A Deep Learning Architecture for Passive Microwave Precipitation Retrievals using CloudSat and GPM Observations

摘要: The Bayesian passive microwave retrievals of precipitation often rely on mathematical matching of the observed vectors of brightness temperature with an a-priori database of precipitation profiles and their corresponding brightness temperatures. Mathematical proximity does not necessarily lead to consistent retrievals due to limited information content of passive microwave observations. This paper defines imposter (genuine) vectors of brightness temperature as those that are mathematically close but physically inconsistent (consistent) and characterizes them through the Silhouette Coefficient (SC) analysis and the Neyman-Pearson (NP) hypothesis testing based on their associated values of cloud ice (IWP) and liquid water path (LWP), given by coincidences of CloudSat Profiling Radar (CPR) and the Global Precipitation Measurement (GPM) Microwave Imager (GMI). To cope with this challenge, a Deep …