Surface snowfall rate estimates from the Global Precipitation Measurement (GPM) mission’sCoreObservatorysensors and theCloudSatradar are compared to those from the Multi-Radar Multi-Sensor (MRMS) radarcomposite product over the continental United States during the period from November 2014 to September 2020. Theanalysis includes the Dual-Frequency Precipitation Radar (DPR) retrieval and its single-frequency counterparts, the GPMCombined Radar Radiometer Algorithm (CORRA), theCloudSatSnow Profile product (2C-SNOW-PROFILE), and twopassive microwave retrievals, i.e., the Goddard Profiling algorithm (GPROF) and the Snow Retrieval Algorithm for GMI(SLALOM). The 2C-SNOW retrieval has the highest Heidke skill score (HSS) for detecting snowfall among the productsanalyzed. SLALOM ranks second; it outperforms GPROF and the other GPM algorithms, all detecting only 30% of thesnow events. Since SLALOM is trained with 2C-SNOW, it suggests that the optimal use of the information content in theGMI observations critically depends on the precipitation training dataset. All the retrievals underestimate snowfall ratesby a factor of 2 compared to MRMS. Large discrepancies (RMSE of 0.7–1.5 mm h21) between spaceborne and ground-based snowfall rate estimates are attributed to the complexity of the ice scattering properties and to the limitations of theremote sensing systems: the DPR instrument has low sensitivity, while the radiometric measurements are affected by theconfounding effects of the background surface emissivity and of the emission of supercooled liquid droplet layers
