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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Cited
14 citations as recorded by crossref.
- Multiscale and Multilevel Feature Fusion Network for Quantitative Precipitation Estimation With Passive Microwave Z. Wang et al. 10.1109/TGRS.2024.3396379
- Understanding Regional Passive Microwave Precipitation Bias Using Radar-Derived Information E. Goldenstern & C. Kummerow 10.1109/TGRS.2024.3470552
- Unsupervised Domain Adaptation to Mitigate Out-of-Distribution Problem of Spatial Radiometer Images: Application to Quantitative Precipitation Estimation V. Sambath et al. 