Title of paper,Article DOI (or URL),Journal name,Year of publication,Is there an ASDC?,Justification for non-open data?,All data available,At least some data available,All code available,At least some code available,All data available upon request,At least some data available upon request,no data produced,links work
Photographic Visualization of Weather Forecasts with Generative Adversarial Networks,https://doi.org/10.1175/AIES-D-22-0028.1,AIES,2023,yes,Licensing/Proprietary,n,y,y,y,,n,,y
A Hybrid Physics–AI Model to Improve Hydrological Forecasts,https://doi.org/10.1175/AIES-D-22-0023.1,AIES,2023,yes,Dataset too large,n,y,n,n,,n,,y
Deep Learning–Based Parameter Transfer in Meteorological Data,https://doi.org/10.1175/AIES-D-22-0024.1,AIES,2023,yes,Can be obtained from other entities,n,y,y,y,,n,,y
Can a Machine Learning–Enabled Numerical Model Help Extend Effective Forecast Range through Consistently Trained Subgrid-Scale Models?,https://doi.org/10.1175/AIES-D-22-0050.1,AIES,2023,yes,None,n,n,y,y,n,n,,y
A Primer on Topological Data Analysis to Support Image Analysis Tasks in Environmental Science,https://doi.org/10.1175/AIES-D-22-0039.1,AIES,2023,yes,N/A,y,y,y,y,,,,y
Emulating Rainfall–Runoff-Inundation Model Using Deep Neural Network with Dimensionality Reduction,https://doi.org/10.1175/AIES-D-22-0036.1,AIES,2023,yes,Dataset too large,n,n,n,n,y,y,,
Strictly Enforcing Invertibility and Conservation in CNN-Based Super Resolution for Scientific Datasets,https://doi.org/10.1175/AIES-D-21-0012.1,AIES,2023,yes,N/A,y,y,n,n,,,,y
Physics-Informed Deep Neural Network for Backward-in-Time Prediction: Application to Rayleigh–Bénard Convection,https://doi.org/10.1175/AIES-D-22-0076.1,AIES,2023,yes,N/A,y,y,y,y,,,,y
Global Extreme Heat Forecasting Using Neural Weather Models,https://doi.org/10.1175/AIES-D-22-0035.1,AIES,2023,yes,N/A,y,y,y,y,,,,y
Emulating the Adaptation of Wind Fields to Complex Terrain with Deep Learning,https://doi.org/10.1175/AIES-D-22-0034.1,AIES,2023,yes,None,n,y,y,y,,n,,y
A Real-Time Spatiotemporal Machine Learning Framework for the Prediction of Nearshore Wave Conditions,https://doi.org/10.1175/AIES-D-22-0033.1,AIES,2023,yes,None,y,y,n,n,,,,
Carefully Choose the Baseline: Lessons Learned from Applying XAI Attribution Methods for Regression Tasks in Geoscience,https://doi.org/10.1175/AIES-D-22-0058.1,AIES,2023,yes,N/A,y,y,y,y,,,,y
Convective-Scale Assimilation of Cloud Cover from Photographs Using a Machine Learning Forward Operator,https://doi.org/10.1175/AIES-D-22-0025.1,AIES,2023,yes,N/A,y,y,n,n,,,,y
Creating and Evaluating Uncertainty Estimates with Neural Networks for Environmental-Science Applications,https://doi.org/10.1175/AIES-D-22-0061.1,AIES,2023,yes,N/A,y,y,y,y,,,,y
Detail Enhancement of AIRS/AMSU Temperature and Moisture Profiles Using a 3D Deep Neural Network,https://doi.org/10.1175/AIES-D-22-0037.1,AIES,2023,yes,Dataset too large,n,y,n,n,,n,,y
Subseasonal Prediction of Central European Summer Heatwaves with Linear and Random Forest Machine Learning Models,https://doi.org/10.1175/AIES-D-22-0038.1,AIES,2023,yes,N/A,y,y,y,y,,,,y
Efficient Probabilistic Prediction and Uncertainty Quantification of Tropical Cyclone–Driven Storm Tides and Inundation,https://doi.org/10.1175/AIES-D-22-0040.1,AIES,2023,yes,N/A,y,y,y,y,,,,y
