Snowfall Regime Classification: Application of a Machine Learning Classifier to Passive Microwave Observations

Several studies demonstrated the importance of snowfall regime identification when retrieving snowfall rate from Passive Microwave (PMW) observations. Whether a precipitation algorithm is based on a-priori or training references, it is crucial to build complete and representative datasets to correctly detect and quantify snowfall from spaceborne sensors. Within the Global Precipitation Measurement (GPM) mission, the Goddard PROFiling (GPROF) algorithm snowfall retrieval is investigated. A combined CloudSat-GPM dataset is used to build a training dataset for an eXtreme Gradient Boost (XGB) model in which the GPM Microwave Imager (GMI) brightness temperatures are associated with a Cloud Profiling Radar (CPR) snowfall regime, classifying the observed scene into ‘dry’ (no precipitation detected), ‘shallow convective’, ‘deep stratiform’ or ‘other’ snowfall class. The Machine Learning (ML) approach is crucial to interpret strong but complex relationships between PMW signals within the atmosphere and snowfall regimes at the surface. The ML classifier training is performed using a CloudSat classifying technique, based on snowing profiles and cloud classification, and applied to GPROF. A couple of case studies will be presented to show the benefits of classifying the snowfall regime for PMW snowfall retrievals.