Articles | Volume 15, issue 17
https://doi.org/10.5194/amt-15-5033-2022
https://doi.org/10.5194/amt-15-5033-2022
Research article
 | 
02 Sep 2022
Research article |  | 02 Sep 2022

GPROF-NN: a neural-network-based implementation of the Goddard Profiling Algorithm

Simon Pfreundschuh, Paula J. Brown, Christian D. Kummerow, Patrick Eriksson, and Teodor Norrestad​​​​​​​

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Cited articles

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Berg, W.: GPM MHS on NOAA-18 Common Calibrated Brightness Temperature L1C 1.5 hours 17 km V07, Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/GPM/MHS/NOAA18/1C/07, 2022b. a
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The Global Precipitation Measurement mission is an international satellite mission providing regular global rain measurements. We present two newly developed machine-learning-based implementations of one of the algorithms responsible for turning the satellite observations into rain measurements. We show that replacing the current algorithm with a neural network improves the accuracy of the measurements. A neural network that also makes use of spatial information unlocks further improvements.