Subseasonal Representation and Predictability of North American Weather Regimes Using Cluster Analysis,https://doi.org/10.1175/AIES-D-22-0051.1,AIES,2023,yes,N/A,y,y,y,y,,,,y
Application of Machine Learning Techniques to Improve Multi-Radar Multi-Sensor (MRMS) Precipitation Estimates in the Western United States,https://doi.org/10.1175/AIES-D-22-0053.1,AIES,2023,yes,N/A,y,y,n,n,,,,y
Bias Correcting Climate Model Simulations Using Unpaired Image-to-Image Translation Networks,https://doi.org/10.1175/AIES-D-22-0031.1,AIES,2023,yes,N/A,y,y,y,y,,,,n
Reducing Southern Ocean Shortwave Radiation Errors in the ERA5 Reanalysis with Machine Learning and 25 Years of Surface Observations,https://doi.org/10.1175/AIES-D-22-0044.1,AIES,2023,yes,N/A,y,y,n,n,,,,y
Let’s Unleash the Network Judgment: A Self-Supervised Approach for Cloud Image Analysis,https://doi.org/10.1175/AIES-D-22-0063.1,AIES,2023,yes,N/A,y,y,y,y,,,,y
Using Machine Learning to Understand Relocation Drivers of Urban Coastal Populations in Response to Flooding,https://doi.org/10.1175/AIES-D-22-0054.1,AIES,2023,yes,Sensitive information,n,n,n,n,n,n,,
Simulation of Atlantic Hurricane Tracks and Features: A Coupled Machine Learning Approach,https://doi.org/10.1175/AIES-D-22-0060.1,AIES,2023,yes,None,n,n,n,n,y,y,,
Using Neural Networks to Learn the Jet Stream Forced Response from Natural Variability,https://doi.org/10.1175/AIES-D-22-0094.1,AIES,2023,yes,N/A,y,y,y,y,,,,y
GRRIEn Analysis: A Data Science Cheat Sheet for Earth Scientists Learning from Global Earth Observations,https://doi.org/10.1175/AIES-D-22-0065.1,AIES,2023,yes,N/A,y,y,n,n,,,,y
Improving Seasonal Prediction of Summer Precipitation in the Middle–Lower Reaches of the Yangtze River Using a TU-Net Deep Learning Approach,https://doi.org/10.1175/AIES-D-22-0078.1,AIES,2023,yes,N/A,y,y,n,n,,,,n
A Review of Machine Learning for Convective Weather,https://doi.org/10.1175/AIES-D-22-0077.1,AIES,2023,yes,N/A,,,,,,,y,
TCDetect: A New Method of Detecting the Presence of Tropical Cyclones Using Deep Learning,https://doi.org/10.1175/AIES-D-22-0045.1,AIES,2023,yes,N/A,y,y,y,y,,,,y
Toward Operational Real-Time Identification of Frontal Boundaries Using Machine Learning,https://doi.org/10.1175/AIES-D-22-0052.1,AIES,2023,yes,N/A,y,y,y,y,,,,y
A Deep Learning–Based Velocity Dealiasing Algorithm Derived from the WSR-88D Open Radar Product Generator,https://doi.org/10.1175/AIES-D-22-0084.1,AIES,2023,yes,Can be obtained from other entities,n,y,n,n,,n,,y
Correcting Subseasonal Forecast Errors with an Explainable ANN to Understand Misrepresented Sources of Predictability of European Summer Temperatures,https://doi.org/10.1175/AIES-D-22-0047.1,AIES,2023,yes,N/A,y,y,y,y,,,,y
O3ResNet: A Deep Learning–Based Forecast System to Predict Local Ground-Level Daily Maximum 8-Hour Average Ozone in Rural and Suburban Environments,https://doi.org/10.1175/AIES-D-22-0085.1,AIES,2023,yes,N/A,y,y,y,y,,,,y
Skillful U.S. Soy Yield Forecasts at Presowing Lead Times,https://doi.org/10.1175/AIES-D-21-0009.1,AIES,2023,yes,N/A,y,y,y,y,,,,y
Automatic Detection of Rainfall at Hourly Time Scales from Mooring Near-Surface Salinity in the Eastern Tropical Pacific,https://doi.org/10.1175/AIES-D-22-0009.1,AIES,2023,yes,N/A,y,y,n,n,,,,y
