Exploring Deep Neural Networks to Retrieve Rain and Snow in High Latitudes Using Multisensor and Reanalysis Data

Satellite remote sensing is able to provide information on global rain and snow, but challenges remain in accurate estimation of precipitation rates, particularly in snow retrieval. In this work, the deep neural network (DNN) is applied to estimate rain and snow rates in high latitudes. The reference data for DNN training are provided by two spaceborne radars onboard the Global Precipitation Measurement (GPM) Core Observatory and CloudSat. Passive microwave data from the GPM Microwave Imager (GMI), infrared data from MODerate resolution Imaging Spectroradiometer and environmental data from European Centre for Medium-Range Weather Forecasts are trained to the spaceborne radar-based reference precipitation. The DNN estimates are compared to data from the Goddard Profiling Algorithm (GPROF), which is used to retrieve passive microwave precipitation for the GPM mission. First, the DNN-based retrieval method performs well in both training and testing periods. Second, the DNN can reveal the advantages and disadvantages of different channels of GMI and MODerate resolution Imaging Spectroradiometer. Additionally, infrared and environmental data can improve precipitation estimation of the DNN, particularly for snowfall. Finally, based on the optimized DNN, rain and snow are estimated in 2017 from orbital GMI brightness temperatures and compared to ERA-Interim and Modern-Era Retrospective analysis for Research and Applications Version 2 reanalysis data. Evaluation results show that (1) the DNN can largely mitigate the underestimation of precipitation rates in high latitudes by GPROF; (2) the DNN-based snowfall estimates largely outperform those of GPROF; and (3) the spatial distributions of DNN-based precipitation are closer to reanalysis data. The method and assessment presented in this study could potentially contribute to the substantial improvement of satellite precipitation products in high latitudes.