Articles | Volume 15, issue 17
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

Ba, J. L., Kiros, J. R., and Hinton, G. E.: Layer normalization, arXiv [preprint],, 21 July 2016. a
Berg, W.: GPM GMI Common Calibrated Brightness Temperatures Collocated L1C 1.5 hours 13 km V07, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set],, 2022a. a
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],, 2022b. a
Bonsignori, R.: The Microwave Humidity Sounder (MHS): in-orbit performance assessment, in: Sensors, systems, and next-generation satellites XI, International Society for Optics and Photonics, vol. 6744, p. 67440A, 2007. a
Boukabara, S.-A., Garrett, K., Chen, W., Iturbide-Sanchez, F., Grassotti, C., Kongoli, C., Chen, R., Liu, Q., Yan, B., Weng, F., Ferraro, R., Kleespies, T. J., and Meng, H.: MiRS: An All-Weather 1DVAR Satellite Data Assimilation and Retrieval System, IEEE T. Geosci. Remote, 49, 3249–3272,, 2011. a
Short summary
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.