Atmospheric Pattern–Based Predictions of S2S Sea Level Anomalies for Two Selected U.S. Locations,https://doi.org/10.1175/AIES-D-22-0057.1,AIES,2023,yes,N/A,y,y,n,n,,,,y
Surrogate Downscaling of Mesoscale Wind Fields Using Ensemble Superresolution Convolutional Neural Networks,https://doi.org/10.1175/AIES-D-23-0007.1,AIES,2023,yes,Licensing/Proprietary,n,y,n,n,,n,,y
Understanding Spatial Context in Convolutional Neural Networks Using Explainable Methods: Application to Interpretable GREMLIN,https://doi.org/10.1175/AIES-D-22-0093.1,AIES,2023,yes,N/A,y,y,y,y,,,,y
A New Approach to Predict Tributary Phosphorus Loads Using Machine Learning– and Physics-Based Modeling Systems,https://doi.org/10.1175/AIES-D-22-0049.1,AIES,2023,yes,N/A,y,y,n,n,,,,y
Probabilistic Forecasting Methods of Winter Mixed-Precipitation Events in New York State Utilizing a Random Forest,https://doi.org/10.1175/AIES-D-22-0080.1,AIES,2023,yes,N/A,y,y,y,y,,,,y
Machine Learning–Based Cloud Forecast Corrections for Fusions of Numerical Weather Prediction Model and Satellite Data,https://doi.org/10.1175/AIES-D-22-0072.1,AIES,2023,yes,Sensitive information,n,y,n,n,,n,,y
Environment-Aware Digital Twins: Incorporating Weather and Climate Information to Support Risk-Based Decision-Making,https://doi.org/10.1175/AIES-D-23-0023.1,AIES,2023,yes,N/A,,,,,,,y,
Perspectives on Artificial Intelligence for Predictions in Ecohydrology,https://doi.org/10.1175/AIES-D-23-0005.1,AIES,2023,yes,N/A,,,,,,,y,
A Review of Recent and Emerging Machine Learning Applications for Climate Variability and Weather Phenomena,https://doi.org/10.1175/AIES-D-22-0086.1,AIES,2023,yes,N/A,,,,,,,y,
ARMing the Edge: Designing Edge Computing–Capable Machine Learning Algorithms to Target ARM Doppler Lidar Processing,https://doi.org/10.1175/AIES-D-22-0062.1,AIES,2023,yes,N/A,y,y,y,y,,,,y
Deriving Severe Hail Likelihood from Satellite Observations and Model Reanalysis Parameters Using a Deep Neural Network,https://doi.org/10.1175/AIES-D-22-0042.1,AIES,2023,yes,N/A,y,y,n,y,,,,n
Assessing Tropical Pacific–Induced Predictability of Southern California Precipitation Using a Novel Multi-Input Multioutput Autoencoder,https://doi.org/10.1175/AIES-D-23-0003.1,AIES,2023,yes,N/A,y,y,n,n,,,,y
A Deep Learning Filter for the Intraseasonal Variability of the Tropics,https://doi.org/10.1175/AIES-D-22-0079.1,AIES,2023,yes,N/A,y,y,y,y,,,,n
Cross-Validation Strategy Impacts the Performance and Interpretation of Machine Learning Models,https://doi.org/10.1175/AIES-D-23-0026.1,AIES,2023,yes,N/A,y,y,y,y,,,,y
Machine Learning for Daily Forecasts of Arctic Sea Ice Motion: An Attribution Assessment of Model Predictive Skill,https://doi.org/10.1175/AIES-D-23-0004.1,AIES,2023,yes,N/A,y,y,n,n,,,,y
Exploring Randomly Wired Neural Networks for Climate Model Emulation,https://doi.org/10.1175/AIES-D-22-0088.1,AIES,2023,yes,N/A,y,y,y,y,,,,y
Multivariate Emulation of Kilometer-Scale Numerical Weather Predictions with Generative Adversarial Networks: A Proof of Concep,https://doi.org/10.1175/AIES-D-23-0006.1,AIES,2023,yes,Available at later date,n,n,y,y,n,n,,y
