Bayesian Deep Learning for Passive Microwave Precipitation Type Detection

Abstract

Precipitation is one of the primary drivers of earth’s water and energy cycles. While its role in the climate system is generally well-understood, our ability to accurately capture the spatio-temporal distribution of precipitation is limited. Satellite-borne passive microwave (PMW) radiometers and the associated research on their precipitation retrievals provide a highly accurate understanding of global total precipitation. Still, these precipitation retrievals are prone to large systematic errors at all spatial and temporal scales given their inability to accurately relate changes in precipitation intensity to the changes in radiometric signatures caused by variability in cloud system structure. Central to generating estimates of precipitation is proper classification of precipitation type, that is, convective or stratiform. This work presents a study on using a Bayesian deep learning (BDL) to help mitigate this problem by accurately classifying precipitation type and providing uncertainty in the classification. Specifically, it adopts a Bayesian form of Residual Networks (ResNet) architectures to extract the information from PMW observations vectors and identify these structural differences of cloud systems while providing, per pixel, classification uncertainty estimates. Benchmarked ResNet architectures in a deterministic configuration achieve accuracies above 86% on the classification task for precipitation type, while a Bayesian configuration of the same architectures reaches accuracy above 90%. Most importantly, uncertainty estimates are predicted by the Bayesian configuration to accompany each individual output value. This allows end-users to use them to calibrate the output by filtering the least certain predictions. The performance of the classifier, therefore, can be further improved depending on the application needs.