F1 score 65.6%). After adding the intensity value in unsupervised segmentation, our automatically generated training samples have competitive results with an average F1 score of 74.8% for ALS data classification while using the ground truth training samples the average F1 score is 75.1%. The result shows that our framework is feasible to automatically generate and improve the training samples with low time and labour costs. © 2020 by the author. Licensee MDPI, Basel, Switzerland."
"57198546889;13103950900;55448001800;57194834916;55613230867;57208292988;57215418505;56119381600;57209601994;57209599054;","Which precipitation product works best in the qinghai-tibet plateau, multi-source blended data, global/regional reanalysis data, or satellite retrieved precipitation data?",2020,"10.3390/rs12040683","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85080879546&doi=10.3390%2frs12040683&partnerID=40&md5=6874ec50022759f4471223af7cc0bbb4","Precipitation serves as a crucial factor in the study of hydrometeorology, ecology, and the atmosphere. Gridded precipitation data are available fromamultitude of sources including precipitation retrieved by satellites, radar, the output of numerical weather prediction models, and extrapolation by ground rain gauge data. Evaluating different types of products in ungauged regions with complex terrain will not only help researchers in applying scientific data, but also provide useful information that can be used to improve gridded precipitation products. The present study aims to evaluate comprehensively 12 precipitation datasets made by raw retrieved products, blended with rain gauge data, and blendedmultiple source datasets inmulti-temporal scales in order to develop a suitablemethod for creating gridded precipitation data in regions with snow-dominated regions with complex terrain. The results show that theMulti-SourceWeighted-Ensemble Precipitation (MSWEP), Global Satellite Mapping of Precipitation with Gauge Adjusted (GSMaP_GAUGE), Tropical RainfallMeasuringMission (TRMM_3B42), Climate Prediction Center Morphing Technique blended with Chinese observations (CMORPH_SUN), and Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) can represent the spatial pattern of precipitation in arid/semi-arid and humid/semi-humid areas of the Qinghai-Tibet Plateau on a climatological spatial pattern. On interannual, seasonal, and monthly scales, the TRMM_3B42, GSMaP_GAUGE, CMORPH_SUN, and MSWEP outperformed the other products. In general, the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN_CCS) has poor performance in basins of the Qinghai-Tibet Plateau. Most products overestimated the extreme indices of the 99th percentile of precipitation (R99), the maximal of daily precipitation in a year (Rmax), and the maximal of pentad accumulation of precipitation in a year (R5dmax). They were underestimated by the extreme index of the total number of days with daily precipitation less than 1 mm (dry day, DD). Compared to products blended with rain gauge data only, MSWEP blended with more data sources, and outperformed the other products. Therefore, multi-sources of blended precipitation should be the hotspot of regional and global precipitation research in the future. © 2020 by the author."
"56203527900;56757045000;15724724300;57204390837;57204883804;","Application of satellite rainfall products for flood inundation modelling in Kelantan River Basin, Malaysia",2019,"10.3390/HYDROLOGY6040095","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079751527&doi=10.3390%2fHYDROLOGY6040095&partnerID=40&md5=a0bab472dd7539a3afe9eabab0feebf4","The advent of satellite rainfall products can provide a solution to the scarcity of observed rainfall data. The present study aims to evaluate the performance of high spatial-temporal resolution satellite rainfall products (SRPs) and rain gauge data in hydrological modelling and flood inundation mapping. Four SRPs, Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM)-Early,-Late (IMERG-E, IMERG-L), Global Satellite Mapping of Precipitation-Near Real Time (GSMaP-NRT), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS) and rain gauge data were used as the primary input to a hydrological model, Rainfall-Runoff-Inundation (RRI) and the simulated flood level and runoff were compared with the observed data using statistical metrics. GSMaP showed the best performance in simulating hourly runoff with the lowest relative bias (RB) and the highest Nash-Sutcliffe efficiency (NSE) of 4.9% and 0.79, respectively. Meanwhile, the rain gauge data was able to produce runoff with-12.2% and 0.71 for RB and NSE, respectively. The other three SRPs showed acceptable results in daily discharge simulation (NSE value between 0.42 and 0.49, and RB value between-23.3% and-31.2%). The generated flood map also agreed with the published information. In general, the SRPs, particularly the GSMaP, showed their ability to support rapid flood forecasting required for early warning of floods. © 2019 by the authors."
"57205651985;57193516410;8502218200;57195735997;6507631512;57211868004;35595209900;","Comparison of aqua/terra MODIS and Himawari-8 satellite data on cloud mask and cloud type classification using split window algorithm",2019,"10.3390/rs11242944","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077910642&doi=10.3390%2frs11242944&partnerID=40&md5=2df9be6e54dd911c1b7467d86187d977","Cloud classification is not only important for weather forecasts, but also for radiation budget studies. Although cloud mask and classification procedures have been proposed for Himawari-8 Advanced Himawari Imager (AHI), their applicability is still limited to daytime imagery. The split window algorithm (SWA), which is a mature algorithm that has long been exploited in the cloud analysis of satellite images, is based on the scatter diagram between the brightness temperature (BT) and BT difference (BTD). The purpose of this research is to examine the usefulness of the SWA for the cloud classification of both daytime and nighttime images from AHI. We apply SWA also to the image data from Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Aqua and Terra to highlight the capability of AHI. We implement the cloud analysis around Japan by employing band 3 (0.469 μm) of MODIS and band 1 (0.47 μm) of AHI for extracting the cloud-covered regions in daytime. In the nighttime case, the bands that are centered at 3.9, 11, 12, and 13 μm are utilized for both MODIS and Himawari-8, with somewhat different combinations for land and sea areas. Thus, different thresholds are used for analyzing summer and winter images. Optimum values for BT and BTD thresholds are determined for the band pairs of band 31 (11.03 μm) and 32 (12.02 μm) of MODIS (SWA31-32) and band 13 (10.4 μm) and 15 (12.4 μm) of AHI (SWA13-15) in the implementation of SWA. The resulting cloud mask and classification are verified while using MODIS standard product (MYD35) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data. It is found that MODIS and AHI results both capture the essential characteristics of clouds reasonably well in spite of the relatively simple scheme of SWA based on four threshold values, although a broader spread of BTD obtained with Himawari-8 AHI (SWA13-15) could possibly lead to more consistent results for cloud-type classification than SWA31-32 based on the MODIS sensors. © 2019 by the authors."
"24832229000;56295385800;57212393572;55724964400;57069455200;57207742903;57208058596;","Point set multi-level aggregation feature extraction based on multi-scale max pooling and LDA for point cloud classification",2019,"10.3390/rs11232846","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076521945&doi=10.3390%2frs11232846&partnerID=40&md5=e19458e86dbed24695f18387ff2f8c54","Accurate and effective classification of lidar point clouds with discriminative features expression is a challenging task for scene understanding. In order to improve the accuracy and the robustness of point cloud classification based on single point features, we propose a novel point set multi-level aggregation features extraction and fusion method based on multi-scale max pooling and latent Dirichlet allocation (LDA). To this end, in the hierarchical point set feature extraction, point sets of different levels and sizes are first adaptively generated through multi-level clustering. Then, more effective sparse representation is implemented by locality-constrained linear coding (LLC) based on single point features, which contributes to the extraction of discriminative individual point set features. Next, the local point set features are extracted by combining the max pooling method and the multi-scale pyramid structure constructed by the point's coordinates within each point set. The global and the local features of the point sets are effectively expressed by the fusion of multi-scale max pooling features and global features constructed by the point set LLC-LDA model. The point clouds are classified by using the point set multi-level aggregation features. Our experiments on two scenes of airborne laser scanning (ALS) point clouds-a mobile laser scanning (MLS) scene point cloud and a terrestrial laser scanning (TLS) scene point cloud-demonstrate the effectiveness of the proposed point set multi-level aggregation features for point cloud classification, and the proposed method outperforms other related and compared algorithms. © 2019 by the authors."
"57193681526;57204516118;55311401100;57037167200;","A novel octree-based 3-D fully convolutional neural network for point cloud classification in road environment",2019,"10.1109/TGRS.2019.2916625","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075617143&doi=10.1109%2fTGRS.2019.2916625&partnerID=40&md5=e42913a52796a6b6e76ad85c0d56b176","The automatic classification of 3-D point clouds is publicly known as a challenging task in a complex road environment. Specifically, each point is automatically classified into a unique category label, and then, the labels are used as clues for semantic analysis and scene recognition. Instead of heuristically extracting handcrafted features in traditional methods to classify all points, we put forward an end-to-end octree-based fully convolutional network (FCN) to classify 3-D point clouds in an urban road environment. There are four contributions in this paper. The first is that the integration and comprehensive uses of OctNet and FCN greatly decrease the computing time and memory demands compared with a dense 3-D convolutional neural network (CNN). The second is that the octree-based network is strengthened by means of modifying the cross-entropy loss function to solve the problems of an unbalanced category distribution. The third is that an Inception-ResNet block is united with our network, which enables our 3-D CNN to effectively learn how to classify scenes containing objects at multiple scales and improve classification accuracy. The last is that an open source data set (HuangshiRoad data set) with ten different classes is introduced for 3-D point cloud classification. Three representative data sets [Semantic3D, WHU_MLS (blocks I and II), and HuangshiRoad] with different covered areas and numbers of points and classes are selected to evaluate our proposed method. The experimental results show that the overall classification accuracy is appreciable, with 89.4% for Semantic3D, 82.9% for WHU_MLS block I, 91.4% for WHU_MLS block II, and 94% for HuangshiRoad. Our deep learning approach can efficiently classify 3-D dense point clouds in an urban road environment measured by a mobile laser scanning (MLS) system or static LiDAR. © 1980-2012 IEEE."
"57195412150;57205353363;7401526171;55331455800;7005052907;57208765667;6701549596;35509463200;56012593900;","Conditional generative adversarial networks (cGANs) for near real-time precipitation estimation from multispectral GOES-16 satellite imageries-PERSIANN-cGAN",2019,"10.3390/rs11192193","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85073408806&doi=10.3390%2frs11192193&partnerID=40&md5=ee563f287877925796524d0b1da5cda2","In this paper, we present a state-of-the-art precipitation estimation framework which leverages advances in satellite remote sensing as well as Deep Learning (DL). The framework takes advantage of the improvements in spatial, spectral and temporal resolutions of the Advanced Baseline Imager (ABI) onboard the GOES-16 platform along with elevation information to improve the precipitation estimates. The procedure begins by first deriving a Rain/No Rain (R/NR) binary mask through classification of the pixels and then applying regression to estimate the amount of rainfall for rainy pixels. A Fully Convolutional Network is used as a regressor to predict precipitation estimates. The network is trained using the non-saturating conditional Generative Adversarial Network (cGAN) and Mean Squared Error (MSE) loss terms to generate results that better learn the complex distribution of precipitation in the observed data. Common verification metrics such as Probability Of Detection (POD), False Alarm Ratio (FAR), Critical Success Index (CSI), Bias, Correlation and MSE are used to evaluate the accuracy of both R/NR classification and real-valued precipitation estimates. Statistics and visualizations of the evaluation measures show improvements in the precipitation retrieval accuracy in the proposed framework compared to the baseline models trained using conventional MSE loss terms. This framework is proposed as an augmentation for PERSIANN-CCS (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network- Cloud Classification System) algorithm for estimating global precipitation. © 2019 by the authors."
"57213188147;57213187904;57213188093;","A Dense Pointnet++ Architecture for 3D Point Cloud Semantic Segmentation",2019,"10.1109/IGARSS.2019.8898177","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077720802&doi=10.1109%2fIGARSS.2019.8898177&partnerID=40&md5=7adb467944aed8f1602d9784756b2b8f","3D point cloud data has been widely used in remote sensing mapping because it is not affected by lighting, shadows and other factors. How to improve the performance of semantic segmentation of 3D point cloud data has attracted more and more attention. Previous works connected shallow features in encoders directly with deep features in decoders, which will lead to semantic gap. In this paper, we propose a Dense PointNet++ architecture, called DPNet, for semantic segmentation of 3D point cloud data. In order to weaken the semantic gap, multiple nested up-sampling layers and a series of cumulative, short and long skip link concatenation are introduced in the network to obtain more abundant point cloud features. Grid map and model fusion are used to further correct the results of network segmentation. The experimental results on US3D data set show that DPNet is superior to existing advanced architectures, especially for the categories with small samples. Moreover, DPNet with grid map and model fusion ranks the first place in 2019 IEEE GRSS Data fusion contest 3D point cloud classification challenge. © 2019 IEEE."
"57204860882;8918197800;57219546794;7006010456;","A local binary pattern classification approach for cloud types derived from all-sky imagers",2019,"10.1080/01431161.2018.1530807","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85057569792&doi=10.1080%2f01431161.2018.1530807&partnerID=40&md5=08329b8fc84c8dbb6cbecfe521da7526","Cloud classification from ground-based observations is a challenging task that attracts increasing attention, favoured by the development of all-sky imaging equipment. In this work, we propose a new method for cloud type classification from all-sky images. Appropriate versions of two descriptors, Regional Local Binary Pattern (R-LBP) and Four Patch-Local Binary Pattern (FP-LBP), are employed on the images in order to extract not only global but also local textural information from the observed cloud type patterns. In the classification stage, a linear Support Vector Machine (SVM) scheme and Linear Discriminant Analysis (LDA) classifiers are adopted for the assignment of the corresponding cloud type label. Our method is evaluated against two state-of-the-art methods and their datasets consisting of 5000 and 2500 images, respectively. According to the results, the proposed method outperforms the previous ones. Due to its promising results and the novelty of local pattern information of clouds, the proposed methodology could be considered as the basis for future studies aiming to overcome the basic disadvantage of all-sky imaging algorithms: to provide regional cloud type information instead of one cloud type for the whole sky. © 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group."
"56785588800;7201944139;57202715771;55733642200;56737387000;7401526171;7005052907;","Predicting floods in a large karst river basin by coupling PERSIANN-CCS QPEs with a physically based distributed hydrological model",2019,"10.5194/hess-23-1505-2019","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85063048364&doi=10.5194%2fhess-23-1505-2019&partnerID=40&md5=9c17a75928f199777c46410caffa6ec0","In general, there are no long-term meteorological or hydrological data available for karst river basins. The lack of rainfall data is a great challenge that hinders the development of hydrological models. Quantitative precipitation estimates (QPEs) based on weather satellites offer a potential method by which rainfall data in karst areas could be obtained. Furthermore, coupling QPEs with a distributed hydrological model has the potential to improve the precision of flood predictions in large karst watersheds. Estimating precipitation from remotely sensed information using an artificial neural network-cloud classification system (PERSIANN-CCS) is a type of QPE technology based on satellites that has achieved broad research results worldwide. However, only a few studies on PERSIANN-CCS QPEs have occurred in large karst basins, and the accuracy is generally poor in terms of practical applications. This paper studied the feasibility of coupling a fully physically based distributed hydrological model, i.e., the Liuxihe model, with PERSIANN-CCS QPEs for predicting floods in a large river basin, i.e., the Liujiang karst river basin, which has a watershed area of 58 270 km-2, in southern China. The model structure and function require further refinement to suit the karst basins. For instance, the sub-basins in this paper are divided into many karst hydrology response units (KHRUs) to ensure that the model structure is adequately refined for karst areas. In addition, the convergence of the underground runoff calculation method within the original Liuxihe model is changed to suit the karst water-bearing media, and the Muskingum routing method is used in the model to calculate the underground runoff in this study. Additionally, the epikarst zone, as a distinctive structure of the KHRU, is carefully considered in the model. The result of the QPEs shows that compared with the observed precipitation measured by a rain gauge, the distribution of precipitation predicted by the PERSIANN-CCS QPEs was very similar. However, the quantity of precipitation predicted by the PERSIANN-CCS QPEs was smaller. A post-processing method is proposed to revise the products of the PERSIANN-CCS QPEs. The karst flood simulation results show that coupling the post-processed PERSIANN-CCS QPEs with the Liuxihe model has a better performance relative to the result based on the initial PERSIANN-CCS QPEs. Moreover, the performance of the coupled model largely improves with parameter re-optimization via the post-processed PERSIANN-CCS QPEs. The average values of the six evaluation indices change as follows: the Nash-Sutcliffe coefficient increases by 14 %, the correlation coefficient increases by 15 %, the process relative error decreases by 8 %, the peak flow relative error decreases by 18 %, the water balance coefficient increases by 8 %, and the peak flow time error displays a 5 h decrease. Among these parameters, the peak flow relative error shows the greatest improvement; thus, these parameters are of page1506 the greatest concern for flood prediction. The rational flood simulation results from the coupled model provide a great practical application prospect for flood prediction in large karst river basins. © 2019 Author(s)."
"57209338786;36499242300;6603115920;57195519405;","Photogrammetric point cloud classification based on geometric and radiometric data integration",2019,"10.1590/s1982-21702019000S00001","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85073479367&doi=10.1590%2fs1982-21702019000S00001&partnerID=40&md5=00ca0246dacf81431405d38c9e4a02ec","The extraction of information from point cloud is usually done after the application of classification methods based on the geometric characteristics of the objects. However, the classification of photogrammetric point clouds can be carried out using radiometric information combined with geometric information to minimize possible classification issues. With this in mind, this work proposes an approach to the classification of photogrammetric point cloud, generated by correspondence of aerial images acquired by Remotely Piloted Aircraft System (RPAS). The proposed approach for classifying photogrammetric point clouds consists of a pixel-supervised classification method, based on a decision tree. To achieve this, three data sets were used, one to define which attributes allow discrimination between the classes and the definition of the thresholds. Initially, several attributes were extracted based on a training sample. The average and standard deviation values for the attributes of each class extracted were used to guide the decision tree definition. The defined decision tree was applied to the other two point clouds to validate the approach and for thematic accuracy assessment. The quantitative analyses of the classifications based on kappa coefficient of agreement, applied to both validation areas, reached values higher than 0.938. © 2019, Universidade Federal do Parana. All rights reserved."
"57210823235;57209325574;57211903687;","An analysis of ground-point classifiers for terrestrial LiDAR",2019,"10.3390/rs11161915","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85071596714&doi=10.3390%2frs11161915&partnerID=40&md5=63f8137411accaee5751110429ee5826","Previous literature has compared the performance of existing ground point classification (GPC) techniques on airborne LiDAR (ALS) data (LiDAR-light detection and ranging); however, their performance when applied to terrestrial LiDAR (TLS) data has not yet been addressed. This research tested the classification accuracy of five openly-available GPC algorithms on seven TLS datasets: Zhang et al.'s inverted cloth simulation (CSF), Kraus and Pfeiffer's hierarchical weighted robust interpolation classifier (HWRI), Axelsson's progressive TIN densification filter (TIN), Evans and Hudak's multiscale curvature classification (MCC), and Vosselman's modified slope-based filter (MSBF). Classification performance was analyzed using the kappa index of agreement (KIA) and rasterized spatial distribution of classification accuracy datasets generated through comparisons with manually classified reference datasets. The results identified a decrease in classification accuracy for the CSF and HWRI classification of low vegetation, for the HWRI and MCC classifications of variably sloped terrain, for the HWRI and TIN classifications of low outlier points, and for the TIN and MSBF classifications of off-terrain (OT) points without any ground points beneath. Additionally, the results show that while no single algorithm was suitable for use on all datasets containing varying terrain characteristics and OT object types, in general, a mathematical-morphology/slope-based method outperformed other methods, reporting a kappa score of 0.902. © 2019 by the authors."
"36491173000;7202269863;55313567200;35612130700;","Using UAV for automatic lithological classification of open pit mining front",2019,"10.1590/0370-44672018720122","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85069743611&doi=10.1590%2f0370-44672018720122&partnerID=40&md5=37ccfcf51f3d5cee0a6c6a03e0d70000","Mine planning is dependent on the natural lithologic features and on the definition of their limits. The geological model is constantly updated during the life of the mine, based on all the information collected so far, plus the knowledge developed from the exploration stage up to the mine closure. As the mine progresses, the amount of available data increases, as well as the experience of the geological modeller and mine planner who deliver the short, medium, and long-term plans. This classical approach can benefit from the automation of the geological mapping on the mining faces and outcrops, improving the speed of repetitious work and avoiding exposure to intrinsic dangers like mining equipment, falling rocks, high wall proximity, among others. The use of photogrammetry to keep up with surface mining activities boarded in UAVs is a reality and the automated lithological classification using machine learning techniques is a low-cost evolution that might present accuracies above 90% of the contact zones and lithologies based on the automated dense point cloud classification when compared to the manual (or reality) classified model. © 2019, Escola de Minas. All rights reserved."
"6603699402;6701360428;","Statistical Model of Physical Parameters of Clouds Based on MODIS Thematic Data",2018,"10.1134/S0001433818090049","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061371583&doi=10.1134%2fS0001433818090049&partnerID=40&md5=b56a8c24239dff53808938fd49c6b78a","Abstract—A statistical model is suggested for the physical parameters of different cloud types. This model has been developed through a comparison of MODIS thematic data with the results of global cloud field classification using neural network technology. The model is a set of one- and two-parametric distributions that describe fluctuations of physical parameters of different cloud types. The distribution parameters are estimated. The comparative analysis is carried out of the parameters under study for different cloud types. The features of different cloud types are determined. The statistical model developed is compared with similar works in this field and international databases; the results show their good consistency. The statistical model suggested can be regarded as a supplement to already existing cloud field models. © 2018, Pleiades Publishing, Ltd."
"56605026200;16416535700;55347249800;6601958160;","Assessing the performance of near real-time rainfall products to represent spatiotemporal characteristics of extreme events: case study of a subtropical catchment in south-eastern Brazil",2018,"10.1080/01431161.2018.1475773","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85056815918&doi=10.1080%2f01431161.2018.1475773&partnerID=40&md5=3cb1d1e6816465322a12b693015288fa","This study evaluates the performance of four Near Real-Time (NRT) satellite rainfall products in estimating the spatiotemporal characteristics of different extreme rainfall events in a subtropical catchment in south-eastern Brazil. The Climate Prediction Centre Morphing algorithm (CMORPH), Tropical Rainfall Measuring Mission, Multisatellite Precipitation Analysis in real time (TMPA-RT), the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Global Cloud Classification System (PERSIANN-GCCS), and the Hydro-Estimator are evaluated for monsoon seasons, based on their capability to represent four types of rainfall events distinguished for: (1) local and short duration, (2) long-lasting event, (3) short and spatial extent, and (4) spatial extent and long lasting. Since the events are defined relative to a percentile, the relative performance variation at different threshold levels (75th, 90th, and 95th) is also evaluated. The data from the 13 Automatic Weather Stations (AWSs) for the period from 2007 to 2014 are used as the reference. The results show that the product performance highly depends on the spatiotemporal characteristics of rainfall events. All four products tend to overestimate intense rainfall in the study area, especially in high altitude zones. CMORPH had the best overall performance to estimate different types of extreme spatiotemporal events. The results allow for developing a better understanding of the accuracy of the NRT products for the estimation of different types of rainfall events. © 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group."
"57190761179;7403352662;","Classification of precipitating clouds using satellite infrared observations and its implications for rainfall estimation",2018,"10.1002/qj.3288","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050890665&doi=10.1002%2fqj.3288&partnerID=40&md5=0d6713d1cdb7d26556aa20a3385db172","Precipitation estimates from satellite infrared (IR) radiometers are typically based on cloud top temperatures. However, these temperatures are weakly related to surface rainfall, particularly for shallow or warm clouds. This study classifies precipitating clouds into five cloud groups. The classification uses three brightness temperature differences (BTDs) and one BTD difference (ΔBTD) from Himawari-8 Advanced Himawari Imager (AHI): BTD1 (6.2–11.2 µm), BTD2 (8.6–11.2 µm), BTD3 (11.2–12.4 µm), and ΔBTD (BTD2 − BTD3). BTD1 is found to be effective for separating shallow and non-shallow clouds in reference to the Global Precipitation Measurement Dual-frequency Precipitation Radar (DPR) level 2 data. Once this separation is complete, non-shallow clouds are further classified. The negative and positive values of ΔBTD usually indicate more water and more ice in clouds, respectively, distinguishing non-shallow clouds with tall and taller cloud heights. Subsequently, BTD1 is applied to non-shallow-tall/taller clouds. Because these clouds can be considered as optically thick, BTD1 identifies the relative coldness of the cloud top based on the extent of water vapour over the cloud top. The final classification yields four non-shallow cloud types: non-shallow-tall-cold, non-shallow-tall-colder, non-shallow-taller-cold, and non-shallow-taller-colder clouds. The relationships between IR brightness temperatures and surface rainfall obtained from the classified cloud groups over four latitude bands reveal clear differences, implying that separating cloud types and accounting for regional differences are desirable to improve the accuracy of IR-based precipitation measurements. © 2018 The Authors. Quarterly Journal of the Royal Meteorological Society published by John Wiley & Sons Ltd on behalf of the Royal Meteorological Society."
"57203728479;7401526171;7005052907;","Bias adjustment of satellite-based precipitation estimation using artificial neural networks-cloud classification system over Saudi Arabia",2018,"10.1007/s12517-018-3860-4","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85052732356&doi=10.1007%2fs12517-018-3860-4&partnerID=40&md5=b10e02a29a6ee16cd2ae3615d7dc44fa","Precipitation is a key input variable for hydrological and climate studies. Rain gauges can provide reliable precipitation measurements at a point of observations. However, the uncertainty of rain measurements increases when a rain gauge network is sparse. Satellite-based precipitation estimations SPEs appear to be an alternative source of measurements for regions with limited rain gauges. However, the systematic bias from satellite precipitation estimation should be estimated and adjusted. In this study, a method of removing the bias from the precipitation estimation from remotely sensed information using artificial neural networks-cloud classification system (PERSIANN-CCS) over a region where the rain gauge is sparse is investigated. The method consists of monthly empirical quantile mapping of gauge and satellite measurements over several climate zones as well as inverse-weighted distance for the interpolation of gauge measurements. Seven years (2010–2016) of daily precipitation estimation from PERSIANN-CCS was used to test and adjust the bias of estimation over Saudi Arabia. The first 6 years (2010–2015) are used for calibration, while 1 year (2016) is used for validation. The results show that the mean yearly bias is reduced by 90%, and the yearly root mean square error is reduced by 68% during the validation year. The experimental results confirm that the proposed method can effectively adjust the bias of satellite-based precipitation estimations. © 2018, Saudi Society for Geosciences."
"36521078000;55818697200;","Automating parameter learning for classifying terrestrial LiDAR point cloud using 2D land cover maps",2018,"10.3390/rs10081192","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85051651871&doi=10.3390%2frs10081192&partnerID=40&md5=6b6f63a991ed171acce1f363e9c79d3b","The automating classification of point clouds capturing urban scenes is critical for supporting applications that demand three-dimensional (3D) models. Achieving this goal, however, is met with challenges because of the varying densities of the point clouds and the complexity of the 3D data. In order to increase the level of automation in the point cloud classification, this study proposes a segment-based parameter learning method that incorporates a two-dimensional (2D) land cover map, in which a strategy of fusing the 2D land cover map and the 3D points is first adopted to create labelled samples, and a formalized procedure is then implemented to automatically learn the following parameters of point cloud classification: the optimal scale of the neighborhood for segmentation, optimal feature set, and the training classifier. It comprises four main steps, namely: (1) point cloud segmentation; (2) sample selection; (3) optimal feature set selection; and (4) point cloud classification. Three datasets containing the point cloud data were used in this study to validate the efficiency of the proposed method. The first two datasets cover two areas of the National University of Singapore (NUS) campus while the third dataset is a widely used benchmark point cloud dataset of Oakland, Pennsylvania. The classification parameters were learned from the first dataset consisting of a terrestrial laser-scanning data and a 2D land cover map, and were subsequently used to classify both of the NUS datasets. The evaluation of the classification results showed overall accuracies of 94.07% and 91.13%, respectively, indicating that the transition of the knowledge learned from one dataset to another was satisfactory. The classification of the Oakland dataset achieved an overall accuracy of 97.08%, which further verified the transferability of the proposed approach. An experiment of the point-based classification was also conducted on the first dataset and the result was compared to that of the segment-based classification. The evaluation revealed that the overall accuracy of the segment-based classification is indeed higher than that of the point-based classification, demonstrating the advantage of the segment-based approaches. © 2018 by the authors."
"23969615900;55778084100;","Warm Season Satellite Precipitation Biases for Different Cloud Types over Western North Pacific",2018,"10.1109/LGRS.2018.2815590","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85045294744&doi=10.1109%2fLGRS.2018.2815590&partnerID=40&md5=b7dbb97bed8b429317fba30d910d3f37","Two along-track (level 2) satellite precipitation retrievals by the Global Precipitation Measurement (GPM) Microwave Imager (GMI) and the Dual Frequency Precipitation Radar Ku-band (DPR-Ku) and two multisatellite precipitation products, global satellite mapping of precipitation (GSMaP) and Integrated Multisatellite Retrievals for GPM (IMERG), are intercompared for different cloud types during warm season over the western North Pacific region. It is found that the biases of the precipitation measurements are systematically associated with cloud types. The best agreements of passive microwave (PMW) products and infrared-based (IR) products with satellite radar-based estimates are found for a relatively weak precipitation range for mid-low clouds (except over land) and high clouds, while similar agreement is found for heavier precipitation range for deep convection regardless of surface type. Precipitation from mid-low clouds over land is considerably underestimated by PMW and IR products over almost the entire intensity range. The IR-based precipitation estimates for deep convective clouds considerably overestimate the intensity for both weak precipitation and cases where precipitation was not detected by the DPR-Ku algorithm. The findings reveal the characteristics of the biases of the products depend on the associated cloud types, which suggests consideration of the cloud type information to improve satellite-based precipitation estimates. © 2004-2012 IEEE."
"57057282400;7101991766;55635837000;","Using UAV technology for landscape classification and mapping in fluvial geomorphology [VyuŽitie uav technolÓgie pre klasifikÁciu a mapovanie krajiny vo fluviÁlnej geomorfolÓgii]",2018,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85049039757&partnerID=40&md5=d4d59097f99f557e860d5bf4f5eb2acd","The aim of this paper is to present the possibilities of UAVs (Unmanned Aerial Vehicles) as photogrammetry payload carriers for data acquisition and fluvial landform identification and mapping. The manual and automatic classification of the Belá River riparian zone for landscape object identification and the analyses of the point cloud density after vegetation filtration was performed. The HEXAKOPTER XL including the Sony NEX 6 camera with 16 – 50 mm lens for landscape monitoring features was used. Data was processed in Agisoft PhotoScan software. The RMSE (root mean square error) of aligned images was 60.121 mm (x coordinate), 43.7584 mm (y coordinate) and 29.46 mm (z coordinate). The resulting point cloud was semiautomatic classified in the software Terrasolid – Terrascan (Microstation), in the following six classes: high vegetation (over 5 m), medium vegetation (from 1.5 m to 5 m), small vegetation (from 0.2 m to 1.5 m), topographic surface and water surface. Orthophotomosaic was classified in ArcGIS software by supervised Maximum Likelihood Classification (MLC). Here training site signatures identified the five land cover categories (water area, bar surface, vegetation, Large Woody Debris – LWD and bare surface). The classification of photogrammetric derived point clouds increases the accuracy elevation model, but on the other hand, does not capture the real terrain and topography under the vegetation. © Geografický ústav SAV/Institute of Geography SAS."