Can Machine Learning Models Be a Suitable Tool for Predicting Central European Cold Winter Weather on Subseasonal to Seasonal Time Scales?,https://doi.org/10.1175/AIES-D-23-0020.1,AIES,2023,yes,N/A,y,y,y,y,,,,y
Short-Term (7 Day) Beaufort Sea Ice Extent Forecasting with Deep Learning,https://doi.org/10.1175/AIES-D-22-0070.1,AIES,2023,yes,N/A,y,y,n,n,,,,n
Uncertainty Calibration of Passive Microwave Brightness Temperatures Predicted by Bayesian Deep Learning Models,https://doi.org/10.1175/AIES-D-22-0056.1,AIES,2023,yes,N/A,y,y,n,n,,,,y
Statistical Modeling of Monthly and Seasonal Michigan Snowfall Based on Machine Learning: A Multiscale Approach,https://doi.org/10.1175/AIES-D-23-0016.1,AIES,2023,yes,N/A,y,y,y,y,,,,y
Improving Precipitation Nowcasting for High-Intensity Events Using Deep Generative Models with Balanced Loss and Temperature Data: A Case Study in the Netherlands,https://doi.org/10.1175/AIES-D-23-0017.1,AIES,2023,yes,N/A,y,y,n,n,,,,y
Understanding Cloud Systems’ Structure and Organization Using a Machine’s Self-Learning Approach,https://doi.org/10.1175/AIES-D-22-0096.1,AIES,2023,yes,N/A,y,y,y,y,,,,y
Insights into the Drivers and Spatiotemporal Trends of Extreme Mediterranean Wildfires with Statistical Deep Learning,https://doi.org/10.1175/AIES-D-22-0095.1,AIES,2023,yes,N/A,y,y,y,y,,,,y
"The Development and Initial Capabilities of ThunderCast, a Deep Learning Model for Thunderstorm Nowcasting in the United States",https://doi.org/10.1175/AIES-D-23-0044.1,AIES,2023,yes,N/A,y,y,y,y,,,,y
Machine Learning for Nonorographic Gravity Waves in a Climate Model,https://doi.org/10.1175/AIES-D-22-0081.1,AIES,2023,yes,N/A,y,y,y,y,,,,y
Algorithmic Hallucinations of Near-Surface Winds: Statistical Downscaling with Generative Adversarial Networks to Convection-Permitting Scales,https://doi.org/10.1175/AIES-D-23-0015.1,AIES,2023,yes,N/A,y,y,y,y,,,,y
Physics-Constrained Deep Learning Postprocessing of Temperature and Humidity,https://doi.org/10.1175/AIES-D-22-0089.1,AIES,2023,yes,N/A,y,y,y,y,,,,y
Current Training and Validation Weaknesses in Classification-Based Radiation Fog Nowcast Using Machine Learning Algorithms,https://doi.org/10.1175/AIES-D-21-0006.1,AIES,2022,yes,Available at later date,n,y,n,n,,n,,y
Detection of Bow Echoes in Kilometer-Scale Forecasts Using a Convolutional Neural Network,https://doi.org/10.1175/AIES-D-21-0010.1,AIES,2022,yes,N/A,y,y,y,y,,,,y
Accurate and Clear Quantitative Precipitation Nowcasting Based on a Deep Learning Model with Consecutive Attention and Rain-Map Discrimination,https://doi.org/10.1175/AIES-D-21-0005.1,AIES,2022,yes,Can be obtained from other entities,n,n,y,y,,,,y
Global Mesoscale Ocean Variability from Multiyear Altimetry: An Analysis of the Influencing Factors,https://doi.org/10.1175/AIES-D-21-0008.1,AIES,2022,yes,None,n,y,n,n,,n,,n
This Looks Like That There: Interpretable Neural Networks for Image Tasks When Location Matters,https://doi.org/10.1175/AIES-D-22-0001.1,AIES,2022,yes,N/A,y,y,y,y,,,,y
"Challenges and Benchmark Datasets for Machine Learning in the Atmospheric Sciences: Definition, Status, and Outlook",https://doi.org/10.1175/AIES-D-21-0002.1,AIES,2022,yes,N/A,,,,,,,y,
Automated Identification of Characteristic Droplet Size Distributions in Stratocumulus Clouds Utilizing a Data Clustering Algorithm,https://doi.org/10.1175/AIES-D-22-0003.1,AIES,2022,yes,N/A,y,y,n,n,,,,y
