Abstract
Improvements in remote sensing capability and improvements in artificial
intelligence have created significant opportunities to advance
understanding of precipitation processes. While highly advanced Machine
Learning (ML) techniques improve the accuracy of precipitation
retrievals, how these observations contribute to our understanding of
precipitation processes remains an underexplored research question. In a
companion manuscript, a precipitation type prognostic ML model is
developed by deriving predictors from the Advanced Baseline Imager (ABI)
sensor onboard Geostationary Observing Environmental Satellite
(GOES)-16. In this study, these predictors are linked to different
precipitation processes. It is observed that satellite observations are
important in separating Rain and No-Rain areas. For stratiform
precipitation types, predictors related to atmospheric moisture content,
such as relative humidity and precipitable water, are the most important
predictors, while for convective types, predictors such as 850-500hPa
lapse-rate and Convective Available Potential Energy (CAPE) are more
important. The diagnostic analysis confirms the benefit of spatial
textures derived from ABI observations to improve the classification
accuracy. It is recommended to combine the heritage water vapor channel
T6.2 with the IR T11.2 channel for improved precipitation
classification. There is more than 10% improvement in detection of
stratiform and tropical precipitation types compared to using T11.2
alone.