PCSSR-DNNWA: A Physical Constraints Based Surface Snowfall Rate Retrieval Algorithm Using Deep Neural Networks With Attention Module

Abstract

Global surface snowfall rate estimation is crucial for hydrological and meteorological applications but is still a challenging task. We present a novel approach to comprehensively consider passive microwave, infrared and physical constraints using deep neural networks with attention module for retrieving surface snowfall rate, namely PCSSR-DNNWA. PCSSR-DNNWA outperforms traditional approaches in predicting surface snowfall rate with CC ~ 0.75, ME ~ -0.03 mm/h, and RMSE ~ 0.21 mm/h. In addition, we found that graupel water path (GWP) is of vital importance with largest contributions in retrieving surface snowfall rate. Integrating the physical constraints, PCSSR-DNNWA paves a new avenue for retrieving satellite-borne surface snowfall rate by intelligently considering the varying importance of the multiple predictors, resulting in increased accuracy, interpretability, and computational efficiency.