Archetypal Analysis of Geophysical Data Illustrated by Sea Surface Temperature,https://doi.org/10.1175/AIES-D-21-0007.1,AIES,2022,yes,N/A,y,y,n,n,,,,y
Probing the Explainability of Neural Network Cloud-Top Pressure Models for LEO and GEO Imagers,https://doi.org/10.1175/AIES-D-21-0001.1,AIES,2022,yes,N/A,y,y,n,n,,,,n
Adaptive Blending of Probabilistic Precipitation Forecasts with Emphasis on Calibration and Temporal Forecast Consistency,https://doi.org/10.1175/AIES-D-22-0020.1,AIES,2022,yes,N/A,y,y,n,n,,,,y
Investigating the Fidelity of Explainable Artificial Intelligence Methods for Applications of Convolutional Neural Networks in Geoscience,https://doi.org/10.1175/AIES-D-22-0012.1,AIES,2022,yes,N/A,y,y,y,y,,,,y
Machine Learning Crop Yield Models Based on Meteorological Features and Comparison with a Process-Based Model,https://doi.org/10.1175/AIES-D-22-0002.1,AIES,2022,yes,N/A,y,y,n,n,,,,y
Automated Identification of “Dunkelflaute” Events: A Convolutional Neural Network–Based Autoencoder Approach,https://doi.org/10.1175/AIES-D-22-0015.1,AIES,2022,yes,N/A,y,y,n,n,,,,y
The Pairwise Similarity Partitioning Algorithm: A Method for Unsupervised Partitioning of Geoscientific and Other Datasets Using Arbitrary Similarity Metrics,https://doi.org/10.1175/AIES-D-22-0005.1,AIES,2022,yes,N/A,y,y,y,y,,,,y
Application of Deep Learning to Understanding ENSO Dynamics,https://doi.org/10.1175/AIES-D-21-0011.1,AIES,2022,yes,N/A,y,y,n,n,,,,y
Hybrid Neural Network Models for Postprocessing Medium-Range Forecasts of Tropical Cyclone Tracks over the Western North Pacific,https://doi.org/10.1175/AIES-D-21-0003.1,AIES,2022,yes,N/A,y,y,n,n,,,,y
Seamless Lightning Nowcasting with Recurrent-Convolutional Deep Learning,https://doi.org/10.1175/AIES-D-22-0043.1,AIES,2022,yes,None,,,,,,,y,
Improvements to the Land Surface Air Temperature Reconstruction in NOAAGlobalTemp: An Artificial Neural Network Approach,https://doi.org/10.1175/AIES-D-22-0032.1,AIES,2022,yes,N/A,y,y,n,n,,,,n
Can We Integrate Spatial Verification Methods into Neural Network Loss Functions for Atmospheric Science?,https://doi.org/10.1175/AIES-D-22-0021.1,AIES,2022,yes,None,n,n,y,y,y,y,,y
Downscaling of Historical Wind Fields over Switzerland Using Generative Adversarial Networks,https://doi.org/10.1175/AIES-D-22-0018.1,AIES,2022,yes,Can be obtained from other entities,n,y,n,n,,n,,y
Modeling Spatial Distribution of Snow Water Equivalent by Combining Meteorological and Satellite Data with Lidar Maps,https://doi.org/10.1175/AIES-D-22-0010.1,AIES,2022,yes,N/A,y,y,n,n,,,,y
Understanding Predictability of Daily Southeast U.S. Precipitation Using Explainable Machine Learning,https://doi.org/10.1175/AIES-D-22-0011.1,AIES,2022,yes,N/A,y,y,y,y,,,,y
On Variability due to Local Minima and K-Fold Cross Validation,https://doi.org/10.1175/AIES-D-21-0004.1,AIES,2022,yes,None,n,n,n,n,y,y,,
Perspectives on AI Architectures and Codesign for Earth System Predictability,https://doi.org/10.1175/AIES-D-23-0029.1,AIES,2024,yes,N/A,,,,,,,y,
Exploring the Use of Machine Learning to Improve Vertical Profiles of Temperature and Moisture,https://doi.org/10.1175/AIES-D-22-0090.1,AIES,2024,yes,N/A,y,y,y,y,,,,y
