PRECIPITATION ESTIMATION FROM FY-3D MICROWAVE RADIATION IMAGER USING DEEP LEARNING

This paper develops a novel deep learning-based model for quantitative precipitation estimation (QPE). In this model, the multi-channel microwave Brightness Temperature (TBV and TBH) and Polarization Difference (PD = TBV - TBH) from the Microwave Radiation Imager (MWRI) aboard the FY-3D satellite are used as predictor variables for QPE. An observational database built from the Integrated Multisatellite Retrievals for GPM (IMERG) is employed as reference to train the deep learning model. In order to assess the model performance, an independent test dataset is used, and the results show that the model is computationally efficient and the derived QPE is highly consistent with IMERG product. The encouraging results also indicate that PD can enhance the precipitation estimation performance compared to using the TB only.