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Machine Learning based Precipitation Types Classification from GEO Satellite Observations: Diagnostic Model

The high spatial, temporal, and spectral resolutions from the new generation of GEO satellites provide opportunities to map precipitation more accurately and enhance our understanding of precipitation processes. The research question addressed in this study is: Which predictors derived from satellite observations are significant in estimating the occurrence of a given precipitation process? Several indices from the Advanced Baseline Imager (ABI) sensor onboard the Geostationary Observing Environmental Satellite (GOES)-16 are derived and matched with surface precipitation types from the Ground Validation Multi-Radar/Multi-Sensor (GV-MRMS) system across the conterminous United States (CONUS). A machine learning (ML) based Random Forest (RF) classification is developed with several categories of predictors, such as ABI brightness temperatures (Tb) from five channels, spectral channel differences and textures, and environmental variables from the Rapid Refresh numerical forecast model (NWP).