Retrieval of rain from Passive Microwave radiometers data has been a challenge ever since the launch of the first Defense Meteorological Satellite Program in the late 70s. Enormous progress has been made since the launch of the Tropical Rainfall Measuring Mission (TRMM) in 1997 but until recently the data were processed pixel-by-pixel or taking a few neighboring pixels into account. Deep learning has obtained remarkable improvement in the computer vision field, and offers a whole new way to tackle the rain retrieval problem. The Global Precipitation Measurement (GPM) Core satellite carries similarly to TRMM, a passive microwave radiometer and a radar that share part of their swath. The brightness temperatures measured in the 37 and 89 GHz channels are used like the RGB components of a regular image while rain rate from Dual Frequency radar provides the surface rain. A U-net is then trained on these data to develop a retrieval algorithm: Deep-learning RAIN (DRAIN). With only four brightness temperatures as an input and no other a priori information, DRAIN is offering similar or slightly better performances than GPROF, the GPM official algorithm, in most situations. These performances are assumed to be due to the fact that DRAIN works on an image basis instead of the classical pixel-by-pixel basis.

翻译：自20世纪70年代末首颗国防气象卫星计划（DMSP）卫星发射以来，利用被动微波辐射计数据进行降雨反演始终是一项挑战。自1997年热带降雨测量任务（TRMM）发射以来，该领域取得了巨大进展，但直到近期，数据处理仍主要基于逐像元或少量邻域像元。深度学习在计算机视觉领域取得了显著突破，为降雨反演问题提供了全新思路。全球降水测量（GPM）核心卫星搭载了与TRMM类似的被动微波辐射计和共用部分扫描幅宽的双频雷达。本研究将37 GHz和89 GHz通道观测的亮温数据类比为常规图像的RGB分量，以双频雷达获取的地表降雨率作为地面真值，基于U-Net架构训练反演算法——深度降雨（DRAIN）。仅以四个通道亮温为输入，无需任何先验信息，DRAIN在大多数场景下表现出与GPM官方算法GPROF相当或略优的性能。该优势归因于DRAIN基于图像整体而非传统逐像元的工作机制。