TARANIS data base A data paper, by Gires A., Tchiguirinskaia I. and Schertzer D. entitled "Two months of disdrometer data in the Paris area" presenting in details this data base is available on the website of "Earth System Science Data Journal" https://www.earth-system-science-data.net. User should cite this paper as well as this data base. Measurement campaign: - 01/01/2016 --> 29/02/2016 : Pars1, Pars2 and PWS on the roof the ENPC/ENSG Carnot Building; time is expressed in local time. This campaign is denoted "Carnot_1". 1) Folder description (it is located on smb://netapp2.enpc.fr/stockage_hmco/disdrometers_data_base/) disdrometers_data_dase/ Raw_data_zip/ Pars1/ Pasr2/ Pars_rad/ PWS/ Each folder contains the files for its disdrometers. The name is Raw_DisdroName_YearMonthDay.zip (ex : Raw_pars1_20161117.zip) Daily_data_python/ Pars1/ Pasr2/ Pars_rad/ PWS/ Each folder contains the files for its disdrometers. The name is DisdroName_raw_data_YearMonthDay.csv (ex : Pars1_raw_data_201306018.npy) Calendars/ Data_5_min/ (one file per day ex: Data_5_min_Carnot_1_2015_11_16_00_00_00__2015_11_16_23_59_30.csv) Data_30_sec/ (one file per day ex: Data_30_sec_Carnot_1_2015_11_16_00_00_00__2015_11_16_23_59_30.csv) Quicklooks/ (one file per day ex : Quicklook_Carnot_1_2015_11_16_00_00_00__2015_11_16_23_59_30.png Calendar_data_5_min_Carnot_1.html Calendar_data_30_sec_Carnot_1.html Calendar_Carnot_1.html Python_scripts/ It contains the python scripts (and associated files) to generate and use this data base. Read_me.txt It contains a short description of the Taranis data base. 2) Tools The tools are located in the script "Tools_data_base_use_v3.py". The main functions are (only a short description is given here - more details are provided as comments in each script) : - Quicklook_and_R_series_generation_Carnot_1 : generating a quicklook image and the corresponding rainfall 30s and 5min rain rate time series for a given rainfall event for the Carnot_1 campaign. - extracting_one_event_Carnot_1 : reading daily.npy files and genrating three lists (one for each disdrometer) containing all the data that can be analyzed for the Carnot_1 campaign. - exporting_full_matrix : reading daily.npy files and exporting full matrix in .csv files for a given disdrometer and event - exporting_R : reading daily.npy files and exporting R in .csv files for a given disdrometer and event - exporting_T : reading daily.npy files and exporting T in .csv files for a given disdrometer and event - exporting_full_matrix_and_T : reading daily.npy files and exporting full matrix and T in .csv files for a given disdrometer and event - Generation_daily_data_python_Carnot_1 : reading the raw data and generating the daily python files (users should not need to use it) Commented examples of use of the functions can be found in the script : - Example_of_use_data_base_Carnot_1.py * Python3 is required because the .npy files containing the data were saved using python3 3) File format - Full matrix daily .csv files They are denoted : "disdro_name"_full_matrix_"evt_limits".csv (ex :Pars_Rad_full_matrix_2017_05_18_16_00_00__2017_05_19_01_00_00.csv) One line per time step Date; number of drops per class of velocity and size (1st size class - 1st velocity class,1st size class - 2nd velocity class, 1st size class - 2nd velocity class, ... , 2nd size class - 1st velocity class...) separated by comas 34 * 34 classes for PWS data 32 * 32 classes for Parsivel data WARNING : data is measured only 9/10 of the time for PWS, hence to have comparable values, one should multiply by 10/9 the number of drops in each class for the PWS. ex : 19:30:00;0.0,0.0,0.0,0.0,0.0,0.0,0.0 ..... (no value are indicated as nan) - R 30 sec daily .csv files They are denoted : "disdro_name"_R_30_sec_"evt_limits".csv (ex :Pars1_R_30_sec_2017_05_18_16_00_00__2017_05_19_01_00_00.csv) One line per time step Date; Rain rate in mm/h over 30 seconds ex : 2017-05-18 16:00:00;0.0 - R 5 min daily .csv files They are denoted : "disdro_name"_R_5_min_"evt_limits".csv (ex :Pars1_R_5_min_2017_05_18_16_00_00__2017_05_19_01_00_00.csv) One line per time step Date; Rain rate in mm/h over 5 minutes ex : 2017-05-18 16:00:00;0.0 - Daily .npy files Each file contains a list. Each element is a list corresponding to a time steps of 30 s For a given time step the list, each element being a quantity measured by the disdrometer For PWS the elements of the list are the following: PWS[0] = Sensor ID PWS[1] = Average visibility (m) (meesage field 20) PWS[2] = Present Weather Code (WMO) (meesage field 21) PWS[3] = Present Weather Code (METAR) (meesage field 21) PWS[4] = Present Weather Code (NWS) (meesage field 23) PWS[5] = Alarms (message field (24)) PWS[6] = Fault status of PWS100 (message field 25) PWS[7] = Temperature (°C) (message field 30) PWS[8] = Sampled relative humidity (%) (message field 30) PWS[9] = Average wetbulb temperature (°C)(message field 30) PWS[10] = Maximum temperature (°C)(message field 31) PWS[11] = Minimum temperature (°C)(message field 31) PWS[12] = Precipitation rate (mm/h)(message field 40) PWS[13] = Precipitation accumulation (mm/h)(message field 40) PWS[14] = Drop size distribution (message field 42) PWS[15] = Average velocity (mm/s)(message field 43) PWS[16] = Average size (mm/h)(message field 43) PWS[17] = Type distribution (message field 44) PWS[18] = Campbell Scientifific standard size/velocity map (34*34) (message field 47) PWS[19] = Peak to pedestal ratio distribution histogram (message field 48) For the Parsivel the elements of the list are the following: Pars[0] = Sensor ID Pars[1] = Precipitation rate (mm/h) (message field 01) Pars[2] = Temperature in the sensor (°C) (message field 12) Pars[3] = OTT standard size/velocity map (32*32) (message field 93) - Daily .zip files Each file contains a the raw .txt files (one file for each 30s time step) of a day for a given disdrometer. These files are not to be used and simply consitute the raw data. To see their format, refer to the function Generation_daily_data_python_Carnot_1