"57193834915;36552612500;","Fog event climatology for Zagreb Airport",2016,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85017022807&partnerID=40&md5=a57cf554c6bf3bbe58d7f7265f896a3e","This work presents a comprehensive study of climatology of fog events at Zagreb Airport. The data used in the study consists of observations from 1994 to 2015, in form of METAR reports. Fog events are classified into five types based on the physical mechanism of formation. The results show a decrease in the annual number of fog events at Zagreb Airport during the last 22 years. Fog is more frequent in the period between September and February, which can be designated as fog season. During spring and summer fog is a relatively rare phenomenon. Fog is usually quite dense; events with a minimum visibility of over 200 m occurred in only 29% of cases. Radiation fog is the dominant type of fog. The analysis has also shown that advective fog is very rare during summer, while precipitation fog and cloud base lowering fog occur only during fall and winter. All fog types except evaporation fog have a similar distribution of duration. Radiation fog and advective fog are the densest types; precipitation fog is the least dense type. A closer analysis of radiation fog has provided data on the annual/diurnal distribution of frequencies of onset and dissipation, wind during onset or dissipation, and persistence."
"57189490508;8569329300;7003401367;22983991300;57191266040;57189270098;26430815000;22985483300;7003686951;","Cloud and cloud shadow masking of high and medium resolution optical sensors-an algorithm inter-comparison example for Landsat 8",2016,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-84988452215&partnerID=40&md5=3c1befe362880129108734bd9e04cbab","Image processing for satellite water quality products requires reliable cloud and cloud shadow detection and cloud classification before atmospheric correction. Within the FP7/HIGHROC (""HIGH spatial and temporal Resolution Ocean Colour"") Project, it was necessary to improve cloud detection and the cloud classification algorithms for the spatial high resolution sensors, aiming at Sentinel 2 and using Landsat 8 as a precursor. We present a comparison of three different algorithms, AFAR developed by RBINS; ACC Am created by VITO, and IDEPIX developed by Brockmann Consult. We show image comparisons and the results of the comparison using a pixel identification database (Pix Box); FMASK results are also presented as reference."
"56038387000;7402477039;","Automatic Cloud Removal from Multitemporal Satellite Images",2015,"10.1007/s12524-014-0396-2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84941731260&doi=10.1007%2fs12524-014-0396-2&partnerID=40&md5=eef062e58464e5bde97a6ed75254b9d2","Remote sensing images are more or less influenced by clouds and cloud shadows during the data acquisition, which pose a major challenge in data processing. As a result, many researchers have come up with different methods to detect and remove the clouds and their shadows from remote sensing images. In this paper, an automatic cloud removal algorithm is proposed to generate cloud-free and cloud shadow-free images from multi temporal registered remote-sensing images. An automatic cloud detection and a shadow detection algorithm is combined in this method. The quality assessment of multitemporal images based on SSIM index is used to sort the images. Information cloning is used to fill the cloud-covered areas in the satellite image. For each cloud contaminated area, the corresponding cloud free areas are selected from sorted multitemporal images to reconstruct the clouds without any visible seams. Experimental analysis is performed on Landsat-7 Enhanced Thematic Mapper Plus (ETM+) sensor images and results are obtained. Experimental results proved the better performance of proposed method. Both the thin and thick clouds can be removed efficiently using the proposed method. © 2014, Indian Society of Remote Sensing."
"57191169028;56257589900;7004571374;","A novel angular filter based LiDAR point cloud classification",2015,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-84943555228&partnerID=40&md5=b8e4b8c845fb4613bbc206e99c223037","We consider the problem of ground filtering of LiDAR point cloud by utilizing geometric properties of the scene area. We propose a new approach for detecting man-made object edges from the elevation profile using a novel angle filtering method. This method analyzes neighbors from two nearby tiers, which alleviates the need for multiple gradient calculations from different directions. A subsequent connected component and convexhull analysis separate all planar surfaces from the detected edges. These separated planar surfaces provide information about objects' geometry. All objects are separately analyzed to reduce the error around the border region, which is prominent in several existing ground filtering algorithms. Experimental results are shown for a complex urban scene, where complicated building structure is present."
"6507257485;56199424800;57125896900;56200183300;56201081100;6507478947;","New indicators for global crop monitoring in CropWatch -case study in North China Plain",2014,"10.1088/1755-1315/17/1/012050","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84902138179&doi=10.1088%2f1755-1315%2f17%2f1%2f012050&partnerID=40&md5=f702b277499758f0197de06e1a8a8c55","CropWatch is a monitoring system developed and operated by the Institute of Remote Sensing and Digital Earth (Chinese Academy of Sciences) to provide global-scale crop information. Now in its 15th year of operation, CropWatch was modified several times to be a timely, comprehensive and independent global agricultural monitoring system using advanced remote sensing technology. Currently CropWatch is being upgraded with new indicators based on new sensors, especially those on board of China Environmental Satellite (HJ-1 CCD), the Medium Resolution Spectral Imager (MERSI) on Chinese meteorological satellite (FY-3A) and cloud classification products of FY-2. With new satellite data, CropWatch will generate new indicators such as fallow land ratio (FLR), crop condition for irrigated (CCI) and non-irrigated (CCNI) areas separately, photosynthetically active radiation (PAR), radiation use efficiency for the photosynthetically active radiation (RUEPAR) and cropping index (CI) with crop rotation information (CRI). In this paper, the methods for monitoring the new indicators are applied to the North China Plain which is one of the major grain producing areas in China. This paper shows the preliminary results of the new indicators and methods; they still need to be thoroughly validated before being incorporated into the operational CropWatch system. In the future, the new and improved indicators will help us to better understand the global situation of food security."
"36816070800;7004671182;8278450900;6507294227;","Enhancement of satellite precipitation estimation via unsupervised dimensionality reduction",2012,"10.1109/TGRS.2012.2189406","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84867054372&doi=10.1109%2fTGRS.2012.2189406&partnerID=40&md5=1f3d4b1d8ea7d87e1caddabbe4f77ff8","A methodology to enhance satellite precipitation estimation using unsupervised dimensionality reduction (UDR) techniques is developed. This enhanced technique is an extension to the precipitation estimation from remotely sensed imagery using an artificial neural network (PERSIANN) and cloud classification system (CCS) method (PERSIANN-CCS) enriched using wavelet features combined with dimensionality reduction. Cloud-top brightness temperature measurements from the Geostationary Operational Environmental Satellite (GOES)-12 are used for precipitation estimation at 4 km $\times$ 4 km spatial resolutions every 30 min. The study area in the continental U.S. covers parts of Louisiana, Arkansas, Kansas, Tennessee, Mississippi, and Alabama. Based on quantitative measures, root mean square error and Heidke skill score (HSS), the results show that the UDR techniques can improve the precipitation estimation accuracy. In addition, the independent component analysis is shown to have better performance than other UDR techniques; and in some cases, it achieves 10% improvement in the HSS. © 2012 IEEE."
"6603299074;6508011000;35810682500;36116327200;","Automated radar identification, measurement of parameters, and classification of convective cells for hail protection and storm warning",2010,"10.3103/S1068373910030040","https://www.scopus.com/inward/record.uri?eid=2-s2.0-77953408798&doi=10.3103%2fS1068373910030040&partnerID=40&md5=6d88573fda2a184619e5b72bd68cc033","Methods, algorithms, and programs of automated radar identification of convective cells in cumulonimbus clouds are considered, that provides the ability to measure the parameters of convective cells, to plot the graphs of the time course of parameters, to compute the direction and speed of the movement, to assess their thunderstorm and hail danger, and to recognize the categories of target objects for the prevention of hail damage and meteorological provision of aviation. © 2010 Allerton Press, Inc."
"57214531307;7004489385;7402296705;7201863347;","Operational cloud classification for the Iberian Peninsula using Meteosat Second Generation and AQUA-AIRS image fusion",2010,"10.1080/01431160902882553","https://www.scopus.com/inward/record.uri?eid=2-s2.0-77649116336&doi=10.1080%2f01431160902882553&partnerID=40&md5=42a576032af7a887efc674b0d33f7468","The aim of this work was the adaptation and improvement of a previous cloud detection and classification algorithm that was developed for the Meteosat-7 satellite. The functions of this satellite have now been taken on by the new series of Meteosat Second Generation (MSG) satellites, which are not just replicas but new, much improved versions of their predecessor. The formerly used Advanced/Tiros-N Operational Vertical Sounder (A/TOVS) probe has also been superseded technologically by new sensors with better spatial resolution, capable of carrying out more accurate measurements at a greater number of wavelengths. This is the case of the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the TERRA and AQUA satellites and of the Atmospheric Infrared Sounder (AIRS) probe. In this context, new potential improvements are analysed for this algorithm by using these new platforms and sensors and the results are compared to those obtained in the first classification. © 2010 Taylor & Francis."
"56154529000;","Cloud type observations and trends in Canada, 1953-2003",2008,"10.3137/ao.460302","https://www.scopus.com/inward/record.uri?eid=2-s2.0-64849092835&doi=10.3137%2fao.460302&partnerID=40&md5=d2812b8e8172d44e952a3dee0c25ce67","The monitoring of cloud amount and type in Canada is discussed in detail, including observing, archiving, data transmission procedures and practices, and automation. There have been some major monitoring challenges since 1953. In 1977, the network-wide replacement of detailed cloud layer amounts and obscuring phenomena by broad sky conditions, based on summation amounts, imposed analysis of frequency of occurrence of mainly cloudy conditions rather than actual amounts. Partial automation with Automated Weather Observing Systems resulted in the cessation of observations of higher clouds and cloud types, as well as the incompatibility of sky coverage with human observations at eight percent of stations. For every hourly report from eighty-four airport stations from 1953 to 2003, each layer is classified according to cloud type and related standard base height into three levels of low, middle, and high clouds. Trends in occurrence of summation amounts of mainly cloudy conditions at each of these three levels are computed on annual, seasonal, daytime and nighttime scales, together with annual trends in occurrence of selected convective and stratiform clouds. Based on annual anomalies averaged over the country and provinces, no major network-wide systematic discontinuities were noted; on average, on an annual basis over the entire network, slight decreasing trends are noted for summation amounts of mainly cloudy conditions at low and middle levels, and increasing trends at high levels. The increasing trend at high levels is indeed remarkable. The rate of increase, especially rapid until 1974, has been shown to be caused by a prominent increase in cirrus cloud reports. The link between this rise and the increase in air traffic was established by others in the United States. This link may also apply in Canada, which experienced a similar expansion in aviation. Notably, the largest increase in high nighttime cloudiness and decrease in low-middle cloudiness is evident in western Canada, possibly contributing to the recently observed warming of daily minimum and maximum temperatures there. The occurrence of stratiform clouds at all levels exhibits significant decreasing trends across the country, except for southern Ontario. Clouds of intense convection show pronounced decreasing trends in western Canada, while not much change is evident elsewhere. Similar to cirrus, stratocumulus is notable as it shows strong positive trends everywhere in the country. On the other hand combined stratus and stratus fractus clouds exhibit decreasing trends, except over British Columbia where the opposite occurs. The findings concerning stratocumulus, stratus and stratus fractus clouds in Canada are similar to the findings in the United States."
"16634679000;","Analysis of physical parameters measured during the ECRIN 96 Experiment",2008,"10.1016/j.atmosres.2007.09.008","https://www.scopus.com/inward/record.uri?eid=2-s2.0-44649182144&doi=10.1016%2fj.atmosres.2007.09.008&partnerID=40&md5=83af3be0e20e52a37782858c9f499d90","A general analysis of stratocumulus observations performed during the ECRIN96 Experiment is presented from the threefold point of view of vertical variability, statistical and spectral fluctuations, which provide information aiming at vertical profile and 3D modelling of these clouds. The analysis of vertical variability confirms essential features of stratocumulus retrieved via the constrained water cloud generator we developed elsewhere (Berton, R.P.H., 2008-this volume. Constrained water cloud generator. Atmos. Res., Paper I). Temperature gradients are found in good agreement with theoretical values in the bound of accuracy tolerance. The temperature difference at the cloud top is also consistent with model estimations and the maximum liquid water content is close to the result of our adiabatic modelling. The statistical analysis performed on the vertical velocity proves the importance of distribution skewness and shows that for both clouds the fraction of updrafts is larger than 0.5 and correlated with negative skewness as pointed out by several authors. Moreover, a relationship between the skewness and the kurtosis established by other authors for vertical velocity realisations is also shown by our data set, but for six parameters and with a different constant. Eventually, the spectral analysis leads to a classification of physical parameters in three classes according to the spectral index β: absolute humidity (β ≈ 5/3), temperature and vertical velocity (β ≈ 4/3), liquid water content and kinetic energy (β ≈ 2/3 to 1). In particular, the liquid water content (β ≈ 1) does not follow Kolmogorov law (β = 5/3). Instead we find this latter value for absolute humidity. Nevertheless, our result for kinetic energy spectra (β ≈ 2/3) is consistent with published analysis of turbulence in stratocumulus. © 2007 Elsevier B.V. All rights reserved."
"6603045912;8645885900;","MERIS cloud masks: Exploration and visualisation of MERIS spectra",2004,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-22144454608&partnerID=40&md5=b6b5fdeb6dba33cafda8de2b1c0a0845","In this work we present a cloud mask for MERIS, developed as part of the EU NAOC project, which can discriminate between optically thick and thin clouds. The method is based on the expert selection of a small labelled data set of cloudy and cloud free pixels in MERIS observations taken over the ocean, guided by meteorological knowledge. This small labelled data set is augmented by a larger unlabelled data set randomly extracted from a number of MERIS scenes over the ocean. This unlabelled data set is used to characterise the structure of the MERIS spectra that are observed, using pattern recognition methods called the generative topographic mapping and Neuroscale. The generative topographic mapping constructs a density model for the 16 dimensional (i. e. the MERIS bands and a ratio between the radiances at the 11th and the 10 th bands) data in a lower (typically 2) dimensional latent space, which allows visualisation and understanding of the structure and distribution of the data. The Neuroscale algorithm is a distance preserving data projection algorithm without a density model. The lower dimensional structure is then used to define a non-linear projection, which retains information, but permits the construction of simpler classification models, something that will be especially important with future hyper-spectral instruments. We show the results of our cloud classification on several MERIS scenes and contrast our cloud mask with the standard MERIS cloud mask."
"6507866559;7006499081;","Cloud and precipitation classification for a depression system approaching the south Balkan Peninsula. A case study of 26 March 1998",2004,"10.1080/01431160410001709057","https://www.scopus.com/inward/record.uri?eid=2-s2.0-8744229976&doi=10.1080%2f01431160410001709057&partnerID=40&md5=8bed19e6002530a057a55f2af167814f","A method is presented that has been developed for classifying cloud and precipitation for a specific synoptic situation affecting south-east Europe during winter and spring: a depression system that gives rise to a heavy rainfall weather situation. The classification algorithm used for classifying clouds and precipitation is based on a combination of spectral channels and the multispectral box classification technique using visible and infrared Meteosat data. In order to increase the accuracy of the classification algorithm, the appropriate cloud types are selected according to the specific synoptic weather situation. The classification algorithm produces nine classes, four of which involve precipitation. These are: (1) clear or insignificant clouds, (2) low thin cloud with no rain, (3) low or middle thin cloud with no rain, (4) low or middle thick cloud with no rain, (5) middle or high cloud with no rain, (6) middle or high cloud with the possibility of rain, (7) middle or high cloud with light-moderate precipitation, (8) middle-high cloud with moderate-heavy precipitation, (9) heavy thunderstorm. The classifier has been tested for its accuracy (ground truth) using comparisons between actual meteorological weather reports and classification results derived from the algorithm applied. For the estimation of classification accuracy, the omission/commission method is applied between the observed and the classification produced values. The case study of 26 March 1998 is presented. The classifier used has proved to be very reliable for classifying major cloud types and precipitation during the synoptic situation of depression systems approaching the south Balkan peninsula from the west. It has been tested for different intensities of rainfall as well as for heavy thunderstorms and the results are very satisfactory. The results produced can be used to support the forecaster's daily work. The need for the extraction of such classification products lies in the fact that detailed weather information is demanded for regions in Greece devoid of surface observations, such as the Aegean Sea, the Ionian Sea, the South Cretan Sea and remote mountainous areas. That information has been found to be useful, especially in cases where significant weather systems are approaching Greece from the west or south-west, where surface observations over the sea are not available and an early knowledge of the heaviness of precipitation is needed, even before that weather system is in range of any land-based weather radar. Moreover, the single use of isolated channels (i.e. infrared or visible) could involve the danger of errors in the interpretation of the satellite image. © 2004 Taylor & Francis Ltd."
"55953510100;35561327100;7202105404;","Fuzzy Rule Based Approaches for Cloud Cover Estimation Using METEOSAT 5 Images",2003,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-0242540947&partnerID=40&md5=4d1f6d502ea2949da873262a01618757","A fuzzy rule based cloud classification scheme is proposed to estimate the cloud cover using METEOSAT 5 visible imagery over Indian subcontinent and Indian Ocean. The technique, which is a supervised one, automatically finds out some human understandable rules (fuzzy rules) for classifying each pixel into one of the three possible classes: clear sky, cloudy sky and partially cloudy sky. In this regard, temporal and spatial properties of the data are being explored. The scheme is tested on images other than the training image(s) and the performance is found to be quite satisfactory."
"6506621914;7003993113;7005461772;6601972980;","Remote sensing of cloud cover in the Arctic region from AVHRR data during the ARTIST experiment",2003,"10.1080/01431160304994","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0037429321&doi=10.1080%2f01431160304994&partnerID=40&md5=b3bf74e6d245e8c15b401958baffaf82","In this paper we present a cloud detection algorithm developed for the Arctic region using Advanced Very High Resolution Radiometer (AVHRR) data. Our approach is a simplified version of the Ebert method to discriminate between clouds, ice and open water in the Arctic Sea. The algorithm is tuned to work on an AVHRR scene typical of the winter to spring transition period. The algorithm has been applied to 1 month (154 scenes) of NOAA-14 AVHRR images (from 16 March to 15 April 1998) covering the region of the Arctic Sea near the Svalbard Islands. The cloud detection results are analysed using various check procedures. The algorithm's pixel classification performance was verified by a satellite image expert. The misclassified pixels were digitalized on the image and counted by the expert in order to quantify the algorithm's accuracy. The cloud classification results are quite accurate: 70% of the images (109) have an error less than 5% and only 11% of the image results have an error greater than 10%. The method's performance is also tested against independent cloud and ice observations obtained, respectively, from the Ny-Alesund meteorological base and from the Special Sensor Microwave/Imager (SSM/I) dataset. The comparison with these independent sources of data confirms the algorithm's good performance."
"55448001800;36656565300;35304280300;36478811600;","Study on cloud classifications by using AVHRR, GMS-5 and TERRA/MODIS satellite data",2002,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-0036353144&partnerID=40&md5=b99cb7a8b72298752622520ad8772691","This paper presents the automated pixel-scale neural network classification methods being developed at National Satellite Meteorological Center (NSMC) of China to classify clouds by using NOAA/AVHRR and GMS-5 satellite imageries. By using Terra satellite MODIS imageries, an automated pixel-scale threshold technique has been developed to detect and classify clouds. The study focuses on applications of these cloud classification techniques to the Huaihe River and the Changjiang (Yangtze) River drainage basin. The different types of clouds show more clearly on this cloud classification image than single band image. The results of the cloud classifications are the basis of studying cloud amount, cloud top height and cloud top pressure. Cloud mask methods are widely used in SST, LST, and TPW retrieval schemes. Some case studies about cloud mask and cloud classification in satellite imageries, which are related with the study of Global Energy and Water Cycle Experiment (GEWEX) in the Huaihe River and the Changjiang River drainage basin are illustrated."
"7006802750;6602337949;6505862984;7202201947;7201527458;14031427400;","Automating the estimation of various meteorological parameters using satellite data and machine learning techniques",2002,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-0036028201&partnerID=40&md5=6ffced3f546df96f93f91360d5bf672f","Satellite data from various sensors and platforms are being used to develop automated algorithms to assist in U.S. Navy operational weather assessment and forecasting. Supervised machine learning techniques are used to discover patterns in the data and develop associated classification and parameter estimation algorithms. These methods are applied to cloud classification in GOES imagery, tropical cyclone intensity estimation using SSM/I data, and cloud ceiling height estimation at remote locations using appropriate geostationary and polar orbiting satellite data in conjunction with numerical weather prediction output and climatology. All developed algorithms rely on training data sets that consist of records of attributes (computed from the appropriate data source) and the associated ground truth."
"7006061457;","Cloud classification according to the phase structure. The cloud phase index",2001,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-0035760354&partnerID=40&md5=9ab450d32e088427dcda457668fb59e7","The modern classification of clouds according to their phase structure (liquid, ice, and mixed clouds) is discussed and it is shown that this classification is not informative. It is suggested that cloud phase structure be described by the cloud phase index (CPI)n of different orders n. The index (CPI)n measures a relative contribution of ice particles to microphysical properties of clouds: the cloud particle concentration (n=1, zero order index), the phase relaxation time (n=2, first-order index), the optical properties (n = 2, second-order index), and the total water content (n = 3, third-order index). The index changes within a cloud at negative temperature between 0 and 1 and may be used locally or for the whole cloud. The cloud phase indices can be useful in field and numerical experiments when studying cloud phase structure and its variations in space and time."
"54681288700;7005181100;7102079222;7004547261;","FFT-based algorithm for computation of Gabor transform with its application to cloud detection/classification",1996,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-0029720528&partnerID=40&md5=ac674d5b80fe0dfedf728ccb669956c3","In this paper an efficient algorithm for the computation of 2-D discrete Gabor transform is introduced. Under the assumption of non-overlapping windows, which is satisfied in many practical cases, the 2-D Gabor coefficients can be calculated through an FFT-based scheme. Combining with the Kohonen self-organized map, this method is used in a real-world problem dealing with cloud detection/classification. Simulation results are also provided which show the promise of the proposed method."
"7201361035;","The influence of clouds on earth radiation budget - a regional study: The North Sea",1994,"10.1016/0273-1177(94)90352-2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0028264577&doi=10.1016%2f0273-1177%2894%2990352-2&partnerID=40&md5=9cc527ede3b764f10a940c0ce80a47cd","The influence of clouds over the North Sea on climate is investigated by analyzing NOAA AVHRR data. The main interest is on high clouds due to their ambivalent behaviour in the radiation field. After a cloud classification, especially for high clouds, and the determination of cloud optical properties, the obtained information was applied to calculate the cloud-climate efficiency. This index is similar to the cloud forcing, but is valid for an individual classified satellite image pixel. The cloud forcing is the sum of the cloud-climate efficiencies over an area. Using NOAA-AVHRR data the annual cycle (October 1989, October 1990, April 1991 - July 1992) of cloud forcing at the top of atmosphere were calculated. Due to the strong dependence on solar insolation, high clouds with the same optical properties lead to an heating or a cooling of the earth/atmosphere system. For thin cirrus clouds the heating effect is well correlated with an increase of the surface temperature. A further approach to compare the increasing/decreasing cloud forcing with an analysis of relative topography 300/1000 hPa shows that an increase is positively correlated with an increase of the temperature in this layer. © 1993."
"35615376800;","Cloud analysis with satellite data",1990,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-0025256395&partnerID=40&md5=e3eb8e8ee3a1b91ba31e0d805cb176b2","After some introductory remarks on the earth's environment, remote sensing, satellite data in general and AVHRR data in particular, emphasis is put on cloud detection, cloud classification and cloud analysis using the algorithm package APOLLO (AVHRR Processing scheme Over Clouds, Land and Ocean). Cloud detection decides whether a pixel is either cloud free, fully cloudy, or neither of the two, i.e., partially cloudy. Cloud classification mainly establishes cloud coverages for different types of clouds. Cloud analysis derives cloud optical properties like optical depth and liquid water path from fully cloudy pixels. An overview of the present state of the art is given together with several examples of applications."
"6603858313;","Statistical approaches to cloud classification",1985,"10.1016/0273-1177(85)90314-X","https://www.scopus.com/inward/record.uri?eid=2-s2.0-46549097534&doi=10.1016%2f0273-1177%2885%2990314-X&partnerID=40&md5=b2d5a28226474bf92aa0313018e46183","For the determination of clouds from satellite data there exist in general more unknown parameters than independent observations. If the bispectral observations are used from the geostationary satellites in the solar (VIS-channel) and in the infrared (IR-channel) range to derive cloud parameters, information is needed whether a pixel radiance is from a cloud free or a cloudy scene. Statistical methods are applied to derive those informations. Various proposed statistical methods are discussed. The histogram analysis developed at the University of Cologne is described in detail: bispectral (two-dimensional) histograms are partitioned into clusters. Cloud cover results are shown. A comparison is given between the results of the histogram analysis, threshold methods (VIS- and IR threshold separately and both combined) and the spatial coherence method developed by Coakly and Bretherton. The cloud cover varies in this example by a factor of two depending on the definition of the threshold between cloud free and cloudy pixels. It is further shown that after a cluster analysis of a two-dimensional histogram the derived cloud cover is not as sensitive to the threshold as for a threshold method. The methods which are discussed here are those proposed for the International Satellite Cloud Climatology Project (ISCCP). The results are from the pilot study of the ISCCP. © 1985."
"6506449466;","A CENTURY OF CLOUD CLASSIFICATION",1969,"10.1002/j.1477-8696.1969.tb03152.x","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84977283270&doi=10.1002%2fj.1477-8696.1969.tb03152.x&partnerID=40&md5=963e795c3cd7ba888a569eeb2f406eab","In order to enable the Meteorologist to apply the key of Analysis to the experience of others, as well as to record his own with brevity and precision, it may perhaps be allowable to introduce a Methodical nomenclature … to the Modifications of Cloud. 1969 Royal Meteorological Society"
"6506051565;7004171611;55466977400;8669710800;7404209127;55752760600;36622868000;55914904100;55788613400;57200660264;57219665487;7403931916;8525147900;7102689523;","A test of the ability of current bulk optical models to represent the radiative properties of cirrus cloud across the mid- And far-infrared",2020,"10.5194/acp-20-12889-2020","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096019084&doi=10.5194%2facp-20-12889-2020&partnerID=40&md5=bcc42e57182eae262ba391298e910e85","Measurements of mid- to far-infrared nadir radiances obtained from the UK Facility for Airborne Atmospheric Measurements (FAAM) BAe 146 aircraft during the Cirrus Coupled Cloud-Radiation Experiment (CIRCCREX) are used to assess the performance of various ice cloud bulk optical property models. Through use of a minimization approach, we find that the simulations can reproduce the observed spectra in the mid-infrared to within measurement uncertainty, but they are unable to simultaneously match the observations over the far-infrared frequency range. When both mid- and far-infrared observations are used to minimize residuals, first-order estimates of the spectral flux differences between the best-performing simulations and observations indicate a compensation effect between the midand far-infrared such that the absolute broadband difference is <0.7Wm-2. However, simply matching the spectra using the mid-infrared (far-infrared) observations in isolation leads to substantially larger discrepancies, with absolute differences reaching 1.8 (3.1)Wm-2. These results show that simulations using these microphysical models may give a broadly correct integrated longwave radiative impact but that this masks spectral errors, with implicit consequences for the vertical distribution of atmospheric heating. They also imply that retrievals using these models applied to mid-infrared radiances in isolation will select cirrus optical properties that are inconsistent with far-infrared radiances. As such, the results highlight the potential benefit of more extensive farinfrared observations for the assessment and, where necessary, the improvement of current ice bulk optical models. © Author(s) 2020."
"56256294100;7401945370;8117864800;35454141800;56032970700;","Evaluations of the thermodynamic phases of clouds in a cloud-system-resolving model using calipso and a satellite simulator over the southern ocean",2020,"10.1175/JAS-D-19-0273.1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85094968073&doi=10.1175%2fJAS-D-19-0273.1&partnerID=40&md5=289144d3daec385beffe8209471a4e4f","A new evaluation method for the thermodynamic phases of clouds in cloud-system-resolving models is presented using CALIPSO observations and a satellite simulator. This method determines the thermodynamic phases using the depolarization ratio and a cloud extinction proxy. For the evaluation, we introduced empirical parameterization of the depolarization ratio of ice and water clouds using temperatures of a reanalysis dataset and total attenuated backscatters of CALIPSO.We evaluated the mixed-phase clouds simulated in a cloud-system-resolving model over the Southern Ocean using single-moment and doublemoment bulk cloud microphysics schemes, referred to as NSW6 and NDW6, respectively. The NDW6 simulations reproduce supercooled water clouds near the boundary layer that are consistent with the observations. Conversely, the NSW6 simulations failed to reproduce such supercooled water clouds. Consistencies between the cloud classes diagnosed by the evaluation method and the simulated hydrometeor categories were examined. NDW6 shows diagnosed water and ice classes that are consistent with the simulated categories, whereas the ice category simulated with NSW6 is diagnosed as liquid water by the present method due to the large extinction from the ice cloud layers. Additional analyses indicated that ice clouds with a small effective radius and large ice water content in NSW6 lead to erroneous values for the fraction of the diagnosed liquid water. It is shown that the uncertainty in the cloud classification method depends on the details of the cloud microphysics schemes. It is important to understand the causes of inconsistencies in order to properly understand the cloud classification applied to model evaluations as well as retrievals. © 2020 American Meteorological Society."
