Reconstruction of 3D precipitation measurements from FY-3G MWRI-RM imaging and sounding channels

Abstract. FengYun 3G satellite (FY-3G), China’s first precipitation measurement satellite, was launched on April 17, 2023. FY-3G carries an advanced multi-channel microwave radiance imager-rainfall measurement (MWRI-RM) system, which, compared to the previous GPM/GMI, includes more sounding channels. Additionally, a Ka/Ku-band dual-frequency precipitation measurement radar (PMR) onboard FY-3G provides 3D observations of severe precipitation systems. Due to the high cost and hardware limitations of precipitation radars, most precipitation-affected satellite observations rely on passive data. Deep learning methods have become effective tools to bridge these two types of observations. In this study, we proposed a deep convolutional neural network (CNN) to reconstruct PMR-Ku reflectivity profiles (VPR) based on MWRI-RM multi-channel radiances across different precipitation scenarios and analyzed the effects of dual oxygen absorption sounding channels and polarization differences (PD) on reconstruction outcomes. Experiments showed that dual oxygen absorption sounding channels improved VPR accuracy, especially over land, reducing RMSE by 17.42 %. Including PD further enhanced accuracy, reducing RMSE by 23.54 %, while also demonstrating excellent capability in precipitation identification, achieving an F1 score of 0.904. Applying the models to Typhoon Khanun and the extreme precipitation event in Beijing further demonstrated the benefits of dual oxygen sounding channels and PD, even for reflectivity contaminated by ground clutter.