Deep Learning Parameterization of Vertical Wind Velocity Variability via Constrained Adversarial Training,https://doi.org/10.1175/AIES-D-23-0025.1,AIES,2024,yes,N/A,y,y,n,n,,,,n
Physics-Inspired Adaptions to Low-Parameter Neural Network Weather Forecast Systems,https://doi.org/10.1175/AIES-D-23-0046.1,AIES,2024,yes,N/A,y,y,y,y,,,,y
Deep Learning Image Segmentation for Atmospheric Rivers,https://doi.org/10.1175/AIES-D-23-0048.1,AIES,2024,yes,N/A,y,y,n,y,,,,y
A Machine Learning Explainability Tutorial for Atmospheric Sciences,https://doi.org/10.1175/AIES-D-23-0018.1,AIES,2024,yes,N/A,y,y,y,y,,,,y
Statistical Treatment of Convolutional Neural Network Superresolution of Inland Surface Wind for Subgrid-Scale Variability Quantification,https://doi.org/10.1175/AIES-D-23-0009.1,AIES,2024,yes,N/A,y,y,y,y,,,,y
Self-Supervised Cloud Classification,https://doi.org/10.1175/AIES-D-23-0036.1,AIES,2024,yes,Can be obtained from other entities,n,y,y,y,,n,,y
Improving Medium-Range Ensemble Weather Forecasts with Hierarchical Ensemble Transformers,https://doi.org/10.1175/AIES-D-23-0027.1,AIES,2024,yes,Available at later date,n,n,y,y,,,,y
Postprocessing of Ensemble Weather Forecasts Using Permutation-Invariant Neural Networks,https://doi.org/10.1175/AIES-D-23-0070.1,AIES,2024,yes,Licensing/Proprietary,n,y,y,y,,n,,y
Airborne Radar Quality Control with Machine Learning,https://doi.org/10.1175/AIES-D-23-0064.1,AIES,2024,yes,N/A,y,y,n,n,,,,y
Automated Large-Scale Tornado Treefall Detection and Directional Analysis Using Machine Learning,https://doi.org/10.1175/AIES-D-23-0062.1,AIES,2024,yes,N/A,y,y,y,y,,,,y
Development of an Optimal Postprocessing Model Using the Microgenetic Algorithm to Improve Precipitation Forecasting in South Korea,https://doi.org/10.1175/AIES-D-23-0069.1,AIES,2024,yes,N/A,y,y,y,y,,,,y
Two-Step Hyperparameter Optimization Method: Accelerating Hyperparameter Search by Using a Fraction of a Training Dataset,https://doi.org/10.1175/AIES-D-23-0013.1,AIES,2024,yes,N/A,y,y,y,y,,,,y
Limitations of XAI Methods for Process-Level Understanding in the Atmospheric Sciences,https://doi.org/10.1175/AIES-D-23-0045.1,AIES,2024,yes,N/A,y,y,y,y,,,,n
Environmental Justice and Lessons Learned from COVID-19 Outcomes—Uncovering Hidden Patterns with Geometric Deep Learning and New NASA Satellite Data,https://doi.org/10.1175/AIES-D-23-0040.1,AIES,2024,yes,N/A,y,y,n,n,,,,y
Enhancing Regional Climate Downscaling through Advances in Machine Learning,https://doi.org/10.1175/AIES-D-23-0066.1,AIES,2024,yes,N/A,,,,,,,y,
Machine Learning Estimation of Maximum Vertical Velocity from Radar,https://doi.org/10.1175/AIES-D-23-0095.1,AIES,2024,yes,Dataset too large,n,y,y,y,,n,,y
Efficient Data-Driven Gap Filling of Satellite Image Time Series Using Deep Neural Networks with Partial Convolutions,https://doi.org/10.1175/AIES-D-22-0055.1,AIES,2024,yes,N/A,y,y,y,y,,,,y
Superresolution of GOES-16 ABI Bands to a Common High Resolution with a Convolutional Neural Network,https://doi.org/10.1175/AIES-D-23-0065.1,AIES,2024,yes,N/A,y,y,n,n,,,,y
Investigating Differences between Tropical Cyclone Detection Systems,https://doi.org/10.1175/AIES-D-22-0046.1,AIES,2024,yes,N/A,y,y,y,y,,,,y