"6603381720;7402379980;57219337022;","Bin-emulating hail melting in three-moment bulk microphysics",2020,"10.1175/JAS-D-19-0268.1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092320699&doi=10.1175%2fJAS-D-19-0268.1&partnerID=40&md5=518d1a5e6543c0b8f8d570162ce3065b","A three-moment bulk microphysics scheme is modified to treat melting in a size-dependent manner that emulates results from a spectral bin scheme. The three-moment bulk framework allows the distribution shape to change and accommodate some direct effects of melting on both the hail and raindrop size distributions. Reflectivity changes and shed raindrop sizes are calculated over discrete size ranges of the hail particle spectrum. Smaller ice particles are treated as melting into drops of the same mass, whereas large particles shed drops as they melt. As small ice particles are lost, the size spectrum naturally becomes narrower and the mean size of small hail can increase. Large hail with a narrow spectrum, however, can decrease in size from melting. A substantial effect is seen on the rain median volume diameter when small drops are shed from large melting hail. The NSSL bulk scheme is compared with bin microphysics in steady-state hail shafts and in a supercell storm case. It is also shown that melting (or any substantial removal of mass) induces gravitational size sorting in bulk microphysics to increase hail size despite the design of the process rates to maintain the mean size of the melting ice. This unintended side effect can be a correct behavior for small hail, but not for large hail with a narrow distribution, when mean hail size should decrease by melting. © 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses)."
"57207833499;55604938200;","A framework for estimating cloudy sky surface downward longwave radiation from the derived active and passive cloud property parameters",2020,"10.1016/j.rse.2020.111972","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087860670&doi=10.1016%2fj.rse.2020.111972&partnerID=40&md5=6a62cd90a39c82aac933e4f92ee6a727","The cloud-base temperature (CBT) is one of the parameters that dominates the cloudy sky surface downward longwave radiation (SDLR). However, CBT is rarely available at regional and global scales, and its application in estimating cloud sky SDLR is limited. In this study, a framework to globally estimate cloud sky SDLR during both daytime and nighttime is proposed. This framework is composed of three parts. First, a global cloudy property database was constructed by combing the extracted cloud vertical structure (CVS) parameters from the active CloudSat data and cloud properties from passive MODIS data. Second, the empirical methods for estimating cloud thickness (CT) under ISCCP cloud classification system and MODIS cloud classification system were developed. Additionally, the coefficients of CERES CT estimate models were refitted using the constructed cloud property database. With the estimated CT and reanalysis data, calculating the CBT is straightforward. The accuracy of the estimated CT for ISCCP cloud type is compared with the existing studies that were conducted at local scales. Our CT estimate accuracy is comparable to that of the existing studies. According to the validation results at ARM NSA and SGP stations, the CT estimated by the developed CT model for MODIS cloud type is better than that estimated by the original CERES CT model. Finally, the cloudy sky SDLR values were derived by feeding the estimated CBT and other parameters to the single-layer cloud model (SLCM). When validated by the ground measured SDLR collected from the SURFRAD network, the bias and RMSE are 5.42 W∙m−2 and 30.3 W∙m−2, respectively. This accuracy is comparable to the evaluation results of the mainstream SDLR products (Gui et al. 2010), the new evaluation results of SLCMs (Yu et al. 2018), and the accuracy of a new cloudy sky SDLR estimate method (Wang et al. 2018). All the derived CBTs improve the SDLR estimate accuracy more than the SLCM that directly uses cloud-top temperature (CTT). We will collect more ground measurements and continue to validate the developed framework in the future. © 2020 Elsevier Inc."
"57126877800;57215848943;57217155505;57200555819;55469293700;","DANCE-NET: Density-aware convolution networks with context encoding for airborne LiDAR point cloud classification",2020,"10.1016/j.isprsjprs.2020.05.023","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086503837&doi=10.1016%2fj.isprsjprs.2020.05.023&partnerID=40&md5=d34e9ce6e8055c89f41ef75d493a4157","Airborne LiDAR point cloud classification has been a long-standing problem in photogrammetry and remote sensing. Early efforts either combine hand-crafted feature engineering with machine learning-based classification models or leverage the power of conventional convolutional neural networks (CNNs) on projected feature images. Recent proposed deep learning-based methods tend to develop new convolution operators which can be directly applied on raw point clouds for representative point feature learning. Although these methods have achieved satisfying performance for the classification of airborne LiDAR point clouds, they cannot adequately recognize fine-grained local structures due to the uneven density distribution of 3D point clouds. In this paper, to address this challenging issue, we introduce a density-aware convolution module which uses the point-wise density to reweight the learnable weights of convolution kernels. The proposed convolution module can approximate continuous convolution on unevenly distributed 3D point sets. Based on this convolution module, we further develop a multi-scale CNN model with downsampling and upsampling blocks to perform per-point semantic labeling. In addition, to regularize the global semantic context, we implement a context encoding module to predict a global context encoding and formulated a context encoding regularizer to enforce the predicted context encoding to be aligned with the ground truth one. The overall network can be trained in an end-to-end fashion and directly produces the desired classification results in one network forward pass. Experiments on the ISPRS 3D Labeling Dataset and 2019 Data Fusion Contest Dataset demonstrate the effectiveness and superiority of the proposed method for airborne LiDAR point cloud classification. © 2020 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS)"
"57191925668;55468705200;57199238005;57196026721;57211081884;56198793300;","ALS Point Cloud Classification with Small Training Data Set Based on Transfer Learning",2020,"10.1109/LGRS.2019.2947608","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082297372&doi=10.1109%2fLGRS.2019.2947608&partnerID=40&md5=0bfb204016c3962f4cdca52a0646b0ee","Point cloud classification of airborne light detection and ranging (LiDAR) data is essential to extract geoinformation. Although deep learning provides a new approach for classification, the time-consuming training process and data dependence prevent its widespread application to point clouds. To solve these problems and leverage the potential of high-performing neural networks, we propose an airborne LiDAR point cloud classification method based on transfer learning. A strategy to generate feature images considering the point cloud spatial distribution is first introduced for applying traditional convolutional neural networks to point clouds. Then, transfer learning is used to extract multiscale and multiview deep features. A simple neural network classifier is designed to reduce dimensionality, fuse and learn high-level features, and postprocessing considering contextual information further improves the classification accuracy. We verified the performance of the proposed method through experiments on two airborne LiDAR data sets with different characteristics and containing eight classes. The results demonstrate that the proposed method can achieve a satisfactory classification accuracy with relatively short training time and less training samples than if using conventional methods. © 2020 IEEE."
"55745285900;55716092000;55713076400;57212168155;57212018377;","Intraseasonal vertical cloud regimes based on CloudSat observations over the tropics",2020,"10.3390/rs12142273","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088660749&doi=10.3390%2frs12142273&partnerID=40&md5=b29f7b8194bfdb08a764b6f823519250","This study identifies the evolution of tropical vertical cloud regimes (CRs) and their associated heating structures on the intraseasonal time scales. Using the cloud classification retrievals of CloudSat during boreal winter between 2006 and 2017, the CR index is defined as the leading pair of the combined multivariate empirical orthogonal functions of the daily mean frequency of deep, high, and low clouds over the tropical Indian Ocean,Maritime Continents, and theWestern Pacific. The principal components of the CR index exhibit robust temporal variance in the 30 to 80 day intraseasonal band. Based on the propagation stages of the CRs, the coherent vertical structures of cloud composition and large-scale moisture and vertical motion exhibit a westward-tilted structure. The associated Q1-QR diabatic heating and cloud radiative forcing are consistent with the key characteristics of the Madden Julian Oscillation (MJO) documented in the previous studies. Lastly, anMJO case study showcases that the presented approach characteristically captures the propagation of moisture, cloud vertical structure, and precipitation activity across spatial and temporal scales. The current results suggest that the CR index can potentially serve as an evaluationmetric to cloud-associated processes in the simulated tropical intraseasonal variability in global climate models. © 2020 by the authors."
"57205423427;16644497500;57197644239;57217014185;57217146516;57217145357;57203897566;","Magnitude agreement, occurrence consistency, and elevation dependency of satellite-based precipitation products over the Tibetan Plateau",2020,"10.3390/rs12111750","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086459096&doi=10.3390%2frs12111750&partnerID=40&md5=0c11e721a4178d41748a97f2204d5176","Satellite remote sensing is a practical technique to estimate global precipitation with adequate spatiotemporal resolution in ungauged regions. However, the performance of satellite-based precipitation products is variable and uncertain for the Tibetan Plateau (TP) because of its complex terrain and climate conditions. In this study, we evaluated the abilities of nine widely used satellite-based precipitation products over the Eastern Tibetan Plateau (ETP) and quantified precipitation dynamics over the entire TP. The evaluation was carried out from three aspects, i.e., magnitude agreement, occurrence consistency, and elevation dependency, from grid-cell to regional scales. The results show that the nine satellite-based products exhibited different agreement with gauge-based reference data with median correlation coefficients ranging from 0.15 to 0.95. Three products (climate hazards group infrared precipitation with stations (CHIRPS), multi-source weighted-ensemble precipitation (MSWEP), and tropical rainfall measuring mission multi-satellite precipitation analysis (TMPA) generally presented the best performance with the reference data, even in complex terrain regions, given their root mean square errors (RMSE) of less than 25 mm/mon. The climate prediction center merged analysis of precipitation (CMAP) product has relatively coarse spatial resolution, but it also exhibited good performance with a bias of less than 20% in watershed scale. Two other products (precipitation estimation from remotely sensed information using artificial neural networks-cloud classification system (PER-CCS) and climate prediction center morphing technique-raw (CMORPH-RAW) overestimated precipitation with median RMSEs of 87 mm/mon and 45 mm/mon, respectively. All the precipitation products generally exhibited better agreement with the reference data for rainy season and lower-elevation regions. All of the products captured precipitation occurrence well, with hit event over 60%, and similar percentages of missed and false event. According to the evaluation, the four products (CHIRPS, MSWEP, TMPA, and CMAP) revealed that the annual precipitation over the TP fluctuated between 333 mm/yr and 488 mm/yr during the period 2003 to 2015. The study indicates the importance of integration of multiple data sources and post-processing (e.g., gauge data fusion and elevation correction) for satellite-based products and have implications for selection of suitable precipitation products for hydrological modeling and water resources assessment for the TP. © 2020 by the authors."
"57202231476;8856938500;55636317262;6701905330;6701802669;7103016965;23484340400;","Summertime cloud phase strongly influences surface melting on the Larsen C ice shelf, Antarctica",2020,"10.1002/qj.3753","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085962473&doi=10.1002%2fqj.3753&partnerID=40&md5=9b02362f7f9afb6129eefbd2643ab016","Surface melting on Antarctic Peninsula ice shelves can influence ice shelf mass balance, and consequently sea level rise. We show that summertime cloud phase on the Larsen C ice shelf on the Antarctic Peninsula strongly influences the amount of radiation received at the surface and can determine whether or not melting occurs. While previous work has separately evaluated cloud phase and the surface energy balance (SEB) during summertime over Larsen C, no previous studies have examined this relationship quantitatively. Furthermore, regional climate models frequently produce surface radiation biases related to cloud ice and liquid water content. This study uses a high-resolution regional configuration of the UK Met Office Unified Model (MetUM) to assess the influence of cloud ice and liquid properties on the SEB, and consequently melting, over the Larsen C ice shelf. Results from a case-study show that simulations producing a vertical cloud phase structure more comparable to aircraft observations exhibit smaller surface radiative biases. A configuration of the MetUM adapted to improve the simulation of cloud phase reproduces the observed surface melt most closely. During a five-week simulation of summertime conditions, model melt biases are reduced to &lt;2 W·m−2: a four-fold improvement on a previous study that used default MetUM settings. This demonstrates the importance of cloud phase in determining summertime melt rates on Larsen C. © 2020 The Authors. Quarterly Journal of the Royal Meteorological Society published by John Wiley & Sons Ltd on behalf of the Royal Meteorological Society."
"55694702700;26423907800;56960514500;56368863100;57208054851;","Harmonized landsat 8 and sentinel-2 time series data to detect irrigated areas: An application in Southern Italy",2020,"10.3390/RS12081275","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084588520&doi=10.3390%2fRS12081275&partnerID=40&md5=7c8cb13264f96429dca8af2c17bca9b6","Lack of accurate and up-to-date data associated with irrigated areas and related irrigation amounts is hampering the full implementation and compliance of theWater Framework Directive (WFD). In this paper, we describe the framework that we developed and implemented within the DIANA project to map the actual extent of irrigated areas in the Campania region (Southern Italy) during the 2018 irrigation season. For this purpose, we considered 202 images from the Harmonized Landsat Sentinel-2 (HLS) products (57 images from Landsat 8 and 145 images from Sentinel-2). Such data were preprocessed in order to extract a multitemporal Normalized Difference Vegetation Index (NDVI) map, which was then smoothed through a gap-filling algorithm. We further integrated data coming from high-resolution (4 km) global satellite precipitation Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN)-Cloud Classification System (CCS) products. We collected an extensive ground truth in the field represented by 2992 data points coming from three main thematic classes: bare soil and rainfed (class 0), herbaceous (class 1), and tree crop (class 2). This information was exploited to generate irrigated area maps by adopting a machine learning classification approach. We compared six different types of classifiers through a cross-validation approach and found that, in general, random forests, support vector machines, and boosted decision trees exhibited the best performances in terms of classification accuracy and robustness to different tested scenarios. We found an overall accuracy close to 90% in discriminating among the three thematic classes, which highlighted promising capabilities in the detection of irrigated areas from HLS products. © 2020 by the authors."
"7103213900;7005742394;","Analytical investigation of the role of lateral mixing in the evolution of nonprecipitating cumulus. Part I: Developing clouds",2020,"10.1175/JAS-D-19-0036.1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082885794&doi=10.1175%2fJAS-D-19-0036.1&partnerID=40&md5=c56039d0a0b479d302fcd21305383901","Evolution of nonprecipitating cumulus clouds (Cu) at the developing stage under the influence of lateral entrainment and mixing is studied analytically using a minimalistic analytical model. We present a model of an ascending cloud volume (a model of developing Cu) whose structure is determined by the processes of droplet diffusion growth/evaporation and entrainment mixing in the horizontal direction. Spatial and time changes of liquid water content, the adiabatic fraction, droplet concentration, and the mean volume droplet radius are calculated. It is shown that the existence of a nondiluted core in a growing cumulus cloud significantly depends on the cloud width and vertical velocity. While at the updraft velocity of 2 m s21 the core of a 400-m-wide cloud becomes diluted at distances of a few hundred meters above cloud base, the core of a cloud of 1000-m width remains nondiluted at distances up to 1500 m above cloud base. The explanation of this result is simple: the increase in cloud width and the decrease in the updraft velocity increase the time during which the cloud is diluted due to mixing. Since lateral mixing synchronously decreases both the cloud water content and droplet concentration, the variation of the mean volume droplet radius is low inside the cloud. The approximate quantitative condition for cloud formation in updraft is derived. It is shown that a cloud can arise when its vertical velocity exceeds a critical value. To produce clouds, narrow turbulent plumes should ascend at higher velocity as compared to wider plumes. High humidity of the environment air is favorable for formation of clouds from plumes. The comparison of the obtained results with previously published observational data indicates a reasonable agreement. The results can be useful for parameterization purposes. © 2020 American Meteorological Society."
"57203909332;57208314568;35489753900;35313639700;57215409801;57212457314;55574865800;55809308400;35168724300;56978385600;","Construction of nighttime cloud layer height and classification of cloud types",2020,"10.3390/rs12040668","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85080933238&doi=10.3390%2frs12040668&partnerID=40&md5=57f54bfca173196cf994416e1e1025fe","A cloud structure construction algorithm adapted for the nighttime condition is proposed and evaluated. The algorithm expands the vertical information inferred from spaceborne radar and lidar via matching of infrared (IR) radiances and other properties at off-nadir locations with their counterparts that are collocated with active footprints. This nighttime spectral radiance matching (NSRM) method is tested using measurements from CloudSat/Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and Moderate Resolution Imaging Spectroradiometer (MODIS). Cloud layer heights are estimated up to 400 km on both sides of the ground track and reconstructed with the dead zone setting for an approximate evaluation of the reliability. By mimicking off-nadir pixels with a dead zone around pixels along the ground track, reconstruction of nadir profiles shows that, at 200 km from the ground track, the cloud top height (CTH) and the cloud base height (CBH) reconstructed by the NSRM method are within 1.49 km and 1.81 km of the original measurements, respectively. The constructed cloud structure is utilized for cloud classification in the nighttime. The same method is applied to the daytime measurements for comparison with collocated MODIS classification based on the International Satellite Cloud Climatology Project (ISCCP) standard. The comparison of eight cloud types over the expanded distance shows good agreement in general. © 2020 by the author."
"57070621900;57199645648;57190336813;56021684000;54791703500;","Structure-aware convolution for 3D point cloud classification and segmentation",2020,"10.3390/rs12040634","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85080904741&doi=10.3390%2frs12040634&partnerID=40&md5=e46e85b99e826fa5d837844c24e99318","Semantic feature learning on 3D point clouds is quite challenging because of their irregular and unordered data structure. In this paper, we propose a novel structure-aware convolution (SAC) to generalize deep learning on regular grids to irregular 3D point clouds. Similar to the template-matching process of convolution on 2D images, the key of our SAC is to match the point clouds' neighborhoods with a series of 3D kernels, where each kernel can be regarded as a ""geometric template"" formed by a set of learnable 3D points. Thus, the interested geometric structures of the input point clouds can be activated by the corresponding kernels. To verify the effectiveness of the proposed SAC, we embedded it into three recently developed point cloud deep learning networks (PointNet, PointNet++, and KCNet) as a lightweight module, and evaluated its performance on both classification and segmentation tasks. Experimental results show that, benefiting from the geometric structure learning capability of our SAC, all these back-end networks achieved better classification and segmentation performance (e.g., +2.77% mean accuracy for classification and +4.99% mean intersection over union (IoU) for segmentation) with few additional parameters. Furthermore, results also demonstrate that the proposed SAC is helpful in improving the robustness of networks with the constraints of geometric structures. © 2020 by the author."
"57204873921;57196504979;35183713900;57210985203;","Analysis and Integration of Surface and Subsurface Information of Different Bridges",2020,"10.1007/s12524-019-01087-2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076833109&doi=10.1007%2fs12524-019-01087-2&partnerID=40&md5=e3a520538e91be23e38b6f3a24ad87fe","Road transportation is one of the major sources of transportation. Bridges are one of the vital engineering structures which have a major impact on the road transportation system. Bridges provide connectivity between two sides of the river banks or untouched paths to ease the travel. The strength of the bridge can get deteriorated due to heavy traffic and aggressive weather conditions. Evaluation of the condition of the bridges traditionally can be more expensive and time-consuming. The other way is by using remote sensing techniques that are nondestructive and advantageous. Terrestrial laser scanning (TLS) and close-range photogrammetry (CRP) are more suitable noninvasive techniques to generate a detailed 3D point cloud model of real objects. Point clouds obtained from TLS and CRP are merged together to produce point cloud dataset (PCD). The PCD can be georeferenced with the help of differential global positioning system points near the structure and total station points on the surface of the structure. The surface analysis for the features like corrosion, vegetation, biological crust, water presence, etc. can be extracted using the PCD and images obtained from digital single-lens reflex camera. Ground-penetrating radar (GPR) can be utilized for generating subsurface 2D and 3D profile scans. Subsurface analysis features like the presence of pier/abutment, water, voids, rebars, crack, asphalt layer, deck layer, etc. can be extracted with the GPR scan profiles. The surface and subsurface information can be visualized together to understand the surface or subsurface features corresponding to each other’s location. Accuracy assessments of the classified images and the classified points of the PCD are done in this research, and the accuracies obtained were 79.69% and 92.494%, respectively. The ground-truth validations were done with the help of laser distometer and measuring tape to precisely compare the values. © 2019, Indian Society of Remote Sensing."
"57214106967;","Discovering the Importance of Mesoscale Cloud Organization Through Unsupervised Classification",2020,"10.1029/2019GL085190","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078262389&doi=10.1029%2f2019GL085190&partnerID=40&md5=e0e4c00d19df74d1dd35f1437a2b2255","The representation of shallow trade wind convective clouds in climate models dominates the uncertainty in climate sensitivity estimates. In particular the radiative impact of cloud spatial organization is poorly understood. This work presents the first unsupervised neural network model which autonomously discovers cloud organization regimes in satellite images. Trained on 10,000 GOES-16 satellite images (tropical Atlantic and boreal winter) the regimes found are shown to exist in a hierarchy of organizational scales, with sub-clusters having distinct radiative properties. The model requires no time-consuming and subjective hand-labeled data based on predefined structures allowing for objective study of very large data sets. The model enables the study of environmental conditions in different organizational regimes and in transitions between regimes and objective comparisons of model behavior with observations through cloud structures emerging in both. These abilities enable the discovery of previously unknown physical relationships in cloud processes, enabling better representation of clouds in weather and climate simulations. ©2019. American Geophysical Union. All Rights Reserved."
"57218643452;47861406800;24173130300;","Reconciling chord length distributions and area distributions for fields of fractal cumulus clouds",2020,"10.3390/ATMOS11080824","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089873230&doi=10.3390%2fATMOS11080824&partnerID=40&md5=de9d077c4c67718f54b0b1c657bab6c7","While the total cover of broken cloud fields can in principle be obtained from one-dimensional measurements, the cloud size distribution normally differs between two-dimensional (area) and one-dimensional retrieval (chord length) methods. In this study, we use output from high-resolution Large Eddy Simulations to generate a transfer function between the two. We retrieve chord lengths and areas for many clouds, and plot the one as a function of the other, and vice versa. We find that the cloud area distribution conditional on the chord length behaves like a gamma distribution with well-behaved parameters, with a mean μ = 1.1L and a shape parameter β = L-0.645. Using this information, we are able to generate a transfer function that can adjust the chord length distribution so that it comes much closer to the cloud area distribution. Our transfer function improves the error in predicting the mean cloud size, and is performs without strong biases for smaller sample sizes. However, we find that the method is still has difficulties in accurately predicting the frequency of occurrence of the largest cloud sizes. © 2020 by the authors."
"7401526171;57218396843;35975568000;","Improving PERSIANN-CCS Using Passive Microwave Rainfall Estimation",2020,"10.1007/978-3-030-24568-9_21","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089068676&doi=10.1007%2f978-3-030-24568-9_21&partnerID=40&md5=cd6573882f174612b92fc8659e48e681","Re-calibrated PERSIANN-CCS is one of the algorithms used in “Integrated Multi-satellitE Retrievals for GPM” (IMERG) to provide high-resolution precipitation estimations from the NASA Global Precipitation Measurement (GPM) program and retrospective data generation for the period covered by the Tropical Rainfall Measurement Mission (TRMM). This study presents the development of a re-calibrated PERSIANN-CCS algorithm for the next-generation GPM multi-sensor precipitation retrieval algorithm (IMERG). The activities include implementing the probability matching method to update PERSIANN-CCS using passive microwave (PMW) rainfall estimation from low earth orbit (LEO) satellites and validation of precipitation estimation using radar rainfall measurement. Further improvement by the addition of warm rain estimation to the PERSIANN-CCS algorithm using warmer temperature thresholds for cloud image segmentation is also presented. Additionally, developments using multispectral image analysis and machine learning approaches are discussed and proposed for future studies. © 2020, Springer Nature Switzerland AG."
"57212374592;45861319600;55331455800;7401526171;7005052907;56012593900;35509463200;","Deep Neural Network Cloud-Type Classification (DeepCTC) model and its application in evaluating PERSIANN-CCS",2020,"10.3390/rs12020316","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081089052&doi=10.3390%2frs12020316&partnerID=40&md5=e8c4c42bcc784aaf2a9ca482976fefe4","Satellite remote sensing plays a pivotal role in characterizing hydrometeorological components including cloud types and their associated precipitation. The Cloud Profiling Radar (CPR) on the Polar Orbiting CloudSat satellite has provided a unique dataset to characterize cloud types. However, data from this nadir-looking radar offers limited capability for estimating precipitation because of the narrow satellite swath coverage and low temporal frequency. We use these high-quality observations to build a Deep Neural Network Cloud-Type Classification (DeepCTC) model to estimate cloud types from multispectral data from the Advanced Baseline Imager (ABI) onboard the GOES-16 platform. The DeepCTC model is trained and tested using coincident data from both CloudSat and ABI over the CONUS region. Evaluations of DeepCTC indicate that the model performs well for a variety of cloud types including Altostratus, Altocumulus, Cumulus, Nimbostratus, Deep Convective and High clouds. However, capturing low-level clouds remains a challenge for the model. Results from simulated GOES-16 ABI imageries of the Hurricane Harvey event show a large-scale perspective of the rapid and consistent cloud-type monitoring is possible using the DeepCTC model. Additionally, assessments using half-hourly Multi-Radar/Multi-Sensor (MRMS) precipitation rate data (for Hurricane Harvey as a case study) show the ability of DeepCTC in identifying rainy clouds, including Deep Convective and Nimbostratus and their precipitation potential. We also use DeepCTC to evaluate the performance of the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS) product over different cloud types with respect to MRMS referenced at a half-hourly time scale for July 2018. Our analysis suggests that DeepCTC provides supplementary insights into the variability of cloud types to diagnose the weakness and strength of near real-time GEO-based precipitation retrievals. With additional training and testing, we believe DeepCTC has the potential to augment the widely used PERSIANN-CCS algorithm for estimating precipitation. © 2020 by the authors."
"57209464709;39561484400;6506741878;56966011200;7006129981;57196817178;","Retrieval and validation of cloud top temperature from the geostationary satellite INSAT-3D",2019,"10.3390/rs11232811","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076535250&doi=10.3390%2frs11232811&partnerID=40&md5=7b3a1ab31eae73c45a5555308535fa74","Investigation of cloud top temperature (CTT) and its diurnal variation is highly reliant on high spatial and temporal resolution satellite data, which is lacking over the Indian region. An algorithm has been developed for detection of clouds and retrieval of CTT from the geostationary satellite INSAT-3D. These retrievals are validated (inter-compared) with collocated in-situ (satellite) measurements with specific intent to generate climate-quality data. The cloud detection algorithm employs nine different tests, in accordance with solar illumination, satellite angle and surface type conditions to generate pixel-resolution cloud mask. Validation of cloud mask with cloud-aerosol lidar with orthogonal polarization (CALIOP) shows that probability of detection (POD) of cloudy (clear) sky is 81% (85%), with 83% hit rate. The algorithm is also implemented on similar channels of moderate resolution imaging spectroradiometer (MODIS), which provides 88% (83%) POD of cloudy (clear) sky, with 86% hit rate. CTT retrieval is done at the pixel level, for all cloud pixels, by employing appropriate methods for various types of clouds. Comparison of CTT with radiosonde and cloud-aerosol lidar and infrared pathfinder satellite observations (CALIPSO) shows mean absolute error less than 3%. The study also examines sensitivity of retrieved CTT to the cloud classification scheme and retrieval criteria. Validation results and their close agreements with those of similar satellites demonstrate the reliability of the retrieved product for climate studies. © 2019 by the authors."
"57213186139;6701534437;","Edge-Convolution Point Net for Semantic Segmentation of Large-Scale Point Clouds",2019,"10.1109/IGARSS.2019.8899303","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077673456&doi=10.1109%2fIGARSS.2019.8899303&partnerID=40&md5=0e4b7eea4624c75d96176461b3c4ee2d","In this paper, we propose a deep learning-based framework which can manage large-scale point clouds of outdoor scenes with high spatial resolution. For large and high-resolution outdoor scenes, point-wise classification approaches are often an intractable problem. Analogous to Object-Based Image Analysis (OBIA), our approach segments the scene by grouping similar points together to generate meaningful objects. Later, our net classifies segments instead of individual points using an architecture inspired by PointNet, which applies Edge convolutions. This approach is trained using both visual and geometrical information. Experiments show the potential of this task even for small training sets. Furthermore, we can show competitive performance on a Large-scale Point Cloud Classification Benchmark. © 2019 IEEE."
"57208332465;42961641500;8909993500;24398842400;","Classification of Arctic multilayer clouds using radiosonde and radar data in Svalbard",2019,"10.5194/acp-19-5111-2019","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85064512433&doi=10.5194%2facp-19-5111-2019&partnerID=40&md5=f6b24527fcb86d0030f7d9a1a2ff7a83","Multilayer clouds (MLCs) occur more often in the Arctic than globally. In this study we present the results of a detection algorithm applied to radiosonde and radar data from an 1-year time period in Ny-Ålesund, Svalbard. Multilayer cloud occurrence is found on 29% of the investigated days. These multilayer cloud cases are further analysed regarding the possibility of ice crystal seeding, meaning that an ice crystal can survive sublimation in a subsaturated layer between two cloud layers when falling through this layer. For this we analyse profiles of relative humidity with respect to ice to identify super- and subsaturated air layers. Then the sublimation of an ice crystal of an assumed initial size of r = 400 μm on its way through the subsaturated layer is calculated. If the ice crystal still exists when reaching a lower supersaturated layer, ice crystal seeding can potentially take place. Seeding cases are found often, in 23% of the investigated days (100% includes all days, as well as non-cloudy days). The identification of seeding cases is limited by the radar signal inside the subsaturated layer. Clearly separated multilayer clouds, defined by a clear interstice in the radar image, do not interact through seeding (9% of the investigated days). There are various deviations between the relative humidity profiles and the radar images, e.g. due to the lack of ice-nucleating particles (INPs) and cloud condensation nuclei (CCN). Additionally, horizontal wind drift of the radiosonde and time restriction when comparing radiosonde and radar data cause further deviations. In order to account for some of these deviations, an evaluation by manual visual inspection is done for the non-seeding cases. © Author(s) 2019."
