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