----------------- The README describes ground-based precipitation radar datasets (two sites in Canada) used to study the glaciation of supercooled liquid-water clouds downwind anthropogenic air pollution sources. Additional information Jorma Rahu Jorma.Rahu@ut.ee Daniel Michelson Daniel.Michelson@ec.gc.ca Velle Toll Velle.Toll@ut.ee This work is funded by the Estonian Research Council grant PRG1726. ----------------- DATASET ----------------- The dataset contains: 1) Radar volume files in ODIM_H5 version 2.2 format; 2) Hydrometeor classification product (PARCA) output from radar data in GIF format; 3) Polygon cut-out coordinates of glaciation-affected areas as TXT files; 4) Analysed radar data in CSV format; 5) Radar cases with no precipitation near industrial air pollution hot spots (CSV format); 6) Radar reflectivity animations (mp4 format); 7) Animations of GOES ABI satellite imagery with radar reflectivity images overlayed (mp4 format); 8) Python code (Jupyter Notebooks) to visualise and process radar data. The datasets describe documented cases of glaciation of supercooled liquid-water clouds downwind of industrial air pollution sources in Regina and Rouyn-Noranda, Canada. Precipitation is seen in ground-based weather radar data. Weather radar volume data is from two radar sites: 1) Bethune, Saskatchewan, Canada; (lat/lon: 50.57123, -105.18285); dual-polarisation data since 09.08.2019 2) Landrienne, Québec, Canada; (lat/lon: 48.55135, -77.80808); dual-polarisation data since 31.10.2019 To calculate snow water equivalent snowfall rates S [mm/h] from the radar reflectivity Z [mm^6/m^3] we used the following Z-S relationship: Z = 399 * S^2.21. And to calculate snowfall rates S [cm/h] representative of the depth of fresh snow on the ground from the radar reflectivity Z [mm^6/m^3] we used the same Z-S relationship: Z = 399 * S^2.21 (only the units of S are different). Creators: Jorma Rahu, Tanel Voormansik, Daniel Michelson, Emma Hung, Norman Donaldson, Velle Toll Rights-holder: datasets 1 and 2 are subject to Crown Copyright of the Government of Canada. Rights-holder: University of Tartu for datasets 3, 4, 5, 6, 7, 8. ----------------- TERMS OF USE ----------------- Datasets are licensed by the rights-holders under a Creative Commons Attribution 4.0 International License: https://creativecommons.org/licenses/by/4.0/. ----------------- CONTENTS ----------------- Abbreviations: ?? - Radar site name LA (Landrienne) or BE (Bethune) yyyy - full year with 4 digits mm - zero-padded month dd - zero-padded day HH - zero-padded hour MM - zero-padded minute 1) 1_Radar_Volumes.zip Radar volume files are distributed as ODIM_H5 version 2.2 files. Full documentation can be found: https://www.eumetnet.eu/wp-content/uploads/2019/05/OPERA-ODIM_H5-v2.2.pdf The filename for single polarisation files: X??yyyymmddHHMM~~DOPVOL1_A.h5 The filename for dual polarisation files: yyyymmddHH_MM_ODIMH5_PVOL6S_VOL_CAS??.h5 2) 2_Hydrometeor_Classification_PARCA.zip Processed hydrometeor classification products as GIF images with radar site in the centre. Structure of filenames: CAS??_yyyymmddHHMM_PARCA_VOL_04.gif Different classes are: RH - rain / hail mixture +RA - heavy rain RA - moderate rain BD - big drops GS - graupel IC - ice crystals WS - wet snow DS - dry snow BI - biological scatterers CL - gound clutter/anomalous propagation UN - unknown 3) 3_Polygons.zip Coordinates for polygons of precipitation areas downwind industrial air pollution sites Structure of filenames: ??yyyymmddHH_MM_polygon.txt Files contain two columns (longitude and latitude coordinates) including header row: lon lat 4) 4_Analysed_radar_data.CSV Analysed radar data are described in a CSV file with 11 columns: Date (yyyy.mm.dd) Radar site (CASLA and CASBE refer to dual polarisation radars) Glaciation-enhanced precipitation (boolean) Start time of enhanced-precip (HH:MM) End time of enhanced-precip (HH:MM) Peak time of precipitation (HH:MM) Peak time mean snow rate (mm/h) Peak time median snow rate (mm/h) Accumulation timeperiod (HH:MM) Accumulated snow over accumulation timeperiod (mm) Accumulation area (km^2) 5) 5_NoPrecip_Radar.CSV In many cases, glaciation events (glaciation of supercooled liquid-water clouds downwind anthropogenic air pollution sources) were identified in satellite imagery, but no precipitation was seen in weather radar data. CSV file contains 2 columns: Date (yyyy.mm.dd) Radar site (CASLA and CASBE refer to dual polarisation radars) 6) 6_Radar_Animations.zip Animations of radar reflectivity. In all animations, the white dot marks the radar location and the red dot marks the studied industrial site. Structure of filenames: ??_DBZH_yy_mm_dd.mp4 7) 7_GOES_and_Radar_Animations.zip Animations of a) True colour composite images from GOES ABI satellite data (left panel), b) True colour composite images from GOES ABI satellite data with radar reflectivity overlayed (middle panel), c) Night microphysics composite images from GOES ABI satellite data (right panel). Structure of filenames: yy_mm_dd.mp4 In all animations, the blue cross (x) marks the radar location and the red dot marks the studied industrial site. 8) 8_python_code.zip Jupyter notebooks to plot Canadian radar data (plot_canada_radar.ipynb) and to process the radar data (clickit_accumulation.ipynb). The accumulation notebook (clickit_accumulation.ipynb) processes raw radar data, accumulates radar data over the period of enhanced precipitation downwind industrial air pollution source, helps to select and save the polygons with anthropogenically enhanced precipitation, clips and processes radar data inside the hand-logged polygons.