Overview This dataset provides annual global maps of seasonal snow cover metrics derived from the MODIS MOD10A2 product for water years 2015–2024. For each year, the dataset includes: Maximum number of consecutive snow days for each pixel [max_consec_snow_days]. Date of snow appearance / first day of snow cover for the max snow cover snow period [SAD_DOWY]. Date of snow disappearance / first day of no snow cover for the max snow snow period [SDD_DOWY]. Data Details Product Source: MODIS MOD10A2 (Microsoft Planetary Computer) Processing: Cloud-filled and darkness-corrected using methods similar to Wrzesien et al. (2019), with pixelwise seasonal metrics aggregated by water year (Oct–Sep NH, Apr–Mar SH). Format: Cloud-optimized Zarr archive, compressed as .tar.lzma Spatial Coverage: Global land areas, MODIS grid tiles overlapping land Temporal Coverage: Water Years 2015–2024 Variables: SAD_DOWY, SDD_DOWY, max_consec_snow_days (per water year) note that SAD_DOWY and SDD_DOWY dates are represented as day of water year, e.g. DOWY 1 in NH is October 1st Projection: MODIS Sinusoidal Grid Data type: int16 Nodata: -32768 Example Usage The zarr dataset is stored here as a compressed tar.lzma archive. First decompress the archive. Below is an example of how to open and use the data in Python: import xarray as xrimport rioxarray # 1. Open the global snow cover Zarr datasetglobal_snow_cover_ds = xr.open_zarr("global_modis_snow_cover.zarr", decode_coords="all", consolidated=True) # 2. Clip to a bounding box around Mount Rainier in EPSG:4326 (lon/lat)rainier_snow_cover_ds = global_snow_cover_ds.rio.clip_box( minx=-121.95, miny=46.7, maxx=-121.45, maxy=46.95, crs="EPSG:4326") # 3. Select the 'max_consec_snow_days' variable for Water Year 2020rainier_max_snow_days_2020_da = rainier_snow_cover_ds['max_consec_snow_days'].sel(water_year=2020) # 4. Reproject out of weird MODIS sinusoidal grid to UTM Zone 10Nrainier_max_snow_days_2020_da_utm = rainier_max_snow_days_2020_da.rio.reproject("EPSG:32610") # 5. Plot the resultrainier_max_snow_days_2020_da_utm.plot.imshow(cmap='viridis', vmin=0, vmax=365) Processing pipelineThe dataset was generated using a reproducible, open-source workflow available at:https://github.com/egagli/MODIS_seasonal_snow_maskKey steps include: MODIS MOD10A2 (8 day maximum snow extent) data ingestion, cloud and darkness gap-filling, per-pixel snow metric extraction, and writing to a global Zarr store. Resources MODIS MOD10A2 Product Guide: https://nsidc.org/sites/default/files/mod10a2-v006-userguide_1.pdf Wrzesien et al. (2019): https://doi.org/10.1029/2019WR024908