"57197703326;24400226100;","Climatology of fog occurrence over a wide flat area in Serbia based on visibility observations",2019,"10.1002/joc.5883","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85055709164&doi=10.1002%2fjoc.5883&partnerID=40&md5=09251aee68b99fa9130fba207a026bf9","Long-term analysis of occurrence of fog events at 14 stations across part of fairly flat terrain of Serbia, which includes the area of the Pannonian Plain, is conducted. For the majority of stations (Palić, Novi Sad, Kikinda, Vršac, Belgrade-Surčin, Smederevska Palanka, Valjevo, Loznica, Negotin) data were available for the 33-year period, 1973–2005. For the rest of stations, data were available for shorter periods: for Sombor and Veliko Gradište 1982–2005, for Sremska Mitrovica and Belgrade-Vračar 1983–2005, and for Zrenjanin 1989–2005. Fog event is defined only based on visibility observations. Almost 75% of analysed data sets showed a negative annual trend of low-visibility events throughout the analysed time periods. This negative trend is increasing as it goes towards the north of Serbia and most of the stations with decreasing trend are rural. Fog is most frequent in the period between October and February and it is likely mostly due to radiative cooling and especially in October when an anticyclonic synoptic situation is prevailing. Stations with higher elevations have more lasting fog events and fog events with more variable mean relative humidity compared to their counterparts, which ranges from 92.1–97.4%. For most of stations December is the month with the highest frequency of occurrence of fog in the presence of a low cloud. Frequency of fog onset during low wind speed (below 2 m/s) is analysed along with its correlation with a frequency of fog onset when the cloud base height is equal to or above 2,000 m. In ~71% of the stations correlations are higher than 0.6, and stations with a lower elevations show a generally better correlation than those with higher elevations. © 2018 Royal Meteorological Society"
"57192264838;24398842400;6701762451;6602890253;24477694300;18438062100;35430463900;7202057166;57189498750;55683037100;6701834052;6601927317;","Comparison of modeled and measured ice nucleating particle composition in a cirrus cloud",2019,"10.1175/JAS-D-18-0034.1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065012850&doi=10.1175%2fJAS-D-18-0034.1&partnerID=40&md5=778af4e4c110a2bf3246a4bc44d931d8","The contribution of heterogeneous ice nucleation to the formation of cirrus cloud ice crystals is still not well quantified. This results in large uncertainties when predicting cirrus radiative effects and their role in Earth's climate system. The goal of this case study is to simulate the composition, and thus activation conditions, of ice nucleating particles (INPs) to evaluate their contribution to heterogeneous cirrus ice formation in relation to homogeneous ice nucleation. For this, the regional model COSMO-Aerosols and Reactive Trace Gases (COSMO-ART) was used to simulate a synoptic cirrus cloud over Texas on 13 April 2011. The simulated INP composition was then compared to measured ice residual particle (IRP) composition from the actual event obtained during the NASA Midlatitude Airborne Cirrus Properties Experiment (MACPEX) aircraft campaign. These IRP measurements indicated that the dominance of heterogeneous ice nucleation was mainly driven by mineral dust with contributions from a variety of other particle types. Applying realistic activation thresholds and concentrations of airborne transported mineral dust and biomass-burning particles, the model implementing the heterogeneous ice nucleation parameterization scheme of Ullrich et al. is able to reproduce the overall dominating ice formation mechanism in contrast to the model simulation with the scheme of Phillips et al. However, the model showed flaws in reproducing the IRP composition. © 2019 American Meteorological Society."
"36769548900;57201802650;57201797139;57203222539;57203228641;36990982800;15034793900;","Line Structure-Based Indoor and Outdoor Integration Using Backpacked and TLS Point Cloud Data",2018,"10.1109/LGRS.2018.2856514","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050970641&doi=10.1109%2fLGRS.2018.2856514&partnerID=40&md5=8eed57088b406cf4b94994ee2acd2292","This letter presents a line structure-based method for integration of centimeter-level indoor backpacked scanning point clouds and millimeter-level outdoor terrestrial laser scanning point clouds. Using 3-D lines for registration, instead of matching points directly, can improve the robustness of the method and adapt to multisource point cloud data of different qualities. Considering the limited overlapping between indoor and outdoor scenes, line structures are extracted from overlapped wall areas that may be included in interior and exterior data. Here, a patch-based method labels a point cloud into wall, ceiling, floor categories, as well as assigning the candidate overlapping walls. Then, lines structures are extracted from the wall plane point cloud. Potential door and window line structures are detected and refined for point cloud registration. Last, an iterative closest point-based method is used to fine tune the registration results. Our results show that the proposed method effectively integrates a promising map of indoor and outdoor scenes. © 2018 IEEE."
"55083178100;57202647645;57192690229;","Towards extraction of lianas from terrestrial lidar scans of tropical forests",2018,"10.1109/IGARSS.2018.8517634","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85063144970&doi=10.1109%2fIGARSS.2018.8517634&partnerID=40&md5=0aef4f09fd61fe99b51a9e277003cc99","Increased liana abundance results in reduced tree growth and increased tree mortality in tropical forest. The impact of lianas on forest-wide carbon storage has been a special interest for many researchers. The vertical and horizontal spatial distribution of lianas in tropical forest will determine the interaction with trees and the forest carbon cycle. In this study, we will introduce an algorithm to extract lianas from terrestrial laser scanning (TLS) data of a tropical forest. We developed a classification method for separating liana points from other points in a point cloud under canopy. We used a Random Forests machine learning algorithm for the classification of liana points from the other points. The leaf-wood and liana-tree classification accuracies are 90.69% and 94.42%, respectively. The results show the potential of TLS data for analysis the spatial distribution of lianas in forest stands and we explore the potential of extracting lianas from TLS point clouds. © 2018 IEEE"
"57188558199;16320137600;","Cloud classification in wide-swath passive sensor images aided by narrow-swath active sensor data",2018,"10.3390/rs10060812","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85048970684&doi=10.3390%2frs10060812&partnerID=40&md5=f04037a9546f824c02224d8774499786","It is a challenge to distinguish between different cloud types because of the complexity and diversity of cloud coverage, which is a significant clutter source that impacts on target detection and identification from the images of space-based infrared sensors. In this paper, a novel strategy for cloud classification in wide-swath passive sensor images is developed, which is aided by narrow-swath active sensor data. The strategy consists of three steps, that is, the orbit registration, most matching donor pixel selection, and cloud type assignment for each recipient pixel. A new criterion for orbit registration is proposed so as to improve the matching accuracy. The most matching donor pixel is selected via the Euclidean distance and the square sum of the radiance relative differences between the recipient and the potential donor pixels. Each recipient pixel is then assigned a cloud type that corresponds to the most matching donor. The cloud classification of the Moderate Resolution Imaging Spectroradiometer (MODIS) images is performed with the aid of the data from Cloud Profiling Radar (CPR). The results are compared with the CloudSat product 2B-CLDCLASS, as well as those that are obtained using the method of the International Satellite Cloud Climatology Project (ISCCP), which demonstrates the superior classification performance of the proposed strategy. © 2018 by the authors."
"57202317272;8529014500;57161909900;57207690147;57207687913;","Ground-based cloud-type recognition using manifold kernel sparse coding and dictionary learning",2018,"10.1155/2018/9684206","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062683492&doi=10.1155%2f2018%2f9684206&partnerID=40&md5=c32fe2b3ca0af53b3318691cd45854b6","Recognizing cloud type of ground-based images automatically has a great influence on the weather service but poses a significant challenge. Based on the symmetric positive definite (SPD) matrix manifold, a novel method named “manifold kernel sparse coding and dictionary learning” (MKSCDL) is proposed for cloud classification. Different from classical features extracted in the Euclidean space, the SPD matrix fuses multiple features and represents non-Euclidean geometric characteristics. MKSCDL is composed of three steps: feature extraction, dictionary learning, and classification. With the learned dictionary, the SPD matrix of the cloud image can be described with the sparse code. The experiments are conducted on two different ground-based cloud image datasets. Benefitting from the sparse representation on the Riemannian matrix manifold, compared to the recent baselines, experimental results demonstrate that MKSCDL possesses a more competitive performance on both grayscale and colour image datasets. Copyright © 2018 Qixiang Luo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited."
"55511392300;7004086472;25522161800;6603627233;","Relationship between cloud-to-ground lightning polarity and the space-time distribution of solid hydrometeors in isolated summer thunderclouds observed by X-band polarimetric radar",2017,"10.1002/2016JD026283","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85029303866&doi=10.1002%2f2016JD026283&partnerID=40&md5=1c569618b34e907d021edbaf36da9567","To understand the charge distribution in thunderclouds associated with solid hydrometeors, the relationship between cloud-to-ground (CG) lightning polarity and the space-time distribution of solid hydrometeors for isolated summer thunderclouds in Japan is examined using X-band polarimetric radar. Hydrometeor classification was conducted to examine the space-time distribution of dry snow, ice crystal, dry graupel (DG), and wet graupel in thunderclouds. Two thunderstorm cases were selected for analysis: 26 July 2010, which generated few positive CG flashes, and 25 August 2010, which generated positive CG flashes in the reflectivity cores. In both cases, negative CG flashes were observed in all reflectivity cores when a large volume of DG was identified above the height of −10°C level. This is consistent with previous studies showing that graupel particles have a negative charge below temperatures of −10°C. Reflectivity cores with positive CG flashes had a large volume of DG up to high altitudes (around or above the −45°C level). Further, reflectivity cores that sustained large DG volumes at high altitudes had a relatively large number of positive CG flashes. The top height of the DG volume reached lower altitudes for reflectivity cores without positive CG flashes compared with those with positive CG flashes. These results suggest that the persistence of graupel particles in reflectivity cores at high altitudes, implying the existence of strong updraft, is a necessary condition for positive CG flashes in summer thunderclouds. This effect would likely be caused by the positively charged graupel particles under high rime accretion rates. ©2017. American Geophysical Union. All Rights Reserved."
"57194798862;36022024600;","Point verification and improved communication of the low-to-medium cloud cover forecasts",2017,"10.1002/met.1645","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85022324782&doi=10.1002%2fmet.1645&partnerID=40&md5=35f9ddc1d099db0e72e75a714c01b3d9","The amount of sunshine weighs heavily in our perception of the weather. It is largely determined by cloud cover, especially that at the low-to-medium level. Therefore, when reviewing the Hong Kong Observatory's weather symbol forecasting product, verification on the low-to-medium cloud field is carried out against the synoptic observations at the Hong Kong International Airport. Several metrics are used to examine the different aspects of the forecasts, and consideration is given to the non-Gaussian nature of the reported cloud amount for a fairer assessment. Based on the data from January to mid-August 2015, the median of the forecasts from the European Centre for Medium-Range Weather Forecasts Ensemble Prediction System is found to outperform the other model forecasts, particularly when the performance is examined by forecast day. This paper presents these results and also discusses the potential of using the field in deriving site-specific weather symbol forecasts. © 2017 Royal Meteorological Society"
"54279269400;34968226100;54387454300;7005720744;55932461800;","Field trial of an automated ground-based infrared cloud classification system",2015,"10.1002/met.1523","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84947434626&doi=10.1002%2fmet.1523&partnerID=40&md5=513c42139355354cfbd20093c4dfcc58","Automated classification of cloud types using a ground-based infrared (IR) imager can provide invaluable high-resolution and localized information for air traffic controllers. Observations can be made consistently, continuously in real time and accurately during both day and night operation. Details of a field trial of an automated, ground-based IR cloud classification system are presented. The system was designed at Campbell Scientific Ltd. in collaboration with Loughborough University, UK. The main objective of the trial was to assess the performance of an automated IR camera system with a lightning detector in classifying several types of clouds, specifically cumulonimbus and towering cumulus, during continuous day and night operation. Results from the classification system were compared with those obtained from Meteorological Aerodrome Reports (METAR) and with data generated by the UK Meteorological Office from their radar- and sferics-automated cloud reports system. In comparisons with METAR data, a probability of detection of up to 82% was achieved, together with a minimum probability of false detection of 18%. © 2015 Royal Meteorological Society."
"35208591600;56428655200;","Building detection with LiDAR point clouds based on regional multi-return density analyzing",2014,"10.1109/EORSA.2014.6927861","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84912061363&doi=10.1109%2fEORSA.2014.6927861&partnerID=40&md5=1422a67249243ad928267a4c1a6e0597","A new strategy for the classification of raw LiDAR points and building detection in urban areas is proposed based on the regional multi-return density analysis and which is constructed on the comprehensive utilization of echo features of different object types and terrain information. The main procedures of the classifying of the off-terrain points is beginning at the construction of Triangulated Irregular Network (TIN), then the regions of each object are captured by the contours clustering based on the topological relations of each contours traced from the TIN. Finally, the type of the object is recognized by the statistical analysis of the regional multi-return density for the significant difference on the building region and vegetation regions. This method makes good use of the difference on echo features of different objects such as buildings and trees but obeying the appearance of the multi-returns happened on the edges of the building. At the same time, the adaptive region determination of the objects is accomplished following the contours clustering. So the proposed method can dramatically increase the classification accuracy and overcome the weakness of the traditional methods which is more useful for the continued researches and applications such as building reconstruction and the parameters estimation of the tress. The experiment proves that the new algorithm can get an effective classification. © 2014 IEEE."
"56022625300;23493588000;46760954500;56203770300;","Linear and non-linear enhancement for sun glint reduction in advanced very high resolution radiometer (AVHRR) image",2014,"10.1088/1755-1315/18/1/012041","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84902341043&doi=10.1088%2f1755-1315%2f18%2f1%2f012041&partnerID=40&md5=6cf57d2f003be6f512e81566d1d67a3e","Cloud detection over water surfaces is difficult due to the sun glint effect. The mixed pixels between both features may introduce inaccurate cloud classification. This problem generally occurs because of less contrast between the glint and the cloud. Both features have almost the same reflectance in the visible wavelength. The piecewise contrast stretch technique shows preservation capability on the reflectance of the cloud. The result of a band ratio was smoothed by applying the Sobel edge detection to provide better cloud feature detection. The study achieved an accuracy of about 77.5% in cloud pixels detection. © Published under licence by IOP Publishing Ltd."
"56728284900;7409077047;55448001800;14009374600;55530911200;55712158600;","Relation between Cumulonimbus(Cb) preicitiation and cloud dynamical features over Huaihe River Basin of China based on FY-2C image",2013,"10.1109/IGARSS.2013.6721216","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84894226858&doi=10.1109%2fIGARSS.2013.6721216&partnerID=40&md5=101ebb53efd6a94236c045be2a5b4b02","The crowning objective of this research are to analyze precipitation character of Cb for different dynamical characters in Huai river basin(HRB) with China's first operational geostationary meteorological satellite FengYun-2C (FY-2C) data. Firstly, 5 cloud patch dynamic parameters with respect to life stage and moving parameters are derived based on the Cb tracking method the author has proposed by combing artificial neural network (ANN) cloud classification[1], and cross-correlation-based approach to track Cb patch motion. Secondly, Cb precipitation over different life cycles and motion characters are analyzed. The result shows that: 1) Rain probability has a similar variation to rain rate, and rain rate is generally not more than 6 mm/hour, and probability is randomly higher than 50%. 2) Both rain rate and probability of single Cb is lower than that of complicated Cb which involves cell-merger and cell-split of some minor Cb patches. 3) Motion features such as horizontal moving speed of cloud patch (HMSP), horizontal moving direction of cloud patch (HMDP), and vertical moving character of cloud patch (VMCP) have no obvious impact on rain. © 2013 IEEE."
"50263335200;14068903300;14068438700;","Effects of orography on the tail-end effects of typhoon Ketsana",2013,"10.2174/1874282301307010014","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84879143764&doi=10.2174%2f1874282301307010014&partnerID=40&md5=e56e0f9712c96c5a330fb219d9cfa43e","The study of tail-end effects of typhoon on orography is new to Malaysia. The current study used FY-2D satellite data to investigate the variation of selected parameters of the Typhoon Ketsana system. In situ data, obtained via the radiosonde technique, were used to verify the atmospheric conditions, whereas the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model was applied to determine the structure of the mountains in East Malaysia (EM). This study aimed to identify typhoon-terrain effects, in terms of wind, cloud, and rain of the tail-end effects of typhoon in a regional environment. The tail-end effects of Typhoon Ketsana were altered by the orography in EM such that a slow movement with higher rate of rainfall was distributed along the mountainous western region, and cloud classification distribution patterns were different before, during, and after the tail-end effects of the typhoon. The wind intensity increased with altitude and affected the larger atmosphere region over EM. Additionally, the location of the Sabah region puts it at a higher risk to the impact of the tail-end effect of typhoons compared with the Sarawak region due to its distance from the typhoon. This study concluded that the impacts of the tail-end effects of a typhoon can also be varied and enhanced by the orography. © Tan et al.; Licensee Bentham Open."
"6508155847;35364952500;55121254900;6505778090;55968054200;","Temporal co-registration for TROPOMI cloud clearing",2012,"10.5194/amt-5-595-2012","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84858741848&doi=10.5194%2famt-5-595-2012&partnerID=40&md5=60a0be9f6aee8c938e96b99d20db6d96","The TROPOspheric Monitoring Instrument (TROPOMI) is anticipated to provide high-quality and timely global atmospheric composition information through observations of atmospheric constituents such as ozone, nitrogen dioxide, sulfur dioxide, carbon monoxide, methane, formaldehyde and aerosol properties. The methane and the aerosol retrievals require very precise cloud clearing, which is difficult to achieve at the TROPOMI spatial resolution (7 by 7 km) and without thermal IR measurements. The TROPOMI carrier - the Sentinel 5 Precursor (S5P), does not include a cloud imager, thus it is planned to fly the S5P mission in a constellation with an instrument yielding an accurate cloud mask. The cloud imagery data will be provided by the US NPOESS Preparatory Project (NPP) mission, which will have the Visible Infrared Imager Radiometer Suite (VIIRS) on board (Scalione, 2004). This paper investigates the temporal co-registration requirements for suitable time differences between the VIIRS measurements of clouds and the TROPOMI methane and aerosol measurements, so that the former could be used for cloud clearing. The temporal co-registration is studied using Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI) data with 15 min temporal resolution (Veefkind, 2008b), and with data from the Geostationary Operational Environmental Satellite - 10 (GOES-10) having 1 min temporal resolution. The aim is to understand and assess the relation between the amount of allowed cloud contamination and the required time difference between the two satellites' overflights. Quantitative analysis shows that a time difference of approximately 5 min is sufficient (in most conditions) to use the cloud information from the first instrument for cloud clearing in the retrievals using data from the second instrument. In recent years the A-train constellation demonstrated the benefit of flying satellites in formation. Therefore this study's findings will be useful for designing future Low Earth Orbit (LEO) satellite constellations. © 2012 Author(s). CC Attribution 3.0 License."
"36816070800;8278450900;7004671182;6507294227;","On the enhancement of infrared satellite precipitation estimates using genetic algorithm filter-based feature selection",2011,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-84879753592&partnerID=40&md5=add63bae6abb05197fcca3c28170ff06","A methodology to enhance a satellite infrared - based high resolution rainfall retrieval algorithm is developed by intelligently selecting features based on a filter model. Our methodology for satellite-based rainfall estimation is similar to the PERSIANN-CCS approach. However, our algorithms are enriched by applying a filterbased feature selection using generic algorithm. The objective of using feature selection is to find the optimal set of features by removing the redundant and irrelevant features. Since we use unsupervised cloud classification technique, Self Organizing Map (SOM), an unsupervised feature selection method, is used. In our approach, first the redundant features are removed by using a feature similarity-based filter and then using Entropy Index along with genetic algorithm searching, the irrelevant features are eliminated. The result shows that using feature selection process can improve Rain/No Rain detection about 10 % at some threshold values and also decreases the RMSE about 2mm."
"57217343488;7004485096;","The CloudSat Education Network: Scientifically significant collaborative research between students and scientists",2010,"10.1109/IGARSS.2010.5651427","https://www.scopus.com/inward/record.uri?eid=2-s2.0-78650859906&doi=10.1109%2fIGARSS.2010.5651427&partnerID=40&md5=7ed9bbbaa441bca456c60d175eb5117f","The CloudSat Education Network (CEN) is the primary education and public outreach component of the CloudSat mission. Approximately 116 schools in 16 countries around the world participate in the CEN, and are recruited from schools in the GLOBE program. Students and teachers in the CEN make atmospheric observations of temperature, precipitation, and crucially, of cloud type and cloud cover amount (including photographs of cloud observations), using a modified GLOBE Atmosphere protocol as a guide for observations. CEN observations are taken coincident with CloudSat overpasses, providing coincident spaceborne- and student surface observations. A preliminary comparison study using CEN-collected observations of cloud type during the period from 2007-2008 compared the observed cloud types to those retrieved using the CloudSat 2B-CLDCLASS product. In this preliminary study, there were 227 coincidental measurements between CEN schools and CloudSat overpasses, with an agreement rate of approximately 66% between the surface observers and satellite observations. © 2010 IEEE."
"16241942600;7003930724;24169569100;","Relation of rain clouds from satellite cloud classification to conventional precipitation surface data for Central Europe",2008,"10.1127/0941-2948/2008/0268","https://www.scopus.com/inward/record.uri?eid=2-s2.0-42949131597&doi=10.1127%2f0941-2948%2f2008%2f0268&partnerID=40&md5=e2e3e950d57bd36d6694197105b8eaa3","The scope of this paper is to show how satellite-derived cloud data and synoptic cloud data can be combined for the analysis of precipitation events. By applying the maximum-likelihood-method a bi-spectral cloud classification of Meteosat data has been performed and these data were compared to synoptic obtervations of the surface WMO network. Cloud classes and cloud cover were derived from a 12-year period 1992-2003 with half-hourly data between 06 and 18 UT. Satellite-derived cloud classes were further compared with conventional precipitation data and with present weather information (ww) to separate them into Cumuliform and stratiform cloud classes. For the verification of the results of the long period some single events of heavy rainfall were investigated, especially the weather situation during the Elbe flood 2002. © by Gebrüder Borntraeger 2008."
"56132618000;36950518200;","The role of cloud radiative forcing in the Asian-Pacific summer monsoon",2007,"10.3319/TAO.2007.18.3.623(A)","https://www.scopus.com/inward/record.uri?eid=2-s2.0-35649028440&doi=10.3319%2fTAO.2007.18.3.623%28A%29&partnerID=40&md5=769b70b77600ff431986513c62f34370","Convective-cloud clusters with strong precipitation occur frequently in most of the Asian-Pacific summer monsoon (APSM) regions such as the Bay of Bengal (BOB), South China Sea (SCS), and Tropical Western North Pacific (TWNP). Cloud radiative forcing (CRF) is important in these regions. The net CRF at the top of the atmosphere (TOA) has shown large cooling over these APSM regions. This is on account of the presence of large amounts of high clouds with large optical depth. Through data analysis, the summer convective precipitation in TWNP is as strong as that in the BOB. However, the average net CRF at the TOA in the BOB (∼ -36 Wm-2) is twice as big as in the TWNP (∼ -17 Wm-2). The spectral analysis of cloud optical depth shows that in the BOB, the highest power is in the intra-seasonal timescale, while in the TWNP, the leading spectral peaks are less than 10 days. The radiative cooling from net CRF at the TOA could be associated with low-frequency oscillation. The difference between the APSM regions is related to their sub-stages separating from CRF in time evolution. In a convective system, convective clouds can detrain to form other high clouds. In the APSM regions, large areas of high-thin and high-thick clouds cause different CRF at the TOA. These two types of CRF relate to precipitation, atmospheric vertical motion, and cloud life cycles etc. and should be separated from the APSM time evolution. We divided the APSM precipitation into two categories. As in the heavy-precipitation stage, clouds with large optical depth shield solar radiation and cause local and instantaneous surface cooling. The outgoing longwave radiation (OLR) is generally lower than 210 (Wm-2). The net CRF at the TOA is large negatively. Besides, large high-thin clouds can be found in a stage of relatively small or no precipitation. The OLR in this stage has a broad range and the net CRF is small and could be either positive or negative. The major difference between the APSM regions occurs in this stage. In this stage at the BOB, significant high-thick clouds cause negative net CRF, while more than half of the SCS and TWNP at this stage is dominated by large amounts of cirrus clouds. The optically thin cirrus clouds with large spatial size and long lasting time are important modulators for modifying the net CRF at the TOA. The poor simulation of the APSM climate in general circulation models (GCMs) maybe associated with the inability for accurately simulating the role of cirrus clouds in this stage. Based on the cloud classification of the International Satellite Cloud Climatology Project (ISCCP), we found a useful cloud-amount index from cloud amounts of cirrus minus the sum of deep convection and cirrostratus. The index can effectively separate different characteristics of CRF from the APSM time evolution. The cloud-amount index should be more appropriate for APSM studies and model simulations instead of considering only one cloud type in convective systems."
"55781536800;55781312700;","Analysis of severe storms in summer time in the Northwest region of Russian Federation using satellite data",2005,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-84879702281&partnerID=40&md5=4ae20f2b1ca0af6bbdcc211a0f94f603","Cloud analysis which used summer time (June-August, 2001, 2002) data gathered over the Northwest region of Russian Federation by radiometer AVHRR/NOAA has been performed. For this purpose the situations with cumulonimbus cloud form connected with severe weather phenomena (squall lines, downpours, thunderstorms) were chosen. The analysis was compared with the precipitation means and severe weather phenomena occurrences obtained from the ground weather observations. The results of the cumulunimbus cloud classification by the types of the weather pattern and results of the statistical analysis of the cloud top height and of the precipitation amounts are presented."
"7003292586;56153005000;6603082632;6602688105;6601968781;","Experimental study of polarization characteristics of lidar signal in case of occlusion front",2005,"10.1080/0143116042000274113","https://www.scopus.com/inward/record.uri?eid=2-s2.0-13344277983&doi=10.1080%2f0143116042000274113&partnerID=40&md5=fead3d0caddd12d297fa117750c714e0","In this work, experimental data of light detection and ranging (lidar) polarization study of cloud formations in a case of warm occlusion front in winter are presented. The changes in the low clouds at the different stages of the front advection are followed: before, during and after the cold air mass settles down. The experiment was carried out using a polarization lidar with variable viewing angle of the receiver, which allows the influence of the multiple scattering on the signal depolarization to be estimated. The data are acquired by simultaneously recording two polarized components of the lidar return: parallel and perpendicular with respect to that of the sounding radiation. The depolarization coefficient of the signals from various clouds types (stratus, stratocumulus, nimbus stratus, etc.) is determined by receiving and rejecting the multiply scattered lidar returns. The depolarization of the lidar returns is determined also in the space between the ground and the clouds base during different stages of the front advection including wet snowfall and no precipitation; the typical values obtained are: 3-5% before precipitation, 5-7% during rain, 10-40% during snowfall and 1-2% after precipitation. Conclusions are drawn about the phase composition of the clouds formations and the heights of the ice crystals nucleation during snowfall. So the evolution of the atmospheric formations is followed during the different stages of the warm occlusion front advection. © 2005 Taylor & Francis Ltd."
"6701607011;6701599239;6604000335;22970696400;7003711370;8680433400;6507267924;8680433600;6602885778;","Comparison of cloud types observed from SEVIRI and POLDER",2004,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-23844520406&partnerID=40&md5=ea45af11e8cada99d073622f423a7f30","A promising way of improving cloud property retrieval, is the combined use of new ensemble of data based on different measurement techniques. As a first step, a comparative study of cloudiness observed by SEVIRI on board Meteosat-8 and POLDER2 (POLarization and Directionality of the Earth's Reflectances) is performed. POLDER-2 cloud products are available only from April to October, 23rd 2003, the end of service of the ADEOS-2 platform. Several days in June 2003 are analyzed. The SEVIRI radiance data and the SAFNWC (Satellite Application Facility in support to NoW Casting) cloud products have been provided by the ""Centre de Météorologie Spatiale"" in Lannion (France). The SEVIRI cloud type and cloud top pressure products are checked against cloud top pressure and thermodynamic phase retrieved from POLDER. A cloud classification based on a Dynamical Clustering Method (DCM) is applied to SEVIRI data for an other interpretation. Late 2004, PARASOL will be launch in the frame of the A-train. The study engaged between POLDER2 and SEVIRI will then go further."
"6507079645;","Cloud types in Kołobrzeg, Poznań and Wieluń in the months with the lowest and highest cloudiness",2003,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-35348981279&partnerID=40&md5=7afe66ae572711065c5cc299528f8430","The comparison of the cloudiness in Kołobrzeg, Poznań, and Wieluń in the months with the lowest and highest cloudiness has been presented. The general cloudiness and the frequency of the occurrence of cloud types in chosen localities have been considered."
"7201898106;7005160468;7006251779;6603327182;55477384200;7004639658;6602184993;7006212327;7004249622;8245694000;7005332556;6701338417;7003961021;7005433221;57204302411;6603604042;7004005128;","Optical classification, existence temperatures, and coexistence of different polar stratospheric cloud types",1999,"10.1029/1999JD900064","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85045784723&doi=10.1029%2f1999JD900064&partnerID=40&md5=3dec78ebb18d3ae043021bf0d54bd385","Multispectral lidar measurements of polar stratospheric clouds (PSCs) from two winter campaigns in 1994/1995 and 1996/1997 at Sodankylä, Finland, have been evaluated together with temperature data from local radiosondes to find optical parameters for a PSC classification of different particle types and their existence temperatures. Precise depolarization measurements show that both solid and liquid particles exist below the NAT (nitric acid trihydrate) temperature. A comparison of temperatures at the PSC base and at the cloud top shows a good agreement with the NAT-existence temperature for solid type Ia clouds and a 3-4 K lower temperature for liquid type Ib clouds. The two particle families are therefore consistent with solid NAT particle formation and condensational growth of HNO3, H2O and H2SO4 liquid ternary solutions. The coexistence of solid and liquid particles has been observed by means of the temporal development of parallel and perpendicular polarized lidar signals. These time series of subsequent lidar measurements show stronger and faster fluctuations in the liquid particle mode compared to the solid particles and thus indicate a higher sensitivity toward temperature fluctuations for the liquid PSCs. While the optical properties of most observations are consistent with the definition of PSC type Ia (solid) and type Ib (liquid) clouds, a third type has been observed which does not fit into the current type Ia/Ib optical classification. This cloud type consists of solid particles but has a higher backscatter than type Ia PSC. Copyright 1999 by the American Geophysical Union."
"7004337580;55999772700;35615424000;7006172186;7004463365;6506940684;7004575340;","Cloud detection with GOME: a refinement of the cloud clearing algorithm using ATSR-2 images",1998,"10.1109/igarss.1998.699515","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0031631762&doi=10.1109%2figarss.1998.699515&partnerID=40&md5=7744f4129a74b08e50c03b841a1c558d","The Global Ozone Monitoring Experiment (GOME), flown on-board the ERS2 satellite since 1995, has the main mission of retrieving total ozone at the nominal ground resolution of 320×40 Km2. The retrieval of different trace gases and aerosol can also be attempted. Cloud detection and characterization, an interesting result in itself, are needed to analyse spectral data prior the retrieval of columnar ozone as well as other atmospheric constituents. The Polarisation Measurement Devices (PMDs) allow for detection of radiation leaving the Earth-atmosphere system at three spectral broad channels, from 300 to 800 nm. The Cloud Clearing Algorithm (CCA [1]) was developed based on a simple thresholding method: cloud detection is obtained within the PMD ground pixel (20×40 Km2, one-sixteenth of GOME's spatial resolution) using thresholds that depend primarily on surface type and reflection, and solar zenith angles. A refinement of the CCA is presented hereafter. Thresholds over the ocean have been computed by comparing PMD detection performances with the Along Track Scanning Radiometer 2 (ATSR2) cloud masks. ATSR2 masks are available on a 2×2 Km2 spatial resolution. Note that GOME and ATSR2 do fly on-board the same spacecraft, thus producing simultaneous nadir images with very reliable co-location. Refined CCA performances have been compared with a totally independent cloud classification algorithm [2] that uses visible-infrared, high resolution full disk METEOSAT images. Case studies are presented, and differences between the two methods are discussed at PMD and spectral GOME ground pixel sizes."
