https://doi.org/10.1029/2024JD041481

Reliable precipitation forcing is essential for calculating the water balance and other hydrological
variables. However, satellite precipitation is often the only forcing available to run hydrological models in data‐
scarce regions compromising hydrological calculations. The Integrated Multi‐satellitE Retrievals for GPM
(IMERG) product estimates precipitation from passive microwave and infrared satellites, which are
intercalibrated based on Global Precipitation Measurement (GPM)'s Dual‐frequency Precipitation Radar (DPR)
and GPM Microwave Image (GMI) instruments. GPM‐DPR radar algorithms have a limited consideration of
particle size distribution (PSD), attenuation correction, and ground clutter, resulting in snowfall estimation
degradation especially in mountain regions. This study aims to improve satellite radar snowfall for this situation.
Nearly 2 years (2019–2022) of aloft precipitation concentration, surface hydrometeor size, number and fall
velocity, and surface precipitation rate from a Canadian Rockies high‐elevation site and collocated GPM‐DPR
reflectivities were used to develop a new snowfall estimation algorithm. Snowfall estimates using the new
algorithm and measured GPM‐DPR reflectivities were compared to other GPM‐DPR‐based products, including
the combined radar‐radiometer algorithm (CORRA), which was employed to intercalibrate IMERG. Snowfall
rates estimated with measured Ka reflectivities, and from CORRA were compared to Micro Rain Radar‐2
(MRR‐2) observations and had correlation, bias, and RMSE of 0.58 and 0.07, 0.43, and 0.38 mm hr 1, and
0.83 and 0.85 mm hr 1, respectively. Predictions using measured Ka reflectivity suggest that enhanced satellite
radar snowfall estimates can be achieved using a simple measured reflectivity algorithm. These improved
snowfall estimates can be adopted to intercalibrate IMERG in cold mountain regions, thereby improving
precipitation estimates.

https://doi.org/10.1029/2024JD041481