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​​​​​​​


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Simon Pfreundschuh on behalf of the Authors (12 Jul 2022)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (14 Jul 2022) by Marloes Penning de Vries

Post-review adjustments

AA: Author's adjustment | EA: Editor approval
AA by Simon Pfreundschuh on behalf of the Authors (01 Sep 2022)   Author's adjustment   Manuscript
EA: Adjustments approved (01 Sep 2022) by Marloes Penning de Vries
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.