"6701752045;6603307411;","A method to derive surface insolation from NOAA AVHRR data",1997,"10.1016/S0273-1177(97)00067-7","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0030708806&doi=10.1016%2fS0273-1177%2897%2900067-7&partnerID=40&md5=940f0d95aaec94b1db3089f7448beeb7","Histogram technique in cloud classification is based on the idea that the histogram of pixel radiances over an area will show clusters of pixels that represent different cloud or surface types. Assuming that radiance reflected (emitted) by a certain cloud level or surface type is normally distributed, the radiance histograms collected over any regular area are mixtures of normal components. A scheme is produced for a two-dimensional case to analyse NOAA AVHRR CH1 and CH4 radiance histograms and separate the components. As a result, mean reflectance and (top) temperature values for each component can be obtained together with the weights of the components that describe cloudy and cloud-free fractions. A simple method to calculate mean (over the histogram area) daily surface insolation is introduced. The necessary coefficients to convert cloud amount and cloud reflectance into atmospheric transmittance have been found by means of ground-truth measurements at seven actinometric stations of Poland. © 1997 COSPAR."
"7005602760;","Recent studies on satellite remote sensing of clouds in Japan",1996,"10.1016/0273-1177(95)00286-3","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0029751940&doi=10.1016%2f0273-1177%2895%2900286-3&partnerID=40&md5=29617498129046b4092e4a2a5f6afb5c","In the latest decade satellite remote sensing of atmospheres, especially of clouds, has been rapidly developed in Japan. A brief review of satellite remote sensing of clouds by Japanese scientists is introduced in this paper. Most of the satellite data used in these studies are obtained by NOAA Advanced Very High Resolution Radiometer (AVHRR). From the infrared split window data which is defined as a brightness temperature difference of AVHRR between channel 4 and channel 5, cloud type classification in the tropical region and identification of clouds over the ice sheet in the Antarctica were successfully carried out. The optical thickness, droplet effective radius, and liquid water path of lower level stratified clouds were obtained from the reflection measurements of solar radiation by visible channel 1 and near infrared channel 3. In addition to AVHRR data analysis, a future perspective of Japanese Earth observation satellite is shortly described."
"7201361035;","The variability of cloud cover and cloud forcing inferred from NOAA AVHRR data for the North Sea",1995,"10.1016/0273-1177(95)00376-P","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0028808424&doi=10.1016%2f0273-1177%2895%2900376-P&partnerID=40&md5=f96856b7313d01cfd75a46290fee9d34","From August 1990 until September 1994, NOAA AVHRR HRPT data were analysed to determine the variability of cloud cover and of cloud forcing at top of atmosphere (TOA) for the North Sea (1300×1300 km2). The first step was a detailed cloud classification based on the maximum likelihood method. That classification scheme allows a discrimination of 24 cloud classes, especially high clouds with different optical properties. The validation of these results is based on synoptical observations. Therefore, the annual variability of cloud cover can be shown for individual synoptical ground stations and for larger areas inferred from satellite data. Due to an overlapping period with ISCCP C2 data, a comparison with the high-resolution results was carried out. That led to a 10 year cloud cover climatology (1983-1993) for the North Sea, where 24 grid areas (each 2.5°×2.5°) could be investigated. A further step is the computation of cloud-climate efficiency and cloud forcing at top of atmosphere, where cloud forcing is the area mean of cloud-climate efficiencies. The cloud-climate efficiency is the cloud forcing of an individual classified cloud. For the analysed period, the variability of cloud forcing depends strongly on the cloud cover variability. Thus, the drought 1992 in Schleswig-Holstein and in northern Germany can be detected and the influence of high clouds, their amplifying heating effect, can be shown. © 1995."
"6507267924;7003711370;","Combined use of AVHRR products and meteosat imagery in nowcasting",1992,"10.1016/0273-1177(92)90216-K","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0026454318&doi=10.1016%2f0273-1177%2892%2990216-K&partnerID=40&md5=04b40c91b10b6575bc73ea7fb61e85b3","We give two examples of AVHRR (Advanced Very High Resolution Radiometer) products which can help forecasters analyse Meteosat imagery. At night, it may be impossible to detect low clouds over land or sea and to distinguish between high thick clouds and cirrus with infrared Meteosat imagery. These tasks can be easily accomplished with AVHRR data. A nighttime cloud classification algorithm, based on threshold tests applied to different combinations of AVHRR channels, is processed on all NOAA-11 AVHRR data received in the Centre de Meteorologie Spatiale in Lannion (France). A final four-bit image covering France is then derived and operationally delivered to forecasters through the French Meteotel network at the same scale and projection as Meteosat images for combined use. Interpreting Meteosat daytime images can also be difficult because of the confusion between snowy ground and clouds. This distinction is operationally done with AVHRR channel 3, but it is not yet sent to forecasters. These areas covered by snow could be combined to a four-bit daytime cloud classification and displayed at the same scale as Meteosat images on the Meteotel network. © 1992."
"7004983356;6602861814;6601981008;","Restitution of surface radiative fluxes from Meteosat data and weather forecasting model outputs",1991,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-0026405496&partnerID=40&md5=c394541ea8b89a17d0065da5c793b369","In order to perform real-time restitution of surface radiative fluxes at small or medium scale over Europe, the northeast Atlantic, and the Mediterranean Sea, it has been decided to derive the cloud characteristics from Meteosat data, and the required atmospheric parameters from numerical weather forecasting model outputs or from climatology. The cloud classification method, its operational implementation, and its application for downward longwave flux (DLF) calculation are presented. A relative calibration of Meteosat visible channels and its application to the operational restitution of shortwave flux over Europe is briefly discussed."
"56994091700;24828175500;7201950609;","Retrieval of cloud classification parameters using two-dimensional fast Fourier transform",1988,"10.1007/BF02861851","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0024151236&doi=10.1007%2fBF02861851&partnerID=40&md5=37eb6a759230ba64b6527fa7df00bc93","A method is presented for the retrieval of classification parameters of clouds observed by satellite-borne imaging systems. It is based on a two-dimensional fast Fourier transform of cloud images and an analysis of their power spectra. The parameters retrieved provide quantitative information on mean brightness, size, shape and directional properties of clouds. The efficacy of the subdivision of the original cloud image into smaller regions and the determination of individual parameters is demonstrated by applying this procedure to some NOAA and INSAT cloud images. © 1988 Indian Academy of Sciences."
"6602605286;7102861843;6507130050;57217711113;","AUTOMATIC CLOUD CLASSIFICATION.",1985,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-0022308001&partnerID=40&md5=65a4a816d986de508cf2ee7bd5e055bd","The design and implementation of an automatic cloud classifier are discussed. It is assumed that the pattern classes have a multivariate normal distribution. Maximum likelihood with threshold and penalized misclassification algorithms have been implemented both on a VAX-11/750 host computer and on an FPS-100 array processor. This technique classifies not only different cloud types but also land, sea, and snow as well. The result of classification and the time required on the host as well as on the array processor for various picture complexities have been tested. The array processor results are found to approach real-time performance. NOAA satellite pictures were used for testing."
"6701607011;24456297600;57196396429;","Cloud cover analysis using spectral and spatial characteristics of meteosat images",1985,"10.1016/0273-1177(85)90315-1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-46549100514&doi=10.1016%2f0273-1177%2885%2990315-1&partnerID=40&md5=11a88b568617414eaeea2bb8a96219c8","New developments of a cloud classification scheme based on histogram clustering by a statistical method are studied. Use of time series of satellite pictures and of spatial variances is introduced and discussed. © 1985."
"16425859400;57219948630;","Incorporating handcrafted features into deep learning for point cloud classification",2020,"10.3390/rs12223713","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096232980&doi=10.3390%2frs12223713&partnerID=40&md5=c312b162302d207f5837672ac9a4cf91","Point cloud classification is an important task in point cloud data analysis. Traditional point cloud classification is conducted primarily on the basis of specific handcrafted features with a specific classifier and is often capable of producing satisfactory results. However, the extraction of crucial handcrafted features hinges on sufficient knowledge of the field and substantial experience. In contrast, while powerful deep learning algorithms possess the ability to learn features automatically, it normally requires complex network architecture and a considerable amount of calculation time to attain better accuracy of classification. In order to combine the advantages of both the methods, in this study, we integrated the handcrafted features, whose benefits were confirmed by previous studies, into a deep learning network, in the hopes of solving the problem of insufficient extraction of specific features and enabling the network to recognise other effective features through automatic learning. This was done to achieve the performance of a complex model by using a simple model and fulfil the application requirements of the remote sensing domain. As indicated by the experimental results, the integration of handcrafted features into the simple and fast-calculating PointNet model could generate a classification result that bore comparison with that generated by a complex network model such as PointNet++ or KPConv. © MDPI AG. All rights reserved."
"55712252200;57207239698;13906187400;15841022400;23003667300;57219899212;57219898386;","Tropical cyclone temperature profiles and cloud macro-/micro-physical properties based on airs data",2020,"10.3390/atmos11111181","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095963009&doi=10.3390%2fatmos11111181&partnerID=40&md5=78886ef1af216731f687da1e8e094984","We used the observations from Atmospheric Infrared Sounder (AIRS) onboard Aqua over the northwest Pacific Ocean from 2006–2015 to study the relationships between (i) tropical cyclone (TC) temperature structure and intensity and (ii) cloud macro-/micro-physical properties and TC intensity. TC intensity had a positive correlation with warm-core strength (correlation coefficient of 0.8556). The warm-core strength increased gradually from 1 K for tropical depression (TD) to >15 K for super typhoon (Super TY). The vertical areas affected by the warm core expanded as TC intensity increased. The positive correlation between TC intensity and warm-core height was slightly weaker. The warm-core heights for TD, tropical storm (TS), and severe tropical storm (STS) were concentrated between 300 and 500 hPa, while those for typhoon (TY), severe typhoon (STY), and Super TY varied from 200 to 350 hPa. Analyses of the cloud macro-/micro-physical properties showed that the top of TC cloud systems mainly consisted of ice clouds. For TCs of all intensities, areas near the TC center showed lower cloud-top pressures and lower cloud-top temperatures, more cloud fractions, and larger ice-cloud effective diameters. With the increase in TC intensity, the levels of ice clouds around the TC center became higher and the spiral cloud-rain bands became larger. When a TC developed into a TY, STY, or Super TY, the convection in the clouds was stronger, releasing more heat, thus forming a much warmer warm core. © 2020 by the authors. Licensee MDPI, Basel, Switzerland."
"56188627800;57209886863;56068624000;54402367600;56897622400;","Multimodal Ground-Based Remote Sensing Cloud Classification via Learning Heterogeneous Deep Features",2020,"10.1109/TGRS.2020.2984265","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095784306&doi=10.1109%2fTGRS.2020.2984265&partnerID=40&md5=ac5f72b72969ca9a58b6dc3709f17e37","Recently, multimodal cloud samples are utilized to learn completed feature representations for cloud classification. However, the existing methods neglect the related information from other multimodal cloud samples in the learning process, which leads to inadequate learning. In this article, we propose a novel deep model to learn heterogeneous deep features (HDFs) for multimodal ground-based remote sensing cloud classification. Specifically, we first design the convolutional neural network (CNN) extractor to combine the visual information and the multimodal information (MI) to obtain the CNN-based features of multimodal cloud samples. Afterward, we treat the CNN-based features of multimodal cloud samples as the nodes of graph, and utilize the similarity between nodes as the adjacency matrix. We feed the graph and the adjacency matrix into the graph convolutional network (GCN) extractor to obtain the GCN-based features that could capture correlations among multimodal cloud samples using graph convolutional layers. After obtaining CNN-based features and GCN-based features, we concatenate the two kinds of heterogeneous features to represent the multimodal cloud samples. As a result, the concatenated feature contains the visual information, the MI and the related information among multimodal cloud samples. We conduct a series of experiments on the multimodal ground-based cloud database (MGCD), and the experimental results verify that the proposed HDF outperforms state-of-the-art methods. © 1980-2012 IEEE."
"57190495366;56604418600;57194447516;56995337200;56796094100;57211810495;57203439071;57198630027;23970956600;7004260140;","A practical method for employing multi-spectral LiDAR intensities in points cloud classification",2020,"10.1080/01431161.2020.1775323","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089952460&doi=10.1080%2f01431161.2020.1775323&partnerID=40&md5=8a9cc256027946dc650658cbb291bd5d","Light Detection and Ranging (LiDAR) intensity is associated with the target surface material, which could help the points cloud classification. However, the intensity is also associated with the laser beam incident angle and the transmitting distance, which obstructs its further application in points cloud classification. Motivated by this problem, this paper proposed a practical method for employing the LiDAR intensities in points cloud classification without distance and incident angle calibration, specifically, ratio values between different spectral channels from a newly invented Hyper-spectral LiDAR (HSL) were defined and calculated for generating robust spectral features. Since the HSL different channels had the same transmitting distance and incident angle, therefore, the ratio values were independent on the laser pulse transmitting distance and laser beam incident angle. An indoor experiment was conducted for fully assessing the proposed method. The HSL had eight different spectral channels with spectral wavelength covering from 650 nm to 1000 nm. In the experiments, papers with different colours were pasted on a flat glass; the HSL scanned them at four distinctive positions with 60 cm displacement. The spectral ratio values between different channels at each position were calculated using the obtained multiple spectral profiles from the HSL. The results showed that the points cloud scanned at different incident and distance could be classified though the spectral ratio values without complex distance and incident angle calibration. © 2020 Informa UK Limited, trading as Taylor & Francis Group."
"36705265400;28367935500;6602239759;35114996800;23970271800;","Increasing Resolution and Resolving Convection Improve the Simulation of Cloud-Radiative Effects Over the North Atlantic",2020,"10.1029/2020JD032667","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092602764&doi=10.1029%2f2020JD032667&partnerID=40&md5=4787a66ec9e3dd56938954cd6219cb98","Clouds interact with atmospheric radiation and substantially modify the Earth's energy budget. Cloud formation processes occur over a vast range of spatial and temporal scales, which make their thorough numerical representation challenging. Therefore, the impact of parameter choices for simulations of cloud-radiative effects is assessed in the current study. Numerical experiments are carried out using the ICOsahedral Nonhydrostatic (ICON) model with varying grid spacings between 2.5 and 80 km and with different subgrid-scale parameterization approaches. Simulations are performed over the North Atlantic with either one-moment or two-moment microphysics and with convection being parameterized or explicitly resolved by grid-scale dynamics. Simulated cloud-radiative effects are compared to products derived from Meteosat measurements. Furthermore, a sophisticated cloud classification algorithm is applied to understand the differences and dependencies of simulated and observed cloud-radiative effects. The cloud classification algorithm developed for the satellite observations is also applied to the simulation output based on synthetic infrared brightness temperatures, a novel approach that is not impacted by changing insolation and guarantees a consistent and fair comparison. It is found that flux biases originate equally from clear-sky and cloudy parts of the radiation field. Simulated cloud amounts and cloud-radiative effects are dominated by marine, shallow clouds, and their behavior is highly resolution dependent. Bias compensation between shortwave and longwave flux biases, seen in the coarser simulations, is significantly diminished for higher resolutions. Based on the analysis results, it is argued that cloud-microphysical and cloud-radiative properties have to be adjusted to further improve agreement with observed cloud-radiative effects. © 2020. The Authors."
"36657489600;7101984634;7404548584;7006423243;","Leveraging spatial textures, through machine learning, to identify aerosols and distinct cloud types from multispectral observations",2020,"10.5194/amt-13-5459-2020","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85093500613&doi=10.5194%2famt-13-5459-2020&partnerID=40&md5=4d56ed1362f65276c7a11bb1f418ea57","Current cloud and aerosol identification methods for multispectral radiometers, such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS), employ multichannel spectral tests on individual pixels (i.e., fields of view). The use of the spatial information in cloud and aerosol algorithms has been primarily through statistical parameters such as nonuniformity tests of surrounding pixels with cloud classification provided by the multispectral microphysical retrievals such as phase and cloud top height. With these methodologies there is uncertainty in identifying optically thick aerosols, since aerosols and clouds have similar spectral properties in coarse-spectral-resolution measurements. Furthermore, identifying clouds regimes (e.g., stratiform, cumuliform) from just spectral measurements is difficult, since low-altitude cloud regimes have similar spectral properties. Recent advances in computer vision using deep neural networks provide a new opportunity to better leverage the coherent spatial information in multispectral imagery. Using a combination of machine learning techniques combined with a new methodology to create the necessary training data, we demonstrate improvements in the discrimination between cloud and severe aerosols and an expanded capability to classify cloud types. The labeled training dataset was created from an adapted NASA Worldview platform that provides an efficient user interface to assemble a human-labeled database of cloud and aerosol types. The convolutional neural network (CNN) labeling accuracy of aerosols and cloud types was quantified using independent Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and MODIS cloud and aerosol products. By harnessing CNNs with a unique labeled dataset, we demonstrate the improvement of the identification of aerosols and distinct cloud types from MODIS and VIIRS images compared to a per-pixel spectral and standard deviation thresholding method. The paper concludes with case studies that compare the CNN methodology results with the MODIS cloud and aerosol products. © Author(s) 2020. This work is distributed under the Creative Commons Attribution 4.0 License."
"6603299074;6508011000;7801621547;15039960300;57194217280;57205125890;57207986597;57207983497;","Investigating the feasibility of artificial convective cloud creation",2020,"10.1016/j.atmosres.2020.104998","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083394212&doi=10.1016%2fj.atmosres.2020.104998&partnerID=40&md5=4e563642a289ecf22b7dee1738458610","In this paper, we study the physical justification and experimental possibility of stimulating convection in the atmosphere to create artificial convective clouds and precipitation. A prerequisite for creation of artificial convective clouds is the presence of water vapor in the atmosphere; this is an encouraging factor as water vapor is present even in regions with an arid climate. Furthermore, numerous cases of convective cloud development under the influence of powerful heat sources are known, even for days lacking conditions for natural cloud development. In this paper, we analyze the results and failure causes of worldwide experiments with meteotrons, which are stationary installations designed to initiate powerful updrafts. To enhance meteotron jet buoyancy, a combined method based on use of a turbojet engine is proposed; the jet output by the engine creates an aerosol cloud that absorbs shortwave solar radiation. An overview of mathematical models of convective jets is given and the assumptions made during mathematical formulation of the problem are analyzed. Theoretical and laboratory results are compared. A mathematical model of a convective jet is proposed and an analytical solution is obtained in cylindrical coordinates. It is shown that the upward flow velocity decreases to a minimum at an altitude where the jet and environment temperatures are aligned. In contrast, the jet radius increases and, at the temperature equalization height, the jet adopts an umbrella form. We conclude that, for an artificially created stream to contribute to development of cloud convection, the temperature equalization height should be equal to or greater than the condensation level. The results obtained in this study can be used in the trials on creation of artificial updrafts and clouds. © 2020"
"55553729023;8937646200;57218918409;57203789706;","Clouda: A ground-based cloud classification method with a convolutional neural network",2020,"10.1175/JTECH-D-19-0189.1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090790468&doi=10.1175%2fJTECH-D-19-0189.1&partnerID=40&md5=aa91695e2cca8204207b0efabf557624","Conventional classification methods are based on artificial experience to extract features, and each link is independent, which is a kind of ‘‘shallow learning.’’ As a result, the scope of the cloud category applied by this method is limited. In this paper, we propose a new convolutional neural network (CNN) with deep learning ability, called CloudA, for the ground-based cloud image recognition method. We use the Singapore Whole-Sky Imaging Categories (SWIMCAT) sample library and total-sky sample library to train and test CloudA. In particular, we visualize the cloud features captured by CloudA using the TensorBoard visualization method, and these features can help us to understand the process of ground-based cloud classification. We compare this method with other commonly used methods to explore the feasibility of using CloudA to classify ground-based cloud images, and the evaluation of a large number of experiments show that the average accuracy of this method is nearly 98.63% for ground-based cloud classification. © 2020 American Meteorological Society."
"35766145000;7006861646;6507876616;6506416572;6507594716;","Homogenization of Geostationary Infrared Imager Channels for Cold Cloud Studies Using Megha-Tropiques/ScaRaB",2020,"10.1109/TGRS.2020.2978171","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090409073&doi=10.1109%2fTGRS.2020.2978171&partnerID=40&md5=1d2fbc49616562684f61a6d08123b474","Infrared (IR) observations from the fleet of multiagencies meteorological geostationary satellites have a great potential to support scientific and operational investigations at a quasi-global scale. In particular, such a data record, defined as the GEOring data set, is well suited to document the tropical convective systems life cycles by applying cloud tracking algorithms. Yet, this GEOring data set is far from being homogeneous, preventing the realization of its potential. A number of sources of inhomogeneities are identified ranging from spatiotemporal resolutions to spectral characteristics of the IR channels and calibration methodologies. While previous efforts have attempted to correct such issues, the adjustment of the cold part of the IR spectrum remains unfit for cold cloud studies. Here, a processing method is introduced to minimize the inhomogeneities against a reference observational data set from the Scanner for Radiation Budget (ScaRaB) instrument onboard the Megha-Tropiques satellite. The method relies on the collocations between the geostationary observations and the reference. The techniques exhibit significant sensitivity to the selection of the relevant pairs of observations requiring a dedicated filtering of the data. A second effort is then proposed to account for the limb-darkening effect and a method is developed to correct the brightness temperature (BT) dependence on the geostationary viewing zenith angle (VZA). Overall, results show a residual after the processing of 0 K between any of the geostationary data and the ScaRaB reference. The final calibrated and limb-adjusted IR observations are then homogeneous for cold BT lower than 240 K with a standard deviation lower than 1.5 K throughout the GEOring. © 1980-2012 IEEE."
"57193699393;23476370700;","On the Factors That Determine Boundary Layer Albedo",2020,"10.1029/2019JD032244","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089367828&doi=10.1029%2f2019JD032244&partnerID=40&md5=55d42809e2df08b435853f44de3436c1","This study investigates the factors that control marine boundary layer cloud albedo measured by the Multiangle Imaging SpectroRadiometer (MISR) over domains of (200 km)2. We use three key metrics to investigate domain albedo: cloud fraction, cloud heterogeneity, and cloud morphology. Cloud heterogeneity is quantified at the domain level with a unified heterogeneity index. Cloud morphology is determined from a cloud classification algorithm using an Artificial Neural Network (ANN) to classify each domain into one of four categories: (i) closed-cell Mesoscale Cellular Convection (MCC); (ii) open-cell MCC; (iii) disorganized MCC; and (iv) No MCC. These different types of MCC are usefully defined as low clouds of different morphologies. Classifications from the ANN are also combined with the satellite observations of MISR to develop relationships between cloud morphology, domain albedo, cloud fraction, and cloud heterogeneity. Cloud morphology is found to play an essential role in modulating these relationships. The cloud fraction-albedo relationships are found to be directly a function of cloud morphology. Relationships between domain albedo and cloud heterogeneity are also found to be a function of MCC type. Our results indicate that the albedo has a strong dependence on cloud morphology and cloud heterogeneity. Understanding both the physical properties and the meteorological controls on MCC has important implications for understanding low cloud behavior and improving their representation in General Circulation Models. © 2020. American Geophysical Union. All Rights Reserved."
"57195695111;23005893600;57216759668;","Supervised Classification and Its Repeatability for Point Clouds from Dense Vhr Tri-Stereo Satellite Image Matching Using Machine Learning",2020,"10.5194/isprs-annals-V-2-2020-525-2020","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091102393&doi=10.5194%2fisprs-annals-V-2-2020-525-2020&partnerID=40&md5=21c722c8bc08a99d4e0b266991fa510b","Image matching of aerial or satellite images and Airborne Laser Scanning (ALS) are the two main techniques for the acquisition of geospatial information (3D point clouds), used for mapping and 3D modelling of large surface areas. While ALS point cloud classification is a widely investigated topic, there are fewer studies related to the image-derived point clouds, even less for point clouds derived from stereo satellite imagery. Therefore, the main focus of this contribution is a comparative analysis and evaluation of a supervised machine learning classification method that exploits the full 3D content of point clouds generated by dense image matching of tri-stereo Very High Resolution (VHR) satellite imagery. The images were collected with two different sensors (Pleíades and WorldView-3) at different timestamps for a study area covering a surface of 24&thinsp;km2, located in Waldviertel, Lower Austria. In particular, we evaluate the performance and precision of the classifier by analysing the variation of the results obtained after multiple scenarios using different training and test data sets. The temporal difference of the two Pleíades acquisitions (7 days) allowed us to calculate the repeatability of the adopted machine learning algorithm for the classification. Additionally, we investigate how the different acquisition geometries (ground sample distance, viewing and convergence angles) influence the performance of classifying the satellite image-derived point clouds into five object classes: <i>ground, trees, roads, buildings, and vehicles</i>. Our experimental results indicate that, in overall the classifier performs very similar in all situations, with values for the F1-score between 0.63 and 0.65 and overall accuracies beyond 93%. As a measure of repeatability, stable classes such as buildings and roads show a variation below 3% for the F1-score between the two Pleíades acquisitions, proving the stability of the model. © 2020 Copernicus GmbH. All rights reserved."
"57204567789;21735637400;7102383497;","Geometry-Based Point Cloud Classification Using Height Distributions",2020,"10.5194/isprs-annals-V-2-2020-259-2020","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091088116&doi=10.5194%2fisprs-annals-V-2-2020-259-2020&partnerID=40&md5=40c076e32b3c2790967bec5877a8e21f","Semantic segmentation is one of the main steps in the processing chain for Airborne Laser Scanning (ALS) point clouds, but it is also one of the most labour intensive steps, as it requires many labelled examples to train a classifier. National mapping agencies (NMAs) have to acquire nationwide ALS data every couple of years for their duties. Having point clouds cover different terrains such as flat or mountainous regions, a classifier often requires a refinement using additional data from those specific terrains. In this study, we present an algorithm, which is able to classify point clouds of similar terrain types without requiring any additional training data and which is still able to achieve overall F1-Scores of over 90% in most setups. Our algorithm uses up to two height distributions within a single cell in a rasterized point cloud. For each distribution, the empirical mean and standard deviation are calculated, which are the input for a Convolutional Neural Network (CNN) classifier. Consequently, our approach only requires the geometry of point clouds, which enables also the usage of the same network structure for point clouds from other sensor systems such as Dense Image Matching. Since the mean ground level varies with the observed area, we also examined five different normalisation methods for our input in order to reduce the ground influence on the point clouds and thus increase its transferability towards other datasets. We test our trained networks on four different tests sets with the classes' ground, building, water, non-ground and bridge. © 2020 Copernicus GmbH. All rights reserved."
"57190034210;57194694881;55444846100;54787529900;6507122674;","A hierarchical machine learning approach for multi-level and multi-resolution 3d point cloud classification",2020,"10.3390/RS12162598","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090017553&doi=10.3390%2fRS12162598&partnerID=40&md5=1d252318efd2db65b905fc1cde7c5ed9","The recent years saw an extensive use of 3D point cloud data for heritage documentation, valorisation and visualisation. Although rich in metric quality, these 3D data lack structured information such as semantics and hierarchy between parts. In this context, the introduction of point cloud classification methods can play an essential role for better data usage, model definition, analysis and conservation. The paper aims to extend a machine learning (ML) classification method with a multi-level and multi-resolution (MLMR) approach. The proposed MLMR approach improves the learning process and optimises 3D classification results through a hierarchical concept. The MLMR procedure is tested and evaluated on two large-scale and complex datasets: the Pomposa Abbey (Italy) and the Milan Cathedral (Italy). Classification results show the reliability and replicability of the developed method, allowing the identification of the necessary architectural classes at each geometric resolution. © 2020 by the authors."
"57211519015;8600097300;57213346004;57192512150;57205863098;","A novel classification extension-based cloud detection method for medium-resolution optical images",2020,"10.3390/RS12152365","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089689487&doi=10.3390%2fRS12152365&partnerID=40&md5=147cee8296c6f2bf6a93c639729a2d6d","Accurate cloud detection using medium-resolution multispectral satellite imagery (such as Landsat and Sentinel data) is always difficult due to the complex land surfaces, diverse cloud types, and limited number of available spectral bands, especially in the case of images without thermal bands. In this paper, a novel classification extension-based cloud detection (CECD) method was proposed for masking clouds in the medium-resolution images. The new method does not rely on thermal bands and can be used for masking clouds in different types of medium-resolution satellite imagery. First, with the support of low-resolution satellite imagery with short revisit periods, cloud and non-cloud pixels were identified in the resampled low-resolution version of the medium-resolution cloudy image. Then, based on the identified cloud and non-cloud pixels and the resampled cloudy image, training samples were automatically collected to develop a random forest (RF) classifier. Finally, the developed RF classifier was extended to the corresponding medium-resolution cloudy image to generate an accurate cloud mask. The CECD method was applied to Landsat-8 and Sentinel-2 imagery to test the performance for different satellite images, and the well-known function of mask (FMASK) method was employed for comparison with our method. The results indicate that CECD is more accurate at detecting clouds in Landsat-8 and Sentinel-2 imagery, giving an average F-measure value of 97.65% and 97.11% for Landsat-8 and Sentinel-2 imagery, respectively, as against corresponding results of 90.80% and 88.47% for FMASK. It is concluded, therefore, that the proposed CECD algorithm is an effective cloud-classification algorithm that can be applied to the medium-resolution optical satellite imagery. © 2020 by the authors."
