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. 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.