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

Global surface snowfall rate estimation is crucial for hydrological and meteorological applications but is still a challenging task. A novel approach is developed to comprehensively use passive microwave, infrared data and physical constraints in deep-learning neural networks with an attention module for retrieving surface snowfall rate (PCSSR-DNNWA). The PCSSR-DNNWA consistently outperforms traditional approaches in predicting surface snowfall rates with a correlation coefficient of & SIM;0.76, mean error of & SIM;-0.02 mm/hr, and root mean squared error of & SIM;0.21 mm/hr. It is found that graupel water path is of vital importance with largest contributions in retrieving surface snowfall rate. By integrating the physical constraints, the algorithm of PCSSR-DNNWA opens a new avenue for retrieving the surface snowfall rate from satellites since some predictors are intelligently considered, resulting in an increased accuracy, interpretability, and computational efficiency.