"56566965900;57216739702;8425156900;7601329386;57216739012;35270436100;","Assessment of high-resolution satellite rainfall products over a gradually elevating mountainous terrain based on a high-density rain gauge network",2020,"10.1080/01431161.2020.1734255","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084463007&doi=10.1080%2f01431161.2020.1734255&partnerID=40&md5=56e620a160ec8236a314e79b24ae8e4a","High-resolution satellite rainfall products (SRPs) provide forcing inputs for hydrologic applications. Complex mountainous terrains have a significant effect on the occurrence and intensity of rainfall. This study focuses on the assessment of errors and rainfall detection capability of SRPs over a complex terrain with an elevation ranging from −95 to 3091 m based on a high-density rain gauge network over the Taihang Mountains of North China. The performance of four high-resolution SRPs (rain gauge bias-corrected Climate Prediction Center morphing technique (CMORPH CRT), Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG), Tropical Rainfall Measuring Mission (TRMM) 3B42V7; and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network-Cloud Classification System (PERSIANN-CCS)) was validated using 104 rain gauge stations from 1 January 2016 to 31 December 2017, and the results show that the annual rainfall varied from 375 mm to 1400 mm on average in 2016 and 2017. At the monthly scale, all SRPs except PERSIANN-CCS performed well. The spatial pattern of the annual rainfall variation, which was highest in the southeast and lowest in the northwest, was adequately captured by IMERG and 3B42 but not CMORPH CRT and PERSIANN-CCS. As indicated by the statistical metrics, all SRPs except PERSIANN-CCS exhibited better performance in the regions in the downward direction of the East-Asian Monsoon. In terms of rainfall detection, all SRPs exhibited moderate rainfall detection capability while IMERG exhibited the lowest false alarm ratio (FAR) equal to 0.41. Compared with 3B42, a significant improvement was found in IMERG, which presented increased correlation coefficient (r) and decreased FAR values over the study areas, and the improvement rate was 75% and 95%, respectively. All SRPs underestimated the no/light rainfall (0–1 mm day−1) events. IMERG and PERSIANN-CCS exhibited poor performance with significant underestimation of the 1–2 mm day−1 rainfall class and overestimation of the 2–5 mm day−1 rainfall class. Our results not only demonstrate the superiority of different products at different elevations but also provide suggestions for further improvement of the SRPs, especially for complex terrains. © 2020, © 2020 Informa UK Limited, trading as Taylor & Francis Group."
"57218467279;7203047936;35182211000;16557269700;57203995567;57217853393;","Improved Himawari-8/AHI Radiance Data Assimilation With a Double Cloud Detection Scheme",2020,"10.1029/2020JD032631","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087659508&doi=10.1029%2f2020JD032631&partnerID=40&md5=72f8a81b37bf75213bb843ba1cfe7cdf","This study explores the possibility of improving the impact of the Advanced Himawari Imager (AHI) clear-sky radiance data assimilation (DA), focusing on cloud detection. First, the performance of the “clear-channel” detection scheme of the minimum residual (MR) method embedded in the Gridpoint Statistical Interpolation (GSI) DA system is compared with the performances of the CLouds from Advanced Very High Resolution Radiometer Extended (CLAVR-x) cloud processing system and the Moderate Resolution Imaging Spectroradiometer (MODIS) cloud-product-generating algorithm. The MR scheme does not reliably identify optically thin clouds along cloud edges. The MR-estimated cloud-top pressures are often too high for upper-level clouds, rendering some cloud-contaminated channels falsely clear. An infrared-only AHI cloud mask (ACM) algorithm is added to the MR scheme to perform a so-called double cloud detection (DCD). The DCD scheme adds nine ACM tests for selecting clear pixels and two thin cloud tests for rejecting pixels affected by upper-level clouds. For a 1-month period, we show the positive impacts of assimilating AHI infrared channels on short-term forecasts of temperature and humidity using the DCD scheme rather than the MR scheme. Improvements in the DCD experiment extend more vertically, horizontally, and temporally than those in the MR experiment during the 48-hr forecasting time. In terms of daily variations in forecasting performance, the DCD experiment consistently improves while the MR experiment fluctuates between improvement and degradation. Such improvements come from an elimination of those data having negative observation-minus-background values of large magnitudes due to cloud contamination, which causes positive biases in humidity analyses. ©2020. American Geophysical Union. All Rights Reserved."
"57217867652;57217869675;56703329300;36815724000;","Object based convolutional neural network for cloud classification in very high-resolution hyperspectral imagery",2020,"10.1088/1755-1315/500/1/012059","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087782896&doi=10.1088%2f1755-1315%2f500%2f1%2f012059&partnerID=40&md5=df4409f5fa466fe285f678261fef95d7","Remote sensing has a critical role for spatial data-based information systems and monitoring of the earth's surface. The presence of clouds in optical sensing remote sensing satellite images is often a problem for many remote sensing applications. Therefore, the proper detection and classification of clouds in optical sensor remote sensing applications is quite a challenging task. This study aims to classify cloud objects in remote sensing satellite image data. The data used in this study is Pleiades very high-resolution satellite imagery data. The number of datasets used amounted to 1299 data. Cloud objects in this study are categorized into three classes, namely thick cloud, thin cloud, and clear. This study uses a deep learning algorithm, Convolutional Neural Network (CNN) for the classification of cloud objects. The CNN model used is LeNet with architectural modifications and parameters adjusted to the research needs. Classification of cloud objects with the LeNet model results in increased accuracy in each epoch during the training process and takes 1150.355 seconds for 200 epochs with the best accuracy value of 97.50%. The performance of LeNet is better than the VGG16 model as a comparison with the best accuracy of 96.50% with 600 data inputs. © 2020 IOP Publishing Ltd. All rights reserved."
"57209638348;57217094757;57208372273;57188746436;","Snow and cloud detection using a convolutional neural network and low-resolution data from the Electro-L No. 2 Satellite",2020,"10.1117/1.JRS.14.034506","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092645221&doi=10.1117%2f1.JRS.14.034506&partnerID=40&md5=6c3b3d056a9a651aeb99b4efec2b2893","We describe an algorithm based on a convolutional neural network that detects cloud formations and snow cover in satellite images using textures. Herein, multispectral satellite images, received from a multizone scanning instrument used for hydrometeorological support and installed on the Russian satellite Electro-L No. 2, are used as input data. The problem of snow and cloud classification in the absence of a spectral channel in the range of 1.4 to 1.8 μm, which is necessary for their accurate separation, is considered. The developed algorithm can produce cloud and snow cover masks for an area limited by the values of the solar zenith angle in the range of 0 deg to 80 deg for daytime. Algorithm accuracy was evaluated using machine learning metrics and comparing its results with ground truth masks segmented manually by an experienced interpreter. In addition, we compared the resulting masks with a similar cloud mask product from the European Organisation for the Exploitation of Meteorological Satellites based on the data of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument installed on the Meteosat-8 satellite. According to the results of this comparison, we conclude that the cloud masks produced by the proposed convolutional neural network-based algorithm have a lower probability of false detection than products based on the SEVIRI data. The proposed algorithm is fully automatic, and it works in any season of the year during the daytime. © 2020 Society of Photo-Optical Instrumentation Engineers (SPIE)."
"8117864800;7006041988;35454141800;7003553324;","Relationships between immersion freezing and crystal habit for arctic mixed-phase clouds-A numerical study",2020,"10.1175/JAS-D-20-0078.1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091595218&doi=10.1175%2fJAS-D-20-0078.1&partnerID=40&md5=6674a3d14aaf885e8903ed3c6b899e22","The number concentration of ice particles in Arctic mixed-phase clouds is a major controlling factor of cloud lifetime. The relationships between ice nucleation mode and ice crystal habit development are not yet constrained by observations. This study uses a habit-predicting microphysical scheme within a 3D large-eddy simulation model to evaluate the relationship between immersion freezing and ice habit in a simulated Arctic mixed-phase cloud case. Three immersion freezing parameterizations are considered: a volume-dependent freezing scheme (VF), a parameterization limited to activated droplets (C-AC), and a parameterization limited to coarse aerosol particles (C-CM). Both C-AC and C-CM are based on classical nucleation theory. The freezing rate with VF is found to be greater in downdraft regions than in updraft regions due to the downdraft having a higher number concentration of large droplets. The C-AC cases show active freezing of small droplets near cloud top, whereas in the C-CM cases, mainly the 8-32-μm-sized droplets freeze in updraft regions near the cloud base. Because the initial crystal size is assumed to affect the axis ratio of hexagonal plates, the VF cases produce crystals with larger axis ratios, resulting in smaller mode radii than the C-AC cases. In all cases, irregular polycrystals dominate near cloud top and a band-like structure develops within the cloud, which qualitatively agrees with previous observations. In the VF and C-CM cases, unactivated large droplets arising from coarse-mode aerosol particles contributed significantly to the freezing rate, producing an important influence on crystal habit. © 2020 American Meteorological Society."
"15129097800;57218295011;57218297589;57215532736;55680726300;7409195734;35307119000;57201430734;","Classification of point clouds for indoor components using few labeled samples",2020,"10.3390/rs12142181","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088628556&doi=10.3390%2frs12142181&partnerID=40&md5=295ff320cf444ba68f7d1b4a4837405f","The existing deep learning methods for point cloud classification are trained using abundant labeled samples and used to test only a few samples. However, classification tasks are diverse, and not all tasks have enough labeled samples for training. In this paper, a novel point cloud classification method for indoor components using few labeled samples is proposed to solve the problem of the requirement for abundant labeled samples for training with deep learning classification methods. This method is composed of four parts: mixing samples, feature extraction, dimensionality reduction, and semantic classification. First, the few labeled point clouds are mixed with unlabeled point clouds. Next, the mixed high-dimensional features are extracted using a deep learning framework. Subsequently, a nonlinear manifold learning method is used to embed the mixed features into a low-dimensional space. Finally, the few labeled point clouds in each cluster are identified, and semantic labels are provided for unlabeled point clouds in the same cluster by a neighborhood search strategy. The validity and versatility of the proposed method were validated by different experiments and compared with three state-of-the-art deep learning methods. Our method uses fewer than 30 labeled point clouds to achieve an accuracy that is 1.89-19.67% greater than existing methods. More importantly, the experimental results suggest that this method is not only suitable for single-attribute indoor scenarios but also for comprehensive complex indoor scenarios. © 2020 by the authors."
"54793415900;56242287700;6602577491;35197884700;7005628166;8853393600;","Classification of clouds sampled at the puy de Dôme station (France) based on chemical measurements and air mass history matrices",2020,"10.3390/atmos11070732","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088091387&doi=10.3390%2fatmos11070732&partnerID=40&md5=5fb1edb5f1db02c23585d491b16d6c0b","A statistical analysis of 295 cloud samples collected at the Puy de Dôme station in France (PUY), covering the period 2001-2018, was conducted using principal component analysis (PCA), agglomerative hierarchical clustering (AHC), and partial least squares (PLS) regression. Our model classified the cloud water samples on the basis of their chemical concentrations and of the dynamical history of their air masses estimated with back-trajectory calculations. The statistical analysis split our dataset into two sets, i.e., the first set characterized by westerly air masses and marine characteristics, with high concentrations of sea salts and the second set having air masses originating from the northeastern sector and the ""continental"" zone, with high concentrations of potentially anthropogenic ions. It appears from our dataset that the influence of cloud microphysics remains minor at PUY as compared with the impact of the air mass history, i.e., physicochemical processes, such as multiphase reactivity. © 2020 by the authors."
"55809206100;57211318296;57193920957;55843650400;7401526171;7005052907;","Bias correction of satellite-based precipitation estimations using quantile mapping approach in different climate regions of Iran",2020,"10.3390/rs12132102","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087546344&doi=10.3390%2frs12132102&partnerID=40&md5=e68f6759cae9889bfc87f5e6cb98be28","High-resolution real-time satellite-based precipitation estimation datasets can play a more essential role in flood forecasting and risk analysis of infrastructures. This is particularly true for extended deserts or mountainous areas with sparse rain gauges like Iran. However, there are discrepancies between these satellite-based estimations and ground measurements, and it is necessary to apply adjustment methods to reduce systematic bias in these products. In this study, we apply a quantile mapping method with gauge information to reduce the systematic error of the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS). Due to the availability and quality of the ground-based measurements, we divide Iran into seven climate regions to increase the sample size for generating cumulative probability distributions within each region. The cumulative distribution functions (CDFs) are then employed with a quantile mapping 0.6° x 0.6° filter to adjust the values of PERSIANN-CCS. We use eight years (2009-2016) of historical data to calibrate our method, generating nonparametric cumulative distribution functions of ground-based measurements and satellite estimations for each climate region, as well as two years (2017-2018) of additional data to validate our approach. The results show that the bias correction approach improves PERSIANN-CCS data at aggregated to monthly, seasonal and annual scales for both the calibration and validation periods. The areal average of the annual bias and annual root mean square errors are reduced by 98% and 56% during the calibration and validation periods, respectively. Furthermore, the averages of the bias and root mean square error of the monthly time series decrease by 96% and 26% during the calibration and validation periods, respectively. There are some limitations in bias correction in the Southern region of the Caspian Sea because of shortcomings of the satellite-based products in recognizing orographic clouds. © 2020 by the authors. Licensee MDPI, Basel, Switzerland."
"57193788172;57209398568;57206924573;6506051565;7004171611;36622868000;55914904100;55466977400;7102689523;57217728673;8525147900;","Cirrus cloud identification from airborne far-infrared and mid-infrared spectra",2020,"10.3390/rs12132097","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087543275&doi=10.3390%2frs12132097&partnerID=40&md5=676968cfa40c35c7e83f55bda62a3c94","Airborne interferometric data, obtained from the Cirrus Coupled Cloud-Radiation Experiment (CIRCCREX) and from the PiknMix-F field campaign, are used to test the ability of a machine learning cloud identification and classification algorithm (CIC). Data comprise a set of spectral radiances measured by the Tropospheric Airborne Fourier Transform Spectrometer (TAFTS) and the Airborne Research Interferometer Evaluation System (ARIES). Co-located measurements of the two sensors allow observations of the upwelling radiance for clear and cloudy conditions across the far-and mid-infrared part of the spectrum. Theoretical sensitivity studies show that the performance of the CIC algorithm improves with cloud altitude. These tests also suggest that, for conditions encompassing those sampled by the flight campaigns, the additional information contained within the far-infrared improves the algorithm's performance compared to using mid-infrared data only. When the CIC is applied to the airborne radiance measurements, the classification performance of the algorithm is very high. However, in this case, the limited temporal and spatial variability in the measured spectra results in a less obvious advantage being apparent when using both mid-and far-infrared radiances compared to using mid-infrared information only. These results suggest that the CIC algorithm will be a useful addition to existing cloud classification tools but that further analyses of nadir radiance observations spanning the infrared and sampling a wider range of atmospheric and cloud conditions are required to fully probe its capabilities. This will be realised with the launch of the Far-infrared Outgoing Radiation Understanding and Monitoring (FORUM) mission, ESA's 9th Earth Explorer. © 2020 by the authors. Licensee MDPI, Basel, Switzerland."
"57188848315;35098748100;57190214513;51461664100;7403564495;36678944300;35099345700;56939103900;56018934800;57212478545;57196548688;57214924430;","Spatiotemporal distributions of cloud radiative forcing and response to cloud parameters over the Mongolian Plateau during 2003–2017",2020,"10.1002/joc.6444","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076733242&doi=10.1002%2fjoc.6444&partnerID=40&md5=4c32d0419c05086969e4004cacd1f0c1","The Mongolian Plateau (MP) is among the most sensitive areas to global climate change, and the clouds over the MP have a greater impact on regional and global radiation budgets by altering the atmospheric and surface radiative forcing. In this study, daily Cloud and Earth Radiation Energy System data are used to investigate spatiotemporal variation of cloud radiative forcing (CRF) at the top of atmosphere (TOA), surface and atmosphere over the MP from 2003 to 2017 and then combined with Moderate Resolution Imaging Spectroradiometer level 2 atmospheric data during the same period to analyse the cloud parameter impacts on CRF over the MP. At the TOA and surface, net radiative forcing (NRF) and shortwave radiative forcing (SRF) have cooling effects and longwave radiative forcing (LRF) have heating effects in all four seasons, and the NRF cooling effect in most areas of the MP decreases in summer and autumn and increases in spring and winter. In the atmosphere, SRF in spring and summer and NRF in summer reach larger values and heat the atmosphere, and LRF plays a strong cooling role in winter. The NRF change trend in the atmosphere over Mongolia is noteworthy in spring, its reduction slope is large, and most areas of Mongolia passed a significance test. As expected, a significant negative correlation was observed between cloud cover and NRF (as well as SRF) at the TOA and surface and a positive correlation was observed with NRF/SRF in the atmosphere and all LRF. With the increase in cloud optical thickness and cloud water path, the NRF and SRF cooling effects at the TOA and surface, the LRF cooling effect in the atmosphere, the LRF heating effect at the surface, and the SRF heating effect in the atmosphere all become stronger. © 2019 Royal Meteorological Society"
"57217145907;55915265200;23003334700;6701724174;","Assessing cloud segmentation in the chromacity diagram of all-sky images",2020,"10.3390/rs12111902","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086448215&doi=10.3390%2frs12111902&partnerID=40&md5=5584952e685f5a9893cfe4467af52c5a","All-sky imaging systems are currently very popular. They are used in ground-based meteorological stations and as a crucial part of the weather monitors for autonomous robotic telescopes. Data from all-sky imaging cameras provide important information for controlling meteorological stations and telescopes, and they have specific characteristics different from widely-used imaging systems. A particularly promising and useful application of all-sky cameras is for remote sensing of cloud cover. Post-processing of the image data obtained from all-sky imaging cameras for automatic cloud detection and for cloud classification is a very demanding task. Accurate and rapid cloud detection can provide a good way to forecast weather events such as torrential rainfalls. However, the algorithms that are used must be specifically calibrated on data from the all-sky camera in order to set up an automatic cloud detection system. This paper presents an assessment of a modified k-means++ color-based segmentation algorithm specifically adjusted to the WILLIAM (WIde-field aLL-sky Image Analyzing Monitoring system) ground-based remote all-sky imaging system for cloud detection. The segmentation method is assessed in two different color-spaces (L*a*b and XYZ). Moreover, the proposed algorithm is tested on our public WMD database (WILLIAM Meteo Database) of annotated all-sky image data, which was created specifically for testing purposes. The WMD database is available for public use. In this paper, we present a comparison of selected color-spaces and assess their suitability for the cloud color segmentation based on all-sky images. In addition, we investigate the distribution of the segmented cloud phenomena present on the all-sky images based on the color-spaces channels. In the last part of this work, we propose and discuss the possible exploitation of the color-based k-means++ segmentation method as a preprocessing step towards cloud classification in all-sky images. © 2020 by the authors."
"57216950962;6701518904;","The vertical structure and spatial variability of lower-tropospheric water vapor and clouds in the trades",2020,"10.5194/acp-20-6129-2020","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085495551&doi=10.5194%2facp-20-6129-2020&partnerID=40&md5=a36e22f740023a2ccf9b2cbff1457a55","Horizontal and vertical variability of water vapor is omnipresent in the tropics, but its interaction with cloudiness poses challenges for weather and climate models. In this study we compare airborne lidar measurements from a summer and a winter field campaign in the tropical Atlantic with high-resolution simulations to analyze the water vapor distributions in the trade wind regime, its covariation with cloudiness, and their representation in simulations. Across model grid spacing from 300m to 2.5km, the simulations show good skill in reproducing the water vapor distribution in the trades as measured by the lidar. An exception to this is a pronounced moist model bias at the top of the shallow cumulus layer in the dry winter season which is accompanied by a humidity gradient that is too weak at the inversion near the cloud top. The model's underestimation of water vapor variability in the cloud and subcloud layer occurs in both seasons but is less pronounced than the moist model bias at the inversion. Despite the model's insensitivity to resolution from hecto- to kilometer scale for the distribution of water vapor, cloud fraction decreases strongly with increasing model resolution and is not converged at hectometer grid spacing. The observed cloud deepening with increasing water vapor path is captured well across model resolution, but the concurrent transition from cloud-free to low cloud fraction is better represented at hectometer resolution. In particular, in the wet summer season the simulations with kilometer-scale resolution overestimate the observed cloud fraction near the inversion but lack condensate near the observed cloud base. This illustrates how a model's ability to properly capture the water vapor distribution does not necessarily translate into an adequate representation of shallow cumulus clouds that live at the tail of the water vapor distribution. © 2020 Copernicus GmbH. All rights reserved."
"6701455548;8247122100;57219131778;57219131345;","Vertical structure of radiative heating rates of the MJO during DYNAMO",2020,"10.1175/JCLI-D-19-0519.1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091401883&doi=10.1175%2fJCLI-D-19-0519.1&partnerID=40&md5=ba62787ef73dce3d3f847aaad0eaa7b4","The vertical structure of radiative heating rates over the region of the tropical Indian Ocean associated with the MJO during the DYNAMO/ARM MJO Investigation Experiment is presented. The mean and variability of heating rates during active, suppressed, and disturbed phases are determined from the Pacific Northwest National Laboratory Combined Remote Sensing Retrieval (CombRet) from Gan Island, Maldives (0.698S, 73.158E). TOA and surface fluxes from the CombRet product are compared with collocated 3-hourly CERES SYN1deg Ed4A satellite retrievals. The fluxes are correlated in time with correlation coefficients around 0.9, yet CombRet time-mean OLR is 15 W m22 larger. Previous work has suggested that CombRet undersamples high clouds, due to signal attenuation by low-level clouds and reduced instrument sensitivity with altitude. However, mean OLR differs between CombRet and CERES for all values of OLR, not just the lowest values corresponding to widespread high clouds. The discrepancy peaks for midrange OLR, suggestive of precipitating, towering cumulus convective clouds, rather than stratiform cirrus clouds. Low biases in the cloud-top height of thick clouds substantially contribute to the overestimate of OLR by CombRet. CombRet data are used to generate composite shortwave and longwave atmospheric heating rate profiles as a function of the local OLR. Although there is considerable variability in CombRet not directly related to OLR, the time–height structure of mean heating rate composites generated using OLR as the interpolant is broadly representative of tropical convective variability on intraseasonal time scales. © 2020 American Meteorological Society."
"57216948094;42862769000;7404148828;57216946296;57191529930;57199698044;57216949704;","Use of Double Channel Differences for Reducing the Surface Emissivity Dependence of Microwave Atmospheric Temperature and Humidity Retrievals",2020,"10.1029/2019EA000854","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085496930&doi=10.1029%2f2019EA000854&partnerID=40&md5=ec4899fb127168ee9433afe7e1c8bc60","Surface emissivity has a significant impact on atmospheric parameter retrievals from microwave sounding instruments. To reduce the dependence of retrievals on surface emissivity, a double channel differences equation is deduced, and a corresponding retrieval scheme is constructed. Retrieval experiments are performed using Advanced Microwave Sounding Unit-A (AMSU-A) and Microwave Humidity Sounder (MHS) simulations and global measurements. Simulation experiments show that the double channel differences scheme can reduce the root mean square errors (RMSE) of the temperature and humidity profiles in the middle and lower atmosphere. Retrieval experiments based on AMSU-A and MHS global measurements show that the proposed scheme can significantly reduce the RMSE of temperature profiles in the lower atmosphere and humidity profiles in the middle and lower atmosphere for cloudy and cloudless conditions, different surface types, and different scan angles, with maximum reduction values of 0.64 K and 9.03%, respectively. Regarding RMSE improvement, that of the cloudy condition is greater than that of the cloudless condition, that of the land is greater than that of the coast and the sea, and there is no significant dependence on the scan angles. The double channel differences scheme is very sensitive to initial near-surface temperatures. Reducing the initial near-surface temperature error can significantly improve the temperature retrieval accuracy below 900 hPa, with maximum reduction value of 3.25 K. ©2020. The Authors."
"54420868300;36598934900;57209538807;","Thunderstorm cloud-type classification from space-based lightning imagers",2020,"10.1175/MWR-D-19-0365.1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084935687&doi=10.1175%2fMWR-D-19-0365.1&partnerID=40&md5=bf0fe2c75f1ffccf94faf31883bb7dc7","The organization and structure of thunderstorms determines the extent and severity of their hazards to the general public and their consequences for the Earth system. Distinguishing vigorous convective regions that produce heavy rain and hail from adjacent regions of stratiform clouds or overhanging anvil clouds that produce light to no rainfall is valuable in operations and physical research. Cloud-type algorithms that partition convection from stratiform regions have been developed for space-based radar, passive microwave, and now Geostationary Operational Environmental Satellites (GOES) Advanced Baseline Imager (ABI) multispectral products. However, there are limitations for each of these products including temporal availability, spatial coverage, and the degree to which they based on cloud microphysics. We have developed a cloud-type algorithm for GOES Geostationary Lightning Mapper (GLM) observations that identifies convective/nonconvective regions in thunderstorms based on signatures of interactions with nonconvective charge structures in the lightning flash data. The GLM sensor permits a rapid (20 s) update cycle over the combined GOES-16-GOES-17 domain across all hours of the day. Storm regions that do not produce lightning will not be classified by our algorithm, however. The GLM cloud-type product is intended to provide situational awareness of electrified nonconvective clouds and to complement other cloud-type retrievals by providing a contemporary assessment tied to lightning physics. We propose that a future combined ABI-GLM cloud-type algorithm would be a valuable product that could draw from the strengths of each instrument and approach. © 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses)."
"57189992504;17433789100;8705440100;22946301100;","Variability in cirrus cloud properties using a PollyXT Raman lidar over high and tropical latitudes",2020,"10.5194/acp-20-4427-2020","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083766605&doi=10.5194%2facp-20-4427-2020&partnerID=40&md5=e137258e909bdacb99f3b763d3fec587","Measurements of geometrical and optical properties of cirrus clouds, performed with a multi-wavelength PollyXT Raman lidar during the period 2008 to 2016, are analysed. The measurements were performed with the same instrument, during sequential periods, in three places at different latitudes, Gwal Pahari (28.43-N, 77.15-E; 243ma.s.l.) in India, Elandsfontein (26.25-S, 29.43-E; 1745ma.s.l.) in South Africa and Kuopio (62.74-N, 27.54-E; 190ma.s.l.) in Finland. The lidar dataset was processed by an automatic cirrus cloud masking algorithm, developed in the frame of this work. In the following, we present a statistical analysis of the lidar-retrieved geometrical characteristics (cloud boundaries, geometrical thickness) and optical properties of cirrus clouds (cloud optical depth, lidar ratio, ice crystal depolarisation ratio) measured over the three areas that correspond to subtropical and subarctic regions as well as their seasonal variability. The effect of multiple scattering from ice particles to the derived optical products is also considered and corrected in this study. Our results show that cirrus layers, which have a noticeable monthly variability, were observed between 6.5 and 13 km, with temperatures ranging from ° 72 to ° 27-C. The observed differences on cirrus clouds geometrical and optical properties over the three regions are discussed in terms of latitudinal and temperature dependence. The latitudinal dependence of the geometrical properties is consistent with satellite observations, following the pattern observed with CloudSat, with decreasing values towards the poles. The geometrical boundaries have their highest values in the subtropical regions, and overall, our results seem to demonstrate that subarctic cirrus clouds are colder, lower and optically thinner than subtropical cirrus clouds. The dependence of cirrus cloud geometrical thickness and optical properties on mid-cirrus temperatures shows a quite similar tendency for the three sites but less variability for the subarctic dataset. Cirrus clouds are geometrically and optically thicker at temperatures between ° 45 and ° 35-C, and a second peak is observed at lower temperatures-° 70-C for the subarctic site. Lidar ratio values also exhibit a pattern, showing higher values moving toward the poles, with higher mean values observed over the subarctic site. The dependency of the mid-cirrus temperatures on the lidar ratio values and the particle depolarisation values is further examined. Our study shows that the highest values of the cirrus lidar ratio correspond to higher values of cirrus depolarisation and warmer cirrus. The kind of information presented here can be rather useful in the cirrus parameterisations required as input to radiative transfer models and can be a complementary tool for satellite products that cannot provide cloud vertical structure. In addition, ground-based statistics of the cirrus properties could be useful in the validation and improvement of the corresponding derived products from satellite retrievals. © Author(s) 2020. This work is distributed under the Creative Commons Attribution 4.0 License."
"56188627800;57201012625;56068624000;54402367600;56897622400;","Ground-Based Cloud Classification Using Task-Based Graph Convolutional Network",2020,"10.1029/2020GL087338","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081747502&doi=10.1029%2f2020GL087338&partnerID=40&md5=21c7de0920afe21db2c94e6ef225df5f","Clouds play a significant role in weather forecasts, water cycle, and climate system. However, existing methods ignore the relations of ground-based cloud images. In this letter, we propose a novel method named task-based graph convolutional network (TGCN) for ground-based cloud classification, which takes image relations into consideration. To this end, we construct the graph using convolutional neural network-based features of ground-based cloud images which are learned in a supervised manner, and incorporate the graph computation into TGCN. Given that existing ground-based cloud databases are with limited labeled training images and categorized according to different classification criteria, we release the largest ground-based remote sensing cloud database (GRSCD) to provide a comparative study for different methods and to further improve the study of regional sky conditions. The experimental results on GRSCD manifest the effectiveness of TGCN for ground-based cloud classification. ©2020. American Geophysical Union. All Rights Reserved."
"57215896236;21835177200;57213552867;","An improved convolution neural network-based model for classifying foliage and woody components from terrestrial laser scanning data",2020,"10.3390/rs12061010","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082300969&doi=10.3390%2frs12061010&partnerID=40&md5=a09048254f8217b65ab644b072c5f141","Separating foliage and woody components can effectively improve the accuracy of simulating the forest eco-hydrological processes. It is still challenging to use deep learning models to classify canopy components from the point cloud data collected in forests by terrestrial laser scanning (TLS). In this study, we developed a convolution neural network (CNN)-based model to separate foliage and woody components (FWCNN) by combing the geometrical and laser return intensity (LRI) information of local point sets in TLS datasets. Meanwhile, we corrected the LRI information and proposed a contribution score evaluation method to objectively determine hyper-parameters (learning rate, batch size, and validation split rate) in the FWCNN model. Our results show that: (1) Correcting the LRI information could improve the overall classification accuracy (OA) of foliage and woody points in tested broadleaf (from 95.05% to 96.20%) and coniferous (from 93.46% to 94.98%) TLS datasets (Kappa ≥ 0.86). (2) Optimizing hyper-parameters was essential to enhance the running efficiency of the FWCNN model, and the determined hyper-parameter set was suitable to classify all tested TLS data. (3) The FWCNN model has great potential to classify TLS data in mixed forests with OA > 84.26% (Kappa ≥ 0.67). This work provides a foundation for retrieving the structural features of woody materials within the forest canopy. © 2020 by the authors. Licensee MDPI, Basel, Switzerland."
