Crowdsourced Data Reveal Shortcomings in Precipitation Phase Products for Rain and Snow Partitioning

Abstract

Abstract Reanalysis products support our understanding of how the precipitation phase influences hydrology across scales. However, a lack of validation data hinders the evaluation of a reanalysis‐estimated precipitation phase. In this study, we used a novel dataset from the Mountain Rain or Snow (MRoS) citizen science project to compare 39,680 MRoS observations from January 2020 to July 2023 across the conterminous United States (CONUS) to assess three precipitation phase products. These products included the Global Precipitation Measurement (GPM) mission Integrated Multi‐satellitE Retrievals for GPM (IMERG), the Modern‐Era Retrospective Analysis for Research and Applications (MERRA‐2), and the North American Land Data Assimilation System (NLDAS‐2). The overall critical success indices for detecting rainfall (snowfall) for IMERG, MERRA‐2, and NLDAS‐2 were 0.51 (0.79), 0.49 (0.77), and 0.54 (0.53), respectively. These indices show that IMERG and MERRA‐2 reasonably classify snowfall, whereas NLDAS‐2 overestimates rainfall. All products performed poorly in detecting subfreezing rainfall and snowfall above 2°C. Therefore, crowdsourced data provides a unique validation source to improve the capabilities of reanalysis products.