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Monitoring changes of precipitation phase from space is important forunderstanding the mass balance of Earth's cryosphere in a changing climate.This paper examines a Bayesian nearest neighbor approach for prognosticdetection of precipitation and its phase using passive microwave observationsfrom the Global Precipitation Measurement (GPM) satellite. The method uses theweighted Euclidean distance metric to search through an a priori databasepopulated with coincident GPM radiometer and radar observations as well asancillary snow-cover data. The algorithm performance is evaluated using datafrom GPM official precipitation products, ground-based radars, andhigh-fidelity simulations from the Weather Research and Forecasting model.Using the presented approach, we demonstrate that the hit probability ofterrestrial precipitation detection can reach to 0.80, while the probability offalse alarm remains below 0.11. The algorithm demonstrates higher skill indetecting snowfall than rainfall, on average by 10 percent. In particular, theprobability of precipitation detection and its solid phase increases by 11 and8 percent, over dry snow cover, when compared to other surface types. The mainreason is found to be related to the ability of the algorithm in capturing thesignal of increased liquid water content in snowy clouds over radiometricallycold snow-covered surfaces

A Nested K-Nearest Prognostic Approach for Microwave Precipitation Phase Detection over Snow Cover