"56420772600;7403045983;","A Discriminative Tensor Representation Model for Feature Extraction and Classification of Multispectral LiDAR Data",2020,"10.1109/TGRS.2019.2947081","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85080903070&doi=10.1109%2fTGRS.2019.2947081&partnerID=40&md5=edc1d6da7a5a77fc8c692fe9eb7dd351","Multispectral light detection and ranging (MS-LiDAR) systems open the door to the possibility in the 3-D land cover classification at a finer scale using only point cloud data. This article proposes a model based on the tensor representation for multispectral point cloud classification. The proposed method combines the 3-D local spatial structure of each multispectral point by characterizing the point with a second-order tensor. The first mode of the tensor indicates the spatial location and spectral information of each point (i.e., the row of the second-order tensor) and the second mode denotes the neighborhood geometric and spectral structures (i.e., the column of the second-order tensor). Then we develop a novel tensor manifold discriminant embedding (TMDE) algorithm to extract the geometric-spectral features for multispectral point clouds classification. TMDE solves the mapping matrices of each mode by preserving the intraclass samples' distribution further making it more compact and maximizing the distance of different classes. Finally, the support vector machine classifier with the extracted features as input is used to implement the classification of multispectral point clouds. Experiments are conducted on two real multispectral point cloud data sets. The experimental results demonstrate that the proposed method can achieve significant improvements in classification accuracies in comparison with several state-of-the-art algorithms. © 1980-2012 IEEE."
"37078354100;55581675600;35227762400;55326237100;6602600408;","A new classification of satellite-derived liquid water cloud regimes at cloud scale",2020,"10.5194/acp-20-2407-2020","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85080876424&doi=10.5194%2facp-20-2407-2020&partnerID=40&md5=fed3dc63cd0f83ba03d542cc6d510a29","Clouds are highly variable in time and space, affecting climate sensitivity and climate change. To study and distinguish the different influences of clouds on the climate system, it is useful to separate clouds into individual cloud regimes. In this work we present a new cloud classification for liquid water clouds at cloud scale defined using cloud parameters retrieved from combined satellite measurements from CloudSat and CALIPSO. The idea is that cloud heterogeneity is a measure that allows us to distinguish cumuliform and stratiform clouds, and cloud-base height is a measure to distinguish cloud altitude. The approach makes use of a newly developed cloud-base height retrieval. Using three cloud-base height intervals and two intervals of cloud-top variability as an inhomogeneity parameter provides six new liquid cloud classes. The results show a smooth transition between marine and continental clouds as well as between stratiform and cumuliform clouds in different latitudes at the high spatial resolution of about 20km. Analysing the micro- and macrophysical cloud parameters from collocated combined MODIS, CloudSat and CALIPSO retrievals shows distinct characteristics for each cloud regime that are in agreement with expectation and literature. This demonstrates the usefulness of the classification. © 2020 Copernicus GmbH. All rights reserved."
"7102743829;55405340400;","Formulation of autoconversion and drop spectra shape in shallow cumulus clouds",2020,"10.1175/JAS-D-19-0134.1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082883568&doi=10.1175%2fJAS-D-19-0134.1&partnerID=40&md5=79a635c74211cf16b660617049f65c09","Two-moment autoconversion parameterizations as compared to accretion parameterizations exhibit significant errors suggesting that additional moments are needed to increase their accuracy. We develop a three-moment autoconversion parameterization using output from an LES model with size-resolved microphysics. Adding the third moment decreases the errors of parameterization and improves precipitation prediction. However, the errors are still significantly larger than errors of accretion rate. An analysis of the cloud drop size distributions (DSDs) in the simulated tropical convective cloud system reveals that most DSDs have a significant fraction of cloud liquid water content qc in the midsize droplet range (radii from 20 to 40 mm). Our data indicate that more than 30% of DSDs have over half of qc contained in the midsize range and about 60% of spectra have, at least, one-third of qc in this range. Even when the rain/drizzle mode is small (radar reflectivity Z, 210 dBZ), there is a significant number of spectra in which fraction of qc in the midsize range is as large as 60%. These DSDs are more complex than the frequently used gamma or lognormal distributions, which exhibit a single mode and can be defined by three microphysical moments. The need to define DSDs by more than three moments explains the large errors in the three-moment autoconversion parameterization. The limitation of three-parameter gamma or lognormal distributions should be kept in mind when applying them in precipitating shallow cumulus clouds. © 2020 American Meteorological Society."
"57205769771;15845811500;9243680600;57203711294;56239390400;57218911958;57215049751;","Evaluation of Convolution Operation Based on the Interpretation of Deep Learning on 3-D Point Cloud",2020,"10.1109/JSTARS.2020.3020321","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091766961&doi=10.1109%2fJSTARS.2020.3020321&partnerID=40&md5=844052b03a6599aa426149474603e80c","The interpretation of deep learning network is an important part in understanding the convolutional neural networks (CNNs). As an exploratory research, this article explored the interpretation method in 3-D point cloud deep learning networks, for the purpose of evaluating the performance of convolution functions in 3-D point cloud CNNs. Specifically, a 3-D point cloud classification network with two branches is used as the interpretation network in two aspects; 1) information entropy is introduced to diagnose the internal representation in the middle layer of CNN; and 2) the external consistency of convolution function is measured by per-point classification accuracy with class activation mapping technique. Four typical convolution functions are tested by the interpretation network on ModelNet40 dataset and the experimental results demonstrate that the proposed evaluation method is reliable. Feature transformation ability and feature recognition ability of convolution functions are extracted by visualization evaluation and proposed measurable metrics evaluation. © 2008-2012 IEEE."
[No author id available],"Cirrus and altocumulus lenticularis clouds provide a photogenic backdrop to the Perito Moreno Glacier",2020,"10.1002/wea.3706","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086245272&doi=10.1002%2fwea.3706&partnerID=40&md5=33468bea5717d0075a10c9f33bf38d40",[No abstract available]
"56441633200;36026763100;15055274800;56677025300;22958039600;","Learning Set Representations for LWIR In-Scene Atmospheric Compensation",2020,"10.1109/JSTARS.2020.2980750","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084281173&doi=10.1109%2fJSTARS.2020.2980750&partnerID=40&md5=c19cf0e60f3c456895a1374d2b9e7604","Atmospheric compensation of long-wave infrared (LWIR) hyperspectral imagery is investigated in this article using set representations learned by a neural network. This approach relies on synthetic at-sensor radiance data derived from collected radiosondes and a diverse database of measured emissivity spectra sampled at a range of surface temperatures. The network loss function relies on LWIR radiative transfer equations to update model parameters. Atmospheric predictions are made on a set of diverse pixels extracted from the scene, without knowledge of blackbody pixels or pixel temperatures. The network architecture utilizes permutation-invariant layers to predict a set representation, similar to the work performed in point cloud classification. When applied to collected hyperspectral image data, this method shows comparable performance to Fast Line-of-Sight Atmospheric Analysis of Hypercubes-Infrared (FLAASH-IR), using an automated pixel selection approach. Additionally, inference time is significantly reduced compared to FLAASH-IR with predictions made on average in 0.24 s on a 128 pixel by 5000 pixel data cube using a mobile graphics card. This computational speed-up on a low-power platform results in an autonomous atmospheric compensation method effective for real-time, onboard use, while only requiring a diversity of materials in the scene. © 2008-2012 IEEE."
"24832229000;56295385800;55724964400;57194545555;56346609600;55986579100;","Multi-view features joint learning with label and local distribution consistency for point cloud classification",2020,"10.3390/RS12010135","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083975260&doi=10.3390%2fRS12010135&partnerID=40&md5=0df84c3554415a3790ee1e43ad907d58","In outdoor Light Detection and Ranging (lidar)point cloud classification, finding the discriminative features for point cloud perception and scene understanding represents one of the great challenges. The features derived from defect-laden (i.e., noise, outliers, occlusions and irregularities) and raw outdoor LiDAR scans usually contain redundant and irrelevant information which adversely aects the accuracy of point semantic labeling. Moreover, point cloud features of dierent views have a capability to express dierent attributes of the same point. The simplest way of concatenating these features of dierent views cannot guarantee the applicability and eectiveness of the fused features. To solve these problems and achieve outdoor point cloud classification with fewer training samples, we propose a novel multi-view features and classifiers' joint learning framework. The proposed framework uses label consistency and local distribution consistency of multi-space constraints for multi-view point cloud features extraction and classification. In the framework, the manifold learning is used to carry out subspace joint learning of multi-view features by introducing three kinds of constraints, i.e., local distribution consistency of feature space and position space, label consistency among multi-view predicted labels and ground truth, and label consistency among multi-view predicted labels. The proposed model can be well trained by fewer training points, and an iterative algorithm is used to solve the joint optimization of multi-view feature projection matrices and linear classifiers. Subsequently, the multi-view features are fused and used for point cloud classification eectively. We evaluate the proposed method on five dierent point cloud scenes and experimental results demonstrate that the classification performance of the proposed method is at par or outperforms the compared algorithms. © 2020 by the authors."
"56390829600;57213521610;56204562000;57198674858;57205488783;7406200372;","Aerosol and Cloud Classifications Derived from MAX-DOAS Measurements in Urban North China and their Comparisons to Multiple Remote Sensing Datasets",2019,"10.1109/ICMO49322.2019.9026051","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082946803&doi=10.1109%2fICMO49322.2019.9026051&partnerID=40&md5=2dfbbea16567621d0b14e69c4ebfa293","Multi-Axis Differential Optical Absorption Spectroscopy, MAX-DOAS for short, is a thriving ground-based passive remote sensing technique, which retrieves the vertical characteristics of aerosol and trace gases in the lower atmosphere using scattered sunlight measured from different axis angles. Clouds have obvious influence on atmospheric radiative transfer process and thus affect the inversion of vertical distribution, making it essential to study and classify the cloud properties. In this study, a cloud identification and classification algorithm script was developed based on several key quantities derived from MAX-DOAS observations, like radiance, color index and the absorption of oxygen dimer \text{O}-{\mathbf {4}} et al. The algorithm was applied to two-month's MAX-DOAS observations in southern urban Beijing ( 39.81 ^{\circ}\mathrm {N}, 116.47 ^{\circ}\mathrm {E}), the megacity in North China, in summer 2017. A cloud classification dataset was created with relatively high time resolution. Aerosol profiles, near surface aerosol extinction and AOD (aerosol optical density) were derived as well by applying PriAM methods of MPIC. The results were compared systematically to several remote sensing techniques, like MODIS, sun photometer and Millimeter wave cloud radar, which have rarely been done before. General consistency and good agreement were achieved under respective aerosol and cloud scenarios, assuring the reliability of the cloud identification and classification algorithm script and the dependable capability of MAX-DOAS to provide aerosol and cloud information. This further indicates that more thorough studies should be carried out to diminish the influence of aerosol and cloud and improve the retrieval accuracy of vertical column densities and profiles from MAX-DOAS in the future. © 2019 IEEE."
"55588318900;55363162900;7403436951;","Inversion and Preliminary Validation for Cloud Classification and Cloud Phase Products of Fengyun-3D in CMA-NSMC",2019,"10.1109/ICMO49322.2019.9026035","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082928606&doi=10.1109%2fICMO49322.2019.9026035&partnerID=40&md5=1ff58db315c001d23d5d4e1f4724e6ac","Fengyun-3D (thereafter referred as FY-3D) is the fourth operational satellite of the second generation polar orbiting meteorological satellite in China. The visible (0.58-0.68 μm), near infrared (1.55-1.64μm and 3.55-3.93 μm) and two long-wave infrared (10.3-11.3 μm and 11.5-12.5 μm) channels of the FY-3D MERSI (Medium Resolution Spectral Imager) instrument can be combined used to retrieve cloud classification and cloud phase products of FY-3D. Cloud classification and cloud phase of FY-3D are retrieved based on the MERSI L1 data and cloud mask product as well as a series of auxiliary data such as surface type and ice-snow identification. By using the spectral and texture characteristics of visible, infrared and near infrared channels data, threshold method is used to identify cloud phases of cloudy pixels, and 1 km resolution cloud phase product is obtained. Then, combined with the results of thin cirrus cloud and broken cloud identification, high, medium and low cloudy pixels are identified by threshold method and cloud classification product with 1 km resolution of FY-3D is obtained. Compared with MODIS cloud phase products, it can be concluded that the precision of cloud phase product is higher for simplestructured clouds, and slightly lower for complex clouds. In general, FY-3D has a better performance on identification of water clouds and a slightly lower accuracy for the ice phase. It should be noted that due to the different algorithm, the methods and criteria for identifying the mixed and uncertain phases are different, which results in some differences between FY-3D and MODIS cloud phase outputs. © 2019 IEEE."
"35299838900;","Calculation of the Natural Illumination of the Earth’s Surface at Different States of Cloud Cover",2019,"10.1134/S0001433819110100","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081742967&doi=10.1134%2fS0001433819110100&partnerID=40&md5=8ad76f5e47f2e0c0cb231db1119b5c78","Abstract: An analytical expression which approximates experimental data on the natural daily illumination of the Earth’s surface in a wide (0–90°) range of the angular height of the Sun is suggested. The coefficients of this expression for the direct, scattered, and total components of natural light at different states of the Earth’s atmosphere and underlying surface are determined. The relationship between the solar angular height in the period from sunrise to sunset and the local time at an arbitrary reference point on the Earth’s surface is defined in the equatorial geocentric coordinates at arbitrary latitude and day of the year. Analytical expressions of the dependence of the direct, scattered, and total components of the natural illumination of the Earth’s surface on the local time for any day of the year and latitude are given. The results can be used in studies of unconscious responses of a human body to diurnal variations in the solar radiation characteristics, as well as in studies of visual perception. © 2019, Pleiades Publishing, Ltd."
"57213189669;53163349900;7005804830;57212251635;","Quantifying the Contribution of Tropical Cyclones to the Earth's Outgoing Radiation",2019,"10.1109/IGARSS.2019.8898504","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077721806&doi=10.1109%2fIGARSS.2019.8898504&partnerID=40&md5=7fa4a14e18a00d4b84121fbc68b7677f","This study aims to quantify the portion of the Earth's outgoing radiation that is attributable to tropical cyclones (TCs). To accomplish this, we have developed a method that starts with an image processing algorithm which labels cloud pixels associated with a TC, based on the time series of brightness temperature images and best-track data. The labels attributable to the TC are then combined with radiation data to obtain the TC-related radiation throughout its lifetime. Preliminary results are shown for the North Atlantic Ocean in 2012 and 2013: In 2012, the average TC shortwave and longwave radiation contributed 0.039 PW (or 0.35%) and 0.099 PW (or 0.34%), respectively, to the total regional radiation; In 2013, the contribution due tot TCs decreased to 0.022 PW (or 0.19%) for SW, and 0.059 PW (or 0.20%) for LW radiation. © 2019 IEEE."
"57213925208;56181559400;6602211600;","Extraction of Multi-Scale Geometric Features for Point Cloud Classification",2019,"10.1109/IGARSS.2019.8898547","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077716425&doi=10.1109%2fIGARSS.2019.8898547&partnerID=40&md5=3628ddc6f84f5e091f4baaa4b9bfb0b1","Light Detection and Ranging (LiDAR) techniques is an efficient way of obtaining 3D information of complex urban scenes. However, automatically and efficiently interpreting acquired 3D points is still a challenging task. For achieving an excellent semantic interpretation of point clouds, the extraction of distinctive and reliable geometric features often plays a vital role. In this paper, we propose a method generating features from the local vicinity of different sizes and combine them for a better feature representation. To evaluate the proposed method, experiments were conducted using Li-DAR point cloud dataset and compared with that using single scale feature extraction methods. © 2019 IEEE."
"6701360428;","Variability of Cloud Parameters from Satellite Data",2019,"10.3103/S1068373919070033","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85070230974&doi=10.3103%2fS1068373919070033&partnerID=40&md5=5e1c6f8b6fbd77cab506b97c0930ef1f","A method for analyzing the variability of characteristics of different cloud types is developed using the results of cloud classification and thematic processing of satellite data. To increase the efficiency of the proposed approach, 16 cloud types were identified during the periods of snow cover absence, and 12 cloud types, in its presence; parallel computation methods on the general-purpose graphic processor units were used. The results of studying the frequency of various cloud types and variations in their parameters over the latitude zones of 50°–60° and 60°–70° N in the Tomsk oblast in 2017 are analyzed. The episodes with the deviation from the annual course are considered for a number of characteristics of several cloud types. © 2019, Allerton Press, Inc."
"57201567647;55720362700;15836110700;57193237418;","Observed characteristics change of tropical cyclones during rapid intensification over western north pacific using cloudsat data",2019,"10.1109/JSTARS.2019.2917091","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85069452088&doi=10.1109%2fJSTARS.2019.2917091&partnerID=40&md5=1d1d821b17a064ada4eab8085ebeee69","Rapid intensification (RI) progress is the main challenge that precludes the improvement of tropical cyclone (TC) intensity estimates. In this paper, a composite study of the observable characteristics of 11-year TCs undergoing RI and non-RI in the Western North Pacific were conducted using CloudSat tropical cyclone (CSTC) dataset. 2B-GEOPROF, 2B-CLDCLASS, and 2B-CWC-RO products in the CSTC dataset were used to construct radar reflectivity, cloud class, and cloud water-content distributions for each category, respectively. The results show that radar echo statistics have an arc-like contour profile for all five categories, with two distinct modes of reflectivity distributions separated by the melting layer. RI has the highest frequency expanding through the whole reflectivity range along with the broadest coverage in the upper branch, and a 'continuity' of reflectivity distribution from-10 to 10 dBZ at 6-11 km could be a vital indicator of TC intensification. A sharp slope can be observed in the lower branch due to heavy precipitation attenuation. Vertical distributions of cloud types show that all categories have similar cloud compositions, and deep convective clouds are apparently to play an important role in maintaining the TC intensification. Liquid water content (LWC) curve exhibits a bimodal distribution with two peaks at around 1.5 and 5 km, while ice water content (IWC) curve is much smoother and has one peak at around 9 km. It is suggested that TCs with larger LWC around freezing level or/and larger IWC at upper level tend to intensify within the next 24 hours. © 2008-2012 IEEE."
"55778936000;56826035600;57209022506;56909776700;57209028653;57201136250;6506609958;","Design a Web Platform to manage environmental monitoring information to be used in multicriteria evaluations of Green Infrastructures",2019,"10.1088/1755-1315/275/1/012005","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85066300675&doi=10.1088%2f1755-1315%2f275%2f1%2f012005&partnerID=40&md5=3fdf201bdb031987dd20d2dfbd9dec17","The aim of the WEQUAL project ""WEb service centre for QUALity multidimensional design and tele-operated monitoring of Green Infrastructures"") is the development of a system able to support a quick environmental monitoring of watercourses where new hydraulic structures are intended to be built, encouraging the identification of design solutions supporting the diffusion of Green Infrastructures (GI). The WEQUAL's idea is to organize a service centre where users (designer, technician, researcher) can access the service through a personal account and carry out an assessment of alternative GI projects. Through some automatic procedures, accessible via a suitable web platform, a set of algorithms may be applied to process raw data collected by an UAV (Unmanned Aircraft Vehicle) equipped with a 3D Lidar, multispectral camera and RGB camera, for the purpose of calculating the WEQUI Index. The WEQUI index is used to assesses the eco-morphological status of the monitored watercourse, taking advantage from data related to NDVI index, Digital Terrain Model (DTM), Digital Surface Model (DSM) and a 3D point cloud classification. The computed value of the WEQUI index may be used to assess the eco-morphological quality at ex-ante and ex-post river stabilization or protection intervention. The platform will provide a shared environment integrating indices calculations and environmental-parameters assessment for multidimensional evaluations. © Published under licence by IOP Publishing Ltd."
"16678405100;35353151900;","A high-level cloud detection method utilizing the GOSAT TANSO-FTS water vapor saturated band",2019,"10.5194/amt-12-389-2019","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85060396260&doi=10.5194%2famt-12-389-2019&partnerID=40&md5=5551d6cd4c74d0b92d495a60b2fcc81a","A detection method for high-level clouds, such as ice clouds, is developed using the water vapor saturated channels of the solar reflected spectrum observed by the Greenhouse gases Observing SATellite (GOSAT) Thermal And Near-infrared Sensor for carbon Observation Fourier Transform Spectrometer (TANSO-FTS). The clouds detected by this method are optically relatively thin (0.01 or less) and located at high altitude. Approximately 85&thinsp;% of the results from this method for clouds with cloud-top altitude above 5&thinsp;km agree with the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) cloud classification. GOSAT has been operating since April 2009 with a 3-day repeat cycle for a pointwise geolocation pattern, providing a spectral data record that exceeds 9 years. Cloud information derived from GOSAT TANSO-FTS spectra could be powerful data for understanding the variability in cirrus cloud on temporal scales from synoptic to interannual. © Author(s) 2019."
"12808931100;56276992000;12809430200;57211811043;","Avhrr data for real-time operational flood forecasting in malaysia",2019,"10.1007/3-540-27468-5_93","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072046700&doi=10.1007%2f3-540-27468-5_93&partnerID=40&md5=bed41fde91c00176ccf06a0db6319b10","Flash floods strike quickly and in most cases without warning. They are usually observed before any warning can be issued and usually persons and property have been affected before the warning reaches them. Such are the conditions prevalent in Malaysia’s extreme monsoon weather that occasionally causes floods and results in the extensive damage to property and sometimes loss of lives. Over the years variously hydrological and structural engineering measures have been implemented for flood monitoring and forecasting. These measures have only yielded limited success as may be seen in the recurring flood situation. Yearly financial and property loss estimates have increased and an estimated cost of over 2.5 billion RM is projected for the year 2004 according to sources from the drainage and irrigation department of Malaysia. It has thus become apparent that Malaysia institutes an effective operational flood forecasting to arrest the persisting flood problem. In this paper we will expound on current flood management and forecasting system being implemented in the country, particularly the Klang Valley that includes Kuala Lumpur where there has been tremendous urban growth and development in the last one and half decades. The paper further discusses where current flood management systems have been lacking in the absence of real-time hydro-meteorological forecasts. Where as hydrodynamic simulations and structural control measures have been emphasized in many flood management systems in Malaysia, the integration of real-time hydro-meteorological forecasts have been conspicuously absent, rendering most in-situ flood forecasts and early warnings ineffective in address the flood problem in the country. Malaysia is a tropical country that lies along the path of the northeast and southwest monsoon. Although satellite image based NWP have proved useful for the tropical and equatorial regions of the world in flood forecasting, they have yet to be applied in Malaysia. Observations have generally shown heavy cumulonimbus clouds formation and thunderstorms precede the usual heavy monsoon rains that cause floods in the region. This makes quantitative precipitation forecast a must be input to any flood early warning design. Numerous empirical studies have determined that cloud top temperatures less that 235k in the tropics are generally expected to produce convective rainfall at the rate of 3mm/hr. In this study we thus investigate monsoon cloud formation that has the propensity to precipitate using NOAA-AVHRR data for real-time operational flood early warning in Malaysia. The AVHRR data has been preferred for its relatively high temporal resolution of at most 6/hours, its easy acquisition and cost effectiveness and its ability for automated geometric rectification when compared to GEOS and GMS data. Cloud cover and types are processed using cloud indexing and pattern recognition techniques on the AVHRR data. The cloud indexing technique was initially developed for NOAA but was later also adapted for Geostationary satellite images. The technique assigns rainfall levels to each cloud type identifies in an image based on the relationship between cold and bright clouds top temperature and the high probability of precipitation. We discuss how visible (VIS) and infrared (IR) techniques are applied to bi-spectral cloud classification and rain areas are determined by classifying pixel clusters in the VIS/IR histogram. Precipitation probability is evaluated based on the relationship between cold and brightness temperature of clouds. The near infrared (NIR) and infrared (IR) channels 3, 4, and 5 of the data are processed for temperature and brightness. Cold clouds with temperature below 235k threshold value are taken as indication of rain. Rainfall is estimated based on the assumption that every cloud pixel has a constant unit rain-rate of 3mmh-1, which is appropriate for tropical precipitation over 2.5o x 2.5o areas around the equator. The paper finally discusses current developments in “nowcasting” that utilizes latest satellite observations together with numerical weather prediction models and how this system can be adapted to the needs of very short term forecast for flood early warnings in Malaysia. © 2019, Springer-Verlag Berlin Heidelberg. All rights reserved."
"55552621500;55459050400;15766596700;","Classification of Terrestrial Laser Scanning Data With Density-Adaptive Geometric Features",2018,"10.1109/LGRS.2018.2860589","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85051643064&doi=10.1109%2fLGRS.2018.2860589&partnerID=40&md5=029e5e3c06134afb0aa7fa9129dd8390","Point cloud classification is a crucial procedure in ground scene interpretation. In this letter, density-adaptive geometric features are proposed for the classification of terrestrial laser scanning data, with the problem brought by point density variation as one of the main concerns. For each point, point spacing is estimated, respectively, based upon the distance to scanner position and the angular resolution, and then used as neighborhood scale basis to generate the search range of optimal radius. In feature extraction, we modify some common geometric features to adapt to density variation, e.g., a polar projection grid is proposed to generate projection features instead of commonly used rectangular grid. The polar grid can make sure similar number of laser beams passing through each grid. An evaluation involving five classifiers is carried out in an outdoor scene captured by Reigl-VZ400 scanner and the results show density-adaptive features have better and more stable performances than features without considering density variation, with the highest overall accuracy of 95.95%. Moreover, the proposed features perform well on the recognition of buildings from a large distance (more than 300 m in this letter). © 2018 IEEE."
"36004934300;8255132900;57188714386;","A neural network sea-ice cloud classification algorithm for copernicus sentinel-3 sea and land surface temperature radiometer",2018,"10.1109/IGARSS.2018.8517857","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85063159658&doi=10.1109%2fIGARSS.2018.8517857&partnerID=40&md5=d3cd7aab641e6669929bf1ca11259f27","A Neural Network approach to classify Sentinel-3 sea and land surface temperature radiometer (SLSTR) pixels over polar regions is presented. The proposed approach is based on a careful preliminary analysis aimed to simulate SLSTR observation by means of MODIS data. The latter have been considered because of the long available time series and the quality of cloud mask products. A large set of MODIS AQUA and TERRA products has been applied to develop the training set of the Neural Network classificator that has been tuned to discriminate clear ocean, clouds and sea-ice surfaces on the scene. © 2018 IEEE"
"57207001788;55948466000;55247565600;14625770800;","Joint encoding LBP features from infrared and visible-light cloud image observations for ground-based cloud classification",2018,"10.1109/IGARSS.2018.8518445","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85063158049&doi=10.1109%2fIGARSS.2018.8518445&partnerID=40&md5=2131541efaabff3f0e1959a71e0e0481","Cloud type classification based on ground-based cloud image observations is an important task in atmospheric research. Currently, two kinds of cloud image observations with infrared and visible light images are widely used for cloud classification. However, they are only independently analyzed and simply compared in the current study. The useful information from these two kinds of images is not fully utilized and integrated. The classification performance could be improved if taking full advantage of the complementary information of these two observations. Thus, first, a database containing these two kinds of cloud images with same temporal resolution is released in this study. Then, a two-observation joint encoding strategy of LBP (local binary pattern) features is proposed to implement cloud classification by encoding the joint distribution of LBP patterns in different observations, which captures the correlation between two observations. Experimental results based on this database show the significant superiority of the proposed method compared to the results based on the single observation. © 2018 IEEE."
"23983423100;57201733749;7003995144;35095461100;57201737833;18133256900;6504524263;57194385572;57203217480;35863893500;","Analysis of heavy rainfall events occurred in Italy by using numerical weather prediction, microwave and infrared technique",2018,"10.1109/IGARSS.2018.8517353","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85063129403&doi=10.1109%2fIGARSS.2018.8517353&partnerID=40&md5=2f9461c188eeaef1e5d1c6a886f85ac9","The extraordinary rainfall event that affected the center of Italy on 9 th and 10 th September 2017 was studied by examining the synoptic analysis, radar network and rain gauges' measurements. The main precipitation event took place in the area around Livorno, where more than 200 mm of precipitation was recorded in 24 hours. The case study is analyzed using Weather Research and Forecasting (WRF) model and two algorithms based on satellite observations: the Rain Class Evaluation from Infrared and Visible observation (RainCEIV) technique and the cloud Classification Mask Coupling of Statistical and Physics Methods (C-MACSP). The analysis shows that WRF is able to forecast the event, though with errors in actual structure, location, and time. For this reason, the combined use of different observational tools could support the WRF simulation to provide a better characterization of the event. © 2018 IEEE"
"57192273635;7401754145;","Evaluation of intensity and rgb values on 3d point cloud classification",2018,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85071931341&partnerID=40&md5=5bd8e690dabc6a6a6388acb2a01dec2b","3D point cloud classification has become an issue of major interest in recent years. The common workflow of 3D point cloud classification involves neighborhood selection, feature selection and extraction and the classification of points based on the respective features. The feature selection and extraction has been the focus of many studies. In most previous studies, only geometric features are used for classification and there are limited studies which have investigated the potential of both intensity and RGB values on classification using TLS (Terrestrial Laser Scanner) point cloud. Therefore, the main objective of this study is to carefully investigate the influence of intensity and RGB values on the classification performance. Firstly, the point cloud data are over-segmented into spatially consistent supervoxels. The supervoxel based neighborhood is utilized to improve computational efficiency. Then four feature sets, namely the geometric features, the geometric features combined with the intensity features, the geometric features combined with RGB features and the geometric features combined with both intensity and RGB features are extracted. These features are then used for training four Random Forest classifiers in the training stage and for classification in the prediction stage. Finally, the recall, precision and overall accuracy are used to evaluate the classification results. The designed experiments are implemented on two real-world datasets. To train the classifiers and evaluate the classification results, these two datasets are manually labeled and classified into six classes which involve ground, façade, pole-like object, tree, vegetation and curb. The experimental results show that the overall classification accuracy has been improved when either intensity or RGB features is involved and the geometric features combined with both intensity and RGB features achieves the highest overall accuracy. © 2018 Asian Association on Remote Sensing. All Rights Reserved."
"57195102154;57204491819;56217320100;57202647606;","Automatic LOD0 classification of airborne LiDAR data in urban and non-urban areas",2018,"10.1080/22797254.2018.1522934","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85055260551&doi=10.1080%2f22797254.2018.1522934&partnerID=40&md5=8177a266b6cce93946555996c72c9c27","Point clouds are a very detailed and accurate vector data model of 3D geographic information. In contrast to other data models, no standard has been defined for visualization and management of point clouds based on levels of detail (LOD). This paper proposes the application of the concept of LODs to point clouds and defines the LOD0 for point cloud classification (the lowest possible level of detail) as urban and non-urban. A methodology based on the use of machine learning techniques is developed to perform LOD0 classification to airborne LiDAR data. Point clouds acquired with airborne laser scanner (ALS) are structured in grid maps and geometric features related with Z distribution and roughness are extracted from each cell. Six machine learning classifiers have been trained with datasets including urban (cities) and non-urban samples (farmlands and forests). The influence of grid size, point density, number of features and classifier type are analysed in detail. The classifiers have been tested in three case studies. The best results correspond to a grid size of 100 m and the use of 12 geometric features. The accuracy is around 90% in all tests and Cohen’s Kappa index reaches 81% in the best of cases. © 2018, © 2018 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group."
"6507534695;","Machine-learning point cloud classification",2017,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85035035463&partnerID=40&md5=5ea0eb317d6bcf3d03056dd22d6feb8e",[No abstract available]
"6508041852;55195720700;55233617600;","Low cost cultural heritage digital documentation",2017,"10.5593/sgem2017/23/S10.021","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85032573085&doi=10.5593%2fsgem2017%2f23%2fS10.021&partnerID=40&md5=518c8b1ffdf61c58c6ebbaf1c2a4ccea","Traditionally, Cultural Heritage Assets are valuable artifacts requiring special -time consuming and costly -manual preservation procedures and always subject to accidentally or intentional damage. The paper main objective are: Using low-cost & real time or late -time (client-server) methodologies at all digital documentation phase, to make CHA accessible to all interested in people by supporting virtual mobility without CHA preservation cost and using CHA digital copies and exact 3d replicasto unlock the potential of reusing CHA for creative understanding, collaborative learning and critical thinking. From these two objectives the necessity for accurate and rapid 3D modelling is obvious. Perspective projection theory and geospatial ICT provide the means for lowcost accurate 3D modelling resulting in CHA digital documentation in the fields of cultural heritage, architecture&archaeology. Even more the low-cost 3D modelling offer great functionalities for ortho-photography&DEM, 3D model reconstruction, quality textured building reconstruction, dense cloud classification &DTM generation, and area and volume accurate measurements. © SGEM2017. All Rights Reserved."
"56905077400;7401754145;57192273635;","A joint classification method for TLS point cloud by intensity and color information",2017,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85021762790&partnerID=40&md5=472761c9bdad5bbc8c49f8d59d00f3fa","Current TLS can collect intensity returns which reflect the physical characteristics of targets. Numerous studies have utilized the intensity values to classify point cloud data. However, the intensity values acquired are influenced by numbers of factors and needed to be calibrated. The classification accuracy of methods using intensity values largely depends on the accuracy of the calibration model. Experiments showed that the accuracy of the calibrated intensity data is only ideal within certain range (between the scanner and the target) limits. Most TLS can also obtain color information which can be used as a complementary to intensity in point cloud classification. In this paper, a new classification method is proposed. This method mainly utilizes the calibrated intensity data and the color information is employed as a constraint. Compared with the previous methods, the proposed method is fault-tolerant with the calibrated intensity data, thus improving the classification results. In this study, the data of intensity and color information of the Faro Focus3D 120 terrestrial laser scanner are investigated. The experiment results indicated that the proposed method can improve the accuracy of the point cloud data classification and can also improve the credibility and reliability of classification results when the intensity data is poorly calibrated. © 2017, The Imaging and Geospatial Information Society. All rights reserved."
"57193706644;56120672600;57193717370;57193704441;36969729100;","An Integrated Method of Multiradar Quantitative Precipitation Estimation Based on Cloud Classification and Dynamic Error Analysis",2017,"10.1155/2017/1475029","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85016009338&doi=10.1155%2f2017%2f1475029&partnerID=40&md5=0d0b2d170f589efdd2b32aaf01493ac9","Relationships between radar reflectivity factor and rainfall are different in various precipitation cloud systems. In this study, the cloud systems are firstly classified into five categories with radar and satellite data to improve radar quantitative precipitation estimation (QPE) algorithm. Secondly, the errors of multiradar QPE algorithms are assumed to be different in convective and stratiform clouds. The QPE data are then derived with methods of Z-R, Kalman filter (KF), optimum interpolation (OI), Kalman filter plus optimum interpolation (KFOI), and average calibration (AC) based on error analysis on the Huaihe River Basin. In the case of flood on the early of July 2007, the KFOI is applied to obtain the QPE product. Applications show that the KFOI can improve precision of estimating precipitation for multiple precipitation types. © 2017 Yong Huang et al."
"6506288956;","Operational high latitude surface irradiance products from polar orbiting satellites",2016,"10.1016/j.polar.2016.10.001","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84994059837&doi=10.1016%2fj.polar.2016.10.001&partnerID=40&md5=833b2325030d4acff199b0c91b84e6ad","It remains a challenge to find an adequate approach for operational estimation of surface incoming short- and longwave irradiance at high latitudes using polar orbiting meteorological satellite data. In this presentation validation results at a number of North Atlantic and Arctic Ocean high latitude stations are presented and discussed. The validation results have revealed that although the method works well and normally fulfil the operational requirements, there is room for improvement. A number of issues that can improve the estimates at high latitudes have been identified. These improvements are partly related to improved cloud classification using satellite data and partly related to improved handling of multiple reflections over bright surfaces (snow and sea ice), especially in broken cloud conditions. Furthermore, the availability of validation sites over open ocean and sea ice is a challenge. © 2016 The Author"
"57192697383;57213411698;16052866300;36763767700;","A preliminary analysis of cloud classification results using Ka-band polarimetric radar signatures",2016,"10.1109/IGARSS.2016.7729135","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85007478094&doi=10.1109%2fIGARSS.2016.7729135&partnerID=40&md5=5a240d20b8662ec698e3426dc3ebbe5a","Surface based millimeter wave radar systems play a substantial role in remote sensing of clouds. A preliminary analysis of the results obtained from the algorithm developed for cloud classification is presented. Our aim is to classify different cloud types (drizzling, precipitating, Mixed Phase, Ice clouds and non-meteorological targets ) solely based on Ka-band radar data. A fuzzy logic technique is adopted and is applied to few cases which infer useful information for cloud studies. Sample datasets from the Ka-band scanning Polarimetric radar (KASPR) of Indian Institute of Tropical Meteorology (IITM), Pune are used for the initial testing of the said algorithm. The developed algorithm would be implemented in Ka-band cloud profiling radar being indigenously developed by SAMEER. © 2016 IEEE."
"55515088900;55515164600;57201675801;","From 3D point cloud to intelligent city model",2015,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-84946405540&partnerID=40&md5=277e3d859697a7acc1599e4bcf3cfe45","The paper is focused on the whole process that need to be accomplished when one wants to create detailed 3D model of the City at the level of detail 1 or more. The authors used 3D datasets from airborne lidar and mobile mapping technology for development of an advanced model of the City of Brno (Czech republic). The process includes point cloud classification, automated building vectorization and detection of trees. These steps are described in the paper in detail. However, these tasks are mostly developed by various software producers. The aim of the paper is to discuss advanced handling of the model. Authors focused on publishing the model to the web, implement it to the web GIS portal and enrich it with information from different fields. Consequently, the integration process of the environmental data from the part of the Brno City as is described in the paper as the part of the information serving platform based on detailed 3D geometry. © SGEM2015."
"55225734700;6701392598;","Torro tornado division report: May 2014",2014,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-84923453265&partnerID=40&md5=b217f2329478220404a3fe319c4e3a1e",[No abstract available]
"55850753800;56562215600;13612525600;","Acknowledgement ground-based cloud using exponential entropy/exponential gray entropy and UPSO",2014,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-84925259908&partnerID=40&md5=458159827d01328897e75a7035bc9e29","Objective and accurate classification model or method of cloud image is a prerequisite for accurate weather monitoring and forecast. Thus safety of aircraft taking off and landing and air flight can be guaranteed. Thresholding is a kind of simple and effective method of cloud classification. It can realize automated ground-based cloud detection and cloudage observation. The existing segmentation methods based on fixed threshold and single threshold cannot achieve good segmentation effect. Thus it is difficult to obtain the accurate result of cloud detection and cloudage observation. In view of the above-mentioned problems, multi-thresholding methods of ground-based cloud based on exponential entropy/exponential gray entropy and uniform searching particle swarm optimization (UPSO) are proposed. Exponential entropy and exponential gray entropy make up for the defects of undefined value and zero value in Shannon entropy. In addition, exponential gray entropy reflects the relative uniformity of gray levels within the cloud cluster and background cluster. Cloud regions and background regions of different gray level ranges can be distinguished more precisely using the multi-thresholding strategy. In order to reduce computational complexity of original exhaustive algorithm for multi-threshold selection, the UPSO algorithm is adopted. It can find the optimal thresholds quickly and accurately. As a result, the real-time processing of segmentation of ground-based cloud image can be realized. The experimental results show that, in comparison with the existing ground-based cloud image segmentation methods and multi-thresholding method based on maximum Shannon entropy, the proposed methods can extract the boundary shape, textures and details feature of cloud more clearly. Therefore, the accuracies of cloudage detection and morphology classification for ground-based cloud are both improved. ©, 2015, Nanjing University of Aeronautics an Astronautics. All right reserved."
"36816070800;8278450900;7004671182;7102643810;7401526171;26026749200;6507294227;","Augmenting satellite precipitation estimation with lightning information",2013,"10.1080/01431161.2013.796100","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84879057560&doi=10.1080%2f01431161.2013.796100&partnerID=40&md5=b21becb02eae401ac158839b0e6ebe85","We have used lightning information to augment the precipitation estimation from remotely sensed imagery using an artificial neural network cloud classification system (PERSIANN-CCS). Co-located lightning data are used to segregate cloud patches, segmented from Geostationary Operational Environmental Satellite (GOES)-12 infrared (IR) data, into either electrified patches (ECPs) or nonelectrified patches (NECPs). A set of features is extracted separately for the ECPs and NECPs. Features for the ECPs include a new feature corresponding to the number of flashes that occur within a 15 minute window around the time of the nominal scan of the satellite IR images of the cloud patches. The cloud patches are classified and clustered using a self-organizing maps (SOM) neural network. Then, brightness temperature and rain rate (T-R) relationships are derived for different clusters. Rain rates are estimated for the cloud patches based on their representative (T-R) relationship. The equitable threat scores (ETS) of the daily and hourly precipitation estimates at a range of rain rate thresholds show that incorporating lightning information can improve categorical precipitation estimation in the winter and fall seasons. In the winter, the ETS improvement is almost 15% for the daily and 12% for the hourly rainfall estimates (at thresholds below 15 mm hour-1). During the same period, there is also a drop in the false alarm ratio (FAR) and a corresponding increase in the probability of detection (POD) at most threshold levels. During the summer and spring seasons, no categorical significant improvements have been noted, except for the BIAS scores for the hourly rainfall estimates at higher thresholds (above 5 mm hour-1) in the summer months. A quantitative evaluation in terms of the root mean squared error (RMSE) and correlation coefficient (CORR) shows that the incorporation of lightning data does improve rainfall estimation over all seasons with the most improvement (around 11-13% CORR improvement) occurring during the winter. We speculate that during the winter, more of the ice processes are packed into a thinner stratiform layer with lower cloud tops and freezing levels. Hence, more of the ice contributes to precipitation on the ground. We also expect that information from lightning, related to the ice microphysics processes, provides surrogate information about the rain rate. © 2013 Copyright Taylor and Francis Group, LLC."
"8612873300;7102866124;","Cloud reflectivity profile classification using MSG/SEVIRI infrared multichannel and TRMM data",2013,"10.1080/01431161.2013.776720","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84875929857&doi=10.1080%2f01431161.2013.776720&partnerID=40&md5=fc30e5857714372d86ede1e3eb978542","This work analyses the capability of utilizing cloud-top multispectral radiation to extract information about the vertical reflectivity profile of clouds. Reflectivity profiles and cloud type classification were collected using the Tropical Rainfall Measuring Mission (TRMM) 2A25 algorithm and brightness temperature multispectral channels (3.9, 6.2, 8.7, 10.8, and 12 μm) from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) aboard the Meteosat Second Generation (MSG) satellite. The analysis was performed on four cloud types: convective, warm, and stratiform with and without bright band, using a four-channel combination (10.8-3.9, 6.2-10.8, 8.7-10.8, and 10.8-12.0 μm). The study was applied over Tropical Africa at the MSG subsatellite point, in August 2006. Sixteen individual profile types were detected: three warm, four convective, three stratiform without bright band, and six stratiform with bright band. These cloud profile types were examined using cloud-top multichannel brightness temperature differences. The channel combination results demonstrated that the information obtained from cloud-top radiation enables us to detect specific individual characteristics within the cloud reflectivity profile. The channel combinations employed in this study were effective in identifying warm and cold cloud types. In the 10.8-3.9 and 8.7-10.8 μm channels, brightness temperature differences were indicated in the detection of warm clouds, while the 6.2-10.8 μm channel was noted to be very efficient in classifying cold clouds. Cold clouds types were much more difficult to classify because they possess a similar multichannel signature, which caused ambiguity in the classification. In order to reduce this uncertainty, it was necessary to use texture information (space variability) to acquire a clearer distinction between different cloud types. The survey analysis showed good performance in classifying cloud types, with an accuracy of about 77.4% and 73.5% for night and day, respectively. © 2013 Copyright Taylor and Francis Group, LLC."
"57200235818;","Lamarck's cloud classification",2003,"10.1256/wea.57.03","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85040425012&doi=10.1256%2fwea.57.03&partnerID=40&md5=5343217c60c649f6ad26f1eec5da445a",[No abstract available]
"6602702069;6602287273;","Thermodynamic structure of the marine atmosphere over the region 80-87°E along 13°N during August (phase II) BOBMEX-99",2003,"10.1007/BF02701994","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0037699365&doi=10.1007%2fBF02701994&partnerID=40&md5=52584c314412f54295066aa28e2d1cb5","Thermodynamic structure of the marine atmosphere in the region between 80 and 87°E along 13°N over the Bay of Bengal was studied using 13 high resolution radiosonde profiles from surface -400 hPa collected onboard ORV Sagar Kanya during the period 27th-30th August, during BOBMEX-99. Saturation point concept, mixing line analysis and conserved variable diagrams have been used to identify boundary layer characteristics such as air mass movement and stability of the atmosphere. The results showed relatively dry air near the ocean surface between 1000 and 950 hPa. This feature is confirmed by the conserved χu structure in this layer. Further, χu seldom showed any inversions in this region. The χe and χes profiles showed persistent low cloud layers between 900 and 700 hPa. The conserved variable diagrams (χc-q) showed the existence of double mixing line structures approximately at 950 and 700 hPa levels."
"7005759648;7102793930;7201722295;","Multispectral analysis of a cloud field observed close to the Argentine coast: A case study",2003,"10.1016/S0169-8095(03)00014-0","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0037386927&doi=10.1016%2fS0169-8095%2803%2900014-0&partnerID=40&md5=820ace67715b95658dc86e6976ac31cd","A multispectral analysis of the data provided by the Advanced Very-High-Resolution Radiometer (AVHRR) on board the NOAA-14 satellite is used to obtain information about a cloud system located over the Buenos Aires Province (Argentina) and the adjacent Atlantic Ocean, up to about 500 km from the coast. For selected areas of the cloud system, relationships are obtained between satellite-retrieved effective radius (re) and cloud top temperature (T). It is shown that, in the middle-level clouds on the ocean, the cloud top structure is characterized by small drops (re ≈ 6-7 μm) at temperatures a few degrees below - 5 °C, and by an increase of drop size up to re ≈ 20-25 μm at temperatures between - 15 and - 18 °C, showing evidences of mixed-phase growth mechanism and glaciation. However, in the cloud area facing the continent, small particles (re &lt; 8-10 μm) were found to prevail up to the maximum altitudes, where T ≈ - 20 °C. Comparing Geostationary Operational Environmental Satellite (GOES) images of the same region, taken at time intervals of about half an hour, a possible correlation is suggested between the different top microstructures and cloud ages in the last area. Low-level clouds are observed on the continent, with top temperatures varying between about 12 and - 8 °C. In this case, re slightly increases with altitude up to about the 0 °C level, reaching maximum values close to 10-13 μm; but it decreases above this altitude, to about 5-6 μm in the highest tops. The possibility that the highest tops (where the drop size decreased with altitude) would not belong to an upper independent cloud layer-as in the cases considered by Lensky and Rosenfeld-but to the cloud system below is discussed by comparing their limited optical thickness (RC1 ≈ 0.4-0.5) with that of the lower clouds, which, at 0 °C, varies in the more extended interval RC1 ≈ 0.4-0.8. © 2003 Elsevier Science B.V. All rights reserved."
"6603265634;6505906590;57197973878;7005523706;","Assessment of neural network schemes to classify cloud data",2002,"10.2190/9VHU-BXA4-WK48-T3DY","https://www.scopus.com/inward/record.uri?eid=2-s2.0-12444285433&doi=10.2190%2f9VHU-BXA4-WK48-T3DY&partnerID=40&md5=21f869063aa4031289d6ff8df4b48965","Using remotely-sensed data from the Tropical Rainfall Measuring Mission (TRMM), a cloud classification study was undertaken employing neural network schemes. The objective of this study was to assess the accuracy of each scheme for classifying clouds. In the first phase, a data preprocessing and feature selection scheme was undertaken to identify a suitable set of features that could be useful in cloud classification. In the next phase, seven neural network classification schemes were implemented to understand the utility of each of these schemes. Parametric schemes performed poorly, while the perception, K-nearest neighbor approaches and the least means square algorithm yielded promising results. Further study is proposed so as to improve rainfall prediction."
"6507535442;","Thirty three years of Meteorological Satellites Operational use in the Institute of Meteorology and Water Management (IMWM) in Poland- History and Recent Developments",2002,"10.1016/S0273-1177(02)80308-8","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0036875091&doi=10.1016%2fS0273-1177%2802%2980308-8&partnerID=40&md5=0d02b08f2d68207c05ec8892446319d9","The first satellite images were received in IMWM in Krakow in early sixties. Since 1967 the data from meteorological satellites have been in continuous operational use. The methods and equipment for reception and processing of satellite data were changed in this period, according to the changes in the meteorological satellites. The different applications of data received from NOAA and METEOSAT satellites were developed in IMWM during those years. The history of operational use and scientific research in the field of remote sensing were briefly described. In recent years rapid development of different weather satellites applications was done in IMWM. Among many different products from satellite data available in IMWM, the following are of high importance: Baltic Sea monitoring (SST, ice cover, suspended matter), land surface of Poland monitoring (vegetation indices, temperatttre, snow cover), atmosphere monitoring (cloud classification, precipitation estimation, retrieval of temperature, moisture, wind fields and ozone content). A dense network of ground measurements and observations in IMWM provides valuable material for satellite methods evaluation and calibration. A computer network was developed to distribute satellite products to all branches of IMWM. © 2002 Published by Elsevier Science Ltd on behalf of COSPAR."
"7409838441;","Adriatic Sea CD-ROM of sea surface temperature images",1999,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-0033512174&partnerID=40&md5=09b771706f8d3f07559d08473b92d2c9","This CD-ROM contains Sea Surface Temperature images (SST) of the Adriatic Sea, from 9 May 1995 to 22 October 1995, obtained from the NOAA Advanced Very High Resolution Radiometer (AVHRR). A receiving station for the High Resolution Picture Transmission (HRPT) data stream from the NOAA polar-orbiting environmental satellites was installed on board the NRV Alliance, and operated during the 1995 cruises, to provide real-time SST images of the Adriatic Sea. Before and after the cruises, additional images were collected from shore at NATO SACLANT Undersea Research Center, La Spezia, Italy. The images were subsequently processed at the University of Hawaii Satellite Oceanography Laboratory, including calibration, navigation, cloud detection, and remapping to a common grid. The processed images cover at 1.25 km pixel resolution a domain of 250x750 pixels centered at 43 deg N 16 deg E and aligned with the Adriatic Sea. An html-based browser was designed at the Department d'Oceanographie Spatiale of IFREMER, to facilitate access to the data. The fields were compressed to GIF images without loss of information. Color-coded SST and cloud classification images are accessible through standard HTML-3.0 documents, and can be visualized using a web viewer such as Netscape or similar. Raw SST and cloud masks are also provided, and software is available on the disk to convert them back to scientific units for further analysis. The CD-Rom table of contents and data access page is located at http://satftp.soest.hawaii.edu/adriatic/Adriatic/html/contents.htm. Contact: Department of Oceanography, University of Hawaii at Manoa, 1000 Pope Road, Honolulu, HI 96822, USA; e-mail: filament@@@soest.hawaii.edu; internet: http://www.soest.hawaii.edu/ (source: Global Change Master Directory, http://gcmd.nasa.gov)."
"7201361035;6504185753;","Radiation budget components at surface and at top of atmosphere for convective cloud cases in Central Europe",1998,"10.1016/S0079-1946(98)00087-1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0032462386&doi=10.1016%2fS0079-1946%2898%2900087-1&partnerID=40&md5=b9db9520ef5e1cbeb2208e43e1666456","The paper is focused on the determination of radiation budget components at surface as well as at top of atmosphere using remotely sensed data for a few convective cloud cases between 1990 and 1993. The target area for this investigation is Central Europe and covers an area of approx. 1800 x 1800 km2. Preliminary results from heating rate computations will further be given. To infer the individual radiation budget components at surface from NOAA-AVHRR and Meteosat data, an inverse remote sensing technique was applied. This techniques uses partly look-up tables, which are generated for different atmospheric conditions. For the atmospheric radiative transfer a delta two-stream approximation scheme was used. The tables consider different variables, like solar zenith angle, cloud optical depth, cloud base height, and for the longwave standard temperature and humidity profiles. The cloud optical properties were computed with the same delta two-stream approximation scheme, where a cloud classification was carried out in advance to distinguish between cloud types. This information was used to define the microphysical cloud properties. For the atmospheric conditions monthly means of horizontal visibility and relative humidity were taken into account. The radiation budget components at top of atmosphere were computed using a narrow-to-broadband conversion considering all geometrical conditions. Finally, the heating rates for four atmospheric layers were also calculated based on the inverse remote sensing technique."
"7006894989;56999946500;6701394887;6507398007;7003877842;7005320660;","Cloud classification and retrieval from spaceborne microwave radiometry using a simulated cloud database",1997,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-0030684382&partnerID=40&md5=2076d17d76a03260eb2d9539eec8b928","A database of cloud genera is illustrated in terms of statistical distributions of hydrometeor vertical profiles. Since it is mainly derived from a microphysical model, it keeps both the microphysical consistency and the meteorological meaning. A simulated database of brightness temperatures, observed both from a spaceborne radiometer as the Special Sensor Microwave Imager and from a ground-based radiometer, is produced. An automatic classifier of cloud genera is proposed and its performances analyzed. Comparisons among SSM/I data, ground-based measurements and meteorological information are also described."
"6508155509;6701403438;","The influence of spatial resolution enhancement a implied to SSM/I data on pattern recognition",1996,"10.1080/01431169608949134","https://www.scopus.com/inward/record.uri?eid=2-s2.0-5944219588&doi=10.1080%2f01431169608949134&partnerID=40&md5=daabbc5b4fac0afb308f76fd89a1e5e1","The impact of spatial resolution enhancement on pattern recognition based on SSM/I measurements is evaluated. The instrument ground footprints for the 19, 22 and 37 GHz channels have considerable overlap. An objective technique can be applied to enhance spatial resolution of measurements to the spatial resolution of the 37 GHz channel. The authors utilize a Backus-Gilbert matrix transform approach. Different validation procedures have been performed to demonstrate the effectiveness of the method with the aim to ameliorate the boundary detection on pattern recognition and specially to cloud classification improvement. © 1996 Taylor & Francis Group, LLC."
"22950471800;7005759648;","Rainfall area identification using satellite data",1995,"10.3354/cr005259","https://www.scopus.com/inward/record.uri?eid=2-s2.0-0029511592&doi=10.3354%2fcr005259&partnerID=40&md5=49216412abd49428282491a8e1a8ccaf","A technique is presented for determining rainfall areas through the simultaneous use of infrared and near-infrared satellite data. These data are a reduced-resolution version of the original AVHRR (Advanced Very High Resolution Radiometer) images from the satellite NOAA-12. The scheme developed was used in order to differentiate among clear sky (or with very few clouds present), raining clouds and non-raining clouds for a mid-latitude region over Argentina. The scheme was developed by applying clustering and discriminant analysis to the maxima obtained from 2-dimensional histograms in the infrared and near-infreared domain. Warm-season data during daylight hours were used to develop and test the scheme. It was tested on 9 dates, and comparison of the classification results with reports from ground stations was encouraging. -Authors"
"57188972870;6602944180;7402706393;","Multi-spectral texture analysis for cloud feature discrimination",1992,"10.1109/IGARSS.1992.578328","https://www.scopus.com/inward/record.uri?eid=2-s2.0-84964474505&doi=10.1109%2fIGARSS.1992.578328&partnerID=40&md5=87e4b142bbd21cf84e817457c912d572","Cloud classification is a difficult task due to the spectral homogeneity of cloud features. In recent years, researchers have devoted considerable attention to the development of new spectral and spatial measures, such as texture analysis, in order to distinguish between different cloud types. A new textural method for cloud classification, based on localized spatial filters was implemented, and the results are summarized in this paper. The textural measure being investigated is based on a class of filters known as Gabor filters. These filters discriminate textural features in a similar manner to that of human vision. This is particularly attractive for the cloud classification problem because the most accurate interpretation still involves ""visual"" subjective classification of images by a meteorologist or climatologist. The new method was applied to NOAA Advanced Very High Resolution Radiometer (AVHRR) imagery containing various cloud classes and meteorologic features. An extensive sensitivity analysis was performed in order to characterize the behavior of parameter settings. Currently, the method is being applied to additional imagery of various spectral and spatial characteristics. © IEEE 1992."
"7402546593;7201914101;6506702741;","Performance of two texture-based classifiers of cloud fields using spatially averaged Landsat data",1989,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-0024813470&partnerID=40&md5=e130d4916301b506a72e3eae014492c8","Using the gray-level difference vector approach, classification accuracies with 1/8-km spatial-resolution data are similar to those obtained using the full spatial-resolution features. The implication is that there are no advantages to be gained in cloud classification accuracies by using even higher spatial resolutions obtained from Landsat Thematic Mapper (TM) or SPOT imagery. The optimum spatial resolution is 1/4 km. However, significant improvement in cloud-classification accuracy compared to that available from the 1-km resolution of AVHRR and GOES imagery is obtained using 1/2-km-resolution data. Cirrus-classification accuracy is especially compromised as spatial resolution is degraded. However, texture measures defined at the combination of pixel separations d = 1,4 improve classification accuracies by several percent, even for 1-km spatial-resolution data. Cirrus-classification accuracy is significantly improved by the use of multiple distance features. In regard to the max-min cooccurrence matrix approach, spatial distribution of threshold consecutive extremes allows for the creation of textural features that bring additional discriminating power to the classifier."
"57212075803;","Production of probability of precipitation maps over oceanic areas from automated cloud classification",1988,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-0024079170&partnerID=40&md5=138a2e6541c5438c0d51cdfb6ec5fd3d","Cloud fields are classified into seventeen classes at the scale of approximately 130 km using L. Garand's (1988) scheme. From collocation with ship observations, the probability of precipitation is established for each class. From independent data taken in 1984 (1067 cases) and 1986 (673 cases) the absolute accuracy of the method is found to be about 1.1% on the monthly scale. However the root-mean-square (RMS) differences between months can be as large as 5.8%, showing the validity of the method as a climate indicator."
"7202296481;","Analysis of 3DNEPH over the North Atlantic Ocean. M.Sc. thesis",1988,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85040879197&partnerID=40&md5=2815bbdc1ac971b6fa90e5ad444cdc78","In this work, the 3DNEPH of January and February, 1979, over the North Atlantic Ocean (40°-60°N and 10°-50°W) are analyzed to obtain the general cloud statistics on total cloud amount and cloud types. The persistence of high amount of cloud cover (69%) and the dominance of layered cloud types in the region are shown. The contigency probability of different cloud types occurring not only gives the information on the co-occurrence of different cloud types but also allow us to examine the cloud classification algorithm of 3DNEPH. In general, more than half of the cases can be approximated by the random and minimum overlap assumption for all grid sizes used. As the grid size increases, the chance that a case can be approximated by the maximum overlap assumption decreases, which is only 11% for (442 km)2. There are 13% of the cases that cannot be approximated by any three of the overlap approximations. This number increases to 16% as grid size increases to (445 km)2. -from Author"
"57212075803;","Production of probability of precipitation maps over oceanic areas from automated cloud classification",1988,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-0024191722&partnerID=40&md5=24061618713abf1cee3c397c1e9cc104","Cloud fields are classified in 17 classes at the scale of ~130 km using the scheme of Garand. From collocation with ship observations, the probability of precipitation is established for each class. The absolute accuracy of the method is found to be about 1.1% at monthly scale. However the RMS differences between various months can be as large as 5.8%. -from Author"
"7409713969;7202997063;","Textural and spectral features as an aid to cloud classification",1988,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-0024145642&partnerID=40&md5=887b52bb3937366303036eeeecebf760","The problem of classifying clouds seen on meteorological satellite images into different types is one which requires the use of textural as well as spectral information. Several textural features are studied to determine their discriminating power across a number of cloud classes including those which have previously been found difficult to separate. -from Authors"
"57196396429;6701607011;","Sampling problems and cloud statistics.",1983,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-0020924630&partnerID=40&md5=e2c58f4f565f210326ad989177a84928","A cloud classification algorithm used to investigate problems of satellite imagery sampling and to derive cloud statistics from Meteosat and GOES EAST data is described. The dynamic cluster method is applied to histograms composed by pixels of a chosen area, which constitute the learning set. The number of classes extracted by the algorithm; the kernels of the classes; a value in each channel and their variances; and the percentage of points in each class of the learning set are obtained. Each point of the image is attributed to a class according to the definition used in the first stage of the distance to a class in the histogram space; Euclidian distance to the kernel of a class + variance of the class. Output is the percentage of points in each class of the image, and classified images. -from STAR, 22(10), 1984"
"57209013730;","Clouds.",1982,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-0020391526&partnerID=40&md5=dbf28262197813525981636c34a01507","Describes the mechanisms of cloud formation and subsequent cloud growth and deals with cloud classification. The growth of droplets is explained with reference to the Bergeron theory. -M.Day"
"7101714152;7401813766;","Satellite cloud analyses for a radiation model.",1980,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-0019094930&partnerID=40&md5=8e7eed20faff2563826e75f2f69c9be9","Pattern recognition techniques for cloud type and cloud amount classification were applied to digital infrared SMS-1 data. The cloud classification results were used in a numerical radiation model to determine solar radiation during Phase III of the GARP Atlantic Tropical Experiment. In order to assess the effects on radiation computations of cloud information derived from both satellite and ship data, cloud analyses based on both data sources were prepared for input into the numerical radiation model. -from Authors"