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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.

Precipitation is a critical component in water, energy and biogeochemical cycles (Kidd & Huffman, 2011;Ma, Xu, et al., 2022;Xu et al., 2022) and snowfall predominates over other types of precipitation in high-latitude regions (Skofronick-Jackson et al., 2015;Xiong et al., 2022). Today, satellite remote sensing is the primary observational source for gathering data on worldwide rainfall and snowfall since meteorological stations, ocean buoys, and weather radars have limited coverage (Hong et al., 2007;Ma, Zhu, & Yang, 2022;. Retrieving surface snowfall rate (SSR) using space-borne passive microwave sensors equipped with high-frequency channels (90-190 GHz) remains a very challenging task (Levizzani et al., 2011;Skofronick-Jackson et al., 2017), despite their high sensitivity to the radiation scattering by ice hydrometeors. The complexity of surface snowfall rate estimation is due to several factors: (a) the presence of snow cover on the ground complicates the separation between atmospheric snowfall and snow cover contributions to the satellite-observed brightness temperature (Kongoli et al., 2003); (b) the wide variation in particle size and shape of snowflakes suggests complicated radiative signatures (Liu & Seo, 2013); (c) the diversity of weather systems found in higher latitudes also contributes to the complexity of surface snowfall rate estimation (Kongoli et al., 2015).Over the past years, great efforts have been paid to develop algorithms for the detection and retrieval of snowfall rate (

Precipitation in different phases has varying effects on runoff. However, monitoring surface snowfall poses a significant challenge, highlighting the importance of developing a snowfall detection algorithm. The objective of this study is develop a snowfall detection algorithm for the Microwave Temperature Sounder-2 (MWTS-II) and the Microwave Humidity Sounder-2 (MWHS-II) onboard the FY-3D satellite while considering the differentiated atmosphere temperature conditions. The results show that: (1) The brightness temperature (TB) of MWTS Channel 3 is well-suited for pre-classifying atmospheric temperatures, and significant differences in TB distribution exist between the two pre-classification subsets. (2) Among six machine classifiers examined, the random forest classifier exhibits favorable classification performance on both the validation set (accuracy: 0.76, recall: 0.76, F1 score: 0.75) and test set (accuracy: 0.80, recall: 0.44, F1 score: 0.44). (3) The application of the snowfall detection algorithm showcases a reasonable spatial distribution and outperforms the IMERG and ERA5 snowfall data.