A New GPM-DPR Algorithm to Estimate Snowfall in Mountain Regions

Abstract

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.