Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 15, issue 17
Articles | Volume 15, issue 17
https://doi.org/10.5194/amt-15-5033-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-15-5033-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
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​​​​​​​

Data sets

GPM GMI Common Calibrated Brightness Temperatures Collocated L1C 1.5 hours 13 km V07 W. Berg https://doi.org/10.5067/GPM/GMI/GPM/1C/07

GPM MHS on NOAA-18 Common Calibrated Brightness Temperature L1C 1.5 hours 17 km V07 W. Berg https://doi.org/10.5067/GPM/MHS/NOAA18/1C/07

GPM DPR and GMI Combined Precipitation L2B 1.5 hours 5 km V07 W. Olson https://doi.org/10.5067/GPM/DPRGMI/CMB/2B/07

MRMS QPE Multi-RADAR Multi-Sensor Archiving https://mtarchive.geol.iastate.edu/2022/01/01/mrms/ncep/PrecipRate/

Model code and software

GPROF-NN: A neural network based implementation of the Goddard Profiling Algorithm S. Pfreundschuh https://doi.org/10.5281/zenodo.5819297

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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.
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