  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.
