Snow Variables for High Mountain Asia

Data associated with the paper: Smith T and Bookhagen B (2020) Assessing Multi-Temporal Snow-Volume Trends in High Mountain Asia From 1987 to 2016 Using High-Resolution Passive Microwave Data. Front. Earth Sci. 8:559175. doi: 10.3389/feart.2020.559175 ( https://doi.org/10.3389/feart.2020.559175 ) This data resource contains one NetCDF file containing high-resolution (3.125km) snow-water equivalent and snow-cover parameters. The named datasets within the NetCDF file are: Annual_SWE_Trend_1987-2016 - Annual average snow-water equivalent trend (1987-2016) DJF_SWE_Trend_1987-2016 - December-January-February average snow-water equivalent trend (1987-2016) MAM_SWE_Trend_1987-2016 - March-April-May average snow-water equivalent trend (1987-2016) JJA_SWE_Trend_1987-2016 - June-July-August average snow-water equivalent trend (1987-2016) SON_SWE_Trend_1987-2016 - September-October-November average snow-water equivalent trend (1987-2016) Annual_SWE_Trend_1987-1997 - Annual average snow-water equivalent trend (1987-1997) Annual_SWE_Trend_1997-2007 - Annual average snow-water equivalent trend (1997-2007) Annual_SWE_Trend_2006-2016 - Annual average snow-water equivalent trend (2006-2016) Annual_Average_SWE - Annual average snow-water equivalent (1987-2016) DJF_Average_SWE - December-January-February average snow-water equivalent (1987-2016) MAM_Average_SWE - March-April-May average snow-water equivalent (1987-2016) JJA_Average_SWE - June-July-August average snow-water equivalent (1987-2016) SON_Average_SWE - September-October-November average snow-water equivalent (1987-2016) Annual_Average_SCA - Annual average snow-covered area (2001-2019) DJF_Average_SCA - December-January-February average snow-covered area (2001-2019) MAM_Average_SCA - March-April-May average snow-covered area (2001-2019) JJA_Average_SCA - June-July-August average snow-covered area (2001-2019) SON_Average_SCA - September-October-November average snow-covered area (2001-2019) The NetCDF file also contains projected x/y coordinates in EASEgrid 2.0, as well as relevant geographic and projection parameters. These data provide high-resolution averages and trends of key snow parameters for analyzing climate change in High Mountain Asia. The underlying data sources are: Brodzik, M. J., D. G. Long, M. A. Hardman, A. Paget, and R. Armstrong. 2016, Updated 2020. MEaSUREs Calibrated Enhanced-Resolution Passive Microwave Daily EASE-Grid 2.0 Brightness Temperature ESDR, Version 1. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: https://doi.org/10.5067/MEASURES/CRYOSPHERE/NSIDC-0630.001. and: Hall, D. K. and G. A. Riggs. 2016. MODIS/Terra Snow Cover Daily L3 Global 500m SIN Grid, Version 6. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: https://doi.org/10.5067/MODIS/MOD10A1.006. Quick python script for converting to GeoTIFF: import xarray as xr import rioxarray ds = xr.open_dataset('SWE_Variables_HMA.nc') save_loc = 'Annual_SWE_Trend.tif' da = ds['Annual_SWE_Trend_1987-2016'] da = da.rio.set_crs(ds.crs) da.rio.to_raster(save_loc