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.
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.
the Creative Commons Attribution 4.0 License.
Research article
| 
02 Sep 2022
Research article |
| 02 Sep 2022
| 02 Sep 2022
GPROF-NN: a neural-network-based implementation of the Goddard Profiling Algorithm
Simon Pfreundschuh
CORRESPONDING AUTHOR
Department of Space, Earth and Environment, Chalmers University of Technology, 41296 Gothenburg, Sweden
Department of Atmospheric Science, Colorado State University, Fort Collins, CO 80523, United States of America
Paula J. Brown
Department of Atmospheric Science, Colorado State University, Fort Collins, CO 80523, United States of America
Christian D. Kummerow
Department of Atmospheric Science, Colorado State University, Fort Collins, CO 80523, United States of America
Patrick Eriksson
Department of Space, Earth and Environment, Chalmers University of Technology, 41296 Gothenburg, Sweden
Teodor Norrestad
independent researcher
formerly at: Department of Space, Earth and Environment, Chalmers University of Technology, 41296 Gothenburg, Sweden
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Preprint withdrawn
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Robin Ekelund, Patrick Eriksson, and Michael Kahnert
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Raindrops become flattened due to aerodynamic drag as they increase in mass and fall speed. This study calculated the electromagnetic interaction between microwave radiation and non-spheroidal raindrops. The calculations are made publicly available to the scientific community, in order to promote accurate representations of raindrops in measurements. Tests show that the drop shape can have a noticeable effect on microwave observations of heavy rainfall.
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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.
The Global Precipitation Measurement mission is an international satellite mission providing...
