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Retrieval of rain from Passive Microwave radiometers data has been achallenge ever since the launch of the first Defense Meteorological SatelliteProgram in the late 70s. Enormous progress has been made since the launch ofthe Tropical Rainfall Measuring Mission (TRMM) in 1997 but until recently thedata were processed pixel-by-pixel or taking a few neighboring pixels intoaccount. Deep learning has obtained remarkable improvement in the computervision field, and offers a whole new way to tackle the rain retrieval problem.The Global Precipitation Measurement (GPM) Core satellite carries similarly toTRMM, a passive microwave radiometer and a radar that share part of theirswath. The brightness temperatures measured in the 37 and 89 GHz channels areused like the RGB components of a regular image while rain rate from DualFrequency radar provides the surface rain. A U-net is then trained on thesedata to develop a retrieval algorithm: Deep-learning RAIN (DRAIN). With onlyfour brightness temperatures as an input and no other a priori information,DRAIN is offering similar or slightly better performances than GPROF, the GPMofficial algorithm, in most situations. These performances are assumed to bedue to the fact that DRAIN works on an image basis instead of the classicalpixel-by-pixel basis.

Evaluation of drain, a deep-learning approach to rain retrieval from gpm passive microwave radiometer