Evaluation of DRAIN, a Deep-Learning Approach to Rain Retrieval From GPM Passive Microwave Radiometer

Abstract

Retrieval of rain from passive microwave radiometer data has been a challenge ever since the launch of the first Defense Meteorological Satellite Program in the late 1980s. 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 similar to TRMM, a passive microwave radiometer, and a radar that shares 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). Using only the brightness temperatures from four channels as input and no other a priori information, DRAIN is offering similar or slightly better performances than goddard profiling algorithm (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.