This paper presents an algorithm that relies on a series of dense and deep neural networks for passive microwave retrieval of precipitation. The neural networks learn from coincidences of brightness temperatures from the Global Precipitation Measurement (GPM) Microwave Imager (GMI) with the active precipitating retrievals from the Dual-frequency Precipitation Radar (DPR) onboard GPM as well as those from the {CloudSat} Profiling Radar (CPR). The algorithm first detects the precipitation occurrence and phase and then estimates its rate, while conditioning the results to some key ancillary information including parameters related to cloud microphysical properties. The results indicate that we can reconstruct the DPR rainfall and CPR snowfall with a detection probability of more than 0.95 while the probability of a false alarm remains below 0.08 and 0.03, respectively. Conditioned to the occurrence of precipitation, the unbiased root mean squared error in estimation of rainfall (snowfall) rate using DPR (CPR) data is less than 0.8 (0.1) mm/hr over oceans and land. Beyond methodological developments, comparing the results with ERA5 reanalysis and official GPM products demonstrates that the uncertainty in global satellite snowfall retrievals continues to be large while there is a good agreement among rainfall products. Moreover, the results indicate that CPR active snowfall data can improve passive microwave estimates of global snowfall while the current CPR rainfall retrievals should only be used for detection and not estimation of rates.

翻译：本文介绍了一个基于一系列密集和深层神经网络的算法,用于对降水进行被动的微波回收。神经网络从全球降水量测量(GPM)微波成像仪(GMI)从全球降水量测量(GMI)的亮度温度巧合中学习,而GPM上的双频降水雷达(DPR)和[CloudSat]分析雷达(CPR)的快速回溯率则从中学习。这一算法首先检测降水量和降水阶段,然后估计降水率,同时根据一些关键辅助信息,包括与云雾微物理特性有关的参数,对结果进行调节。结果表明,我们可以重建DPR降雨量和CPR降雪降雪量的巧合,探测概率大于0.95,而错误警报概率的可能性则分别低于0.08和0.03。考虑到降水量的发生情况,使用DPR(CPR)的降水量和降水量估算率的不偏差,只有0.8 (0.1毫米/小时) /小时。除了方法发展之外,将结果与ER5的降水量测量结果与ER值比,与ER5的降水量降水量和官方降水量测算的降水量测算结果,同时,不断进行。使用的全球降水量测算结果表明,全球降水量测算结果的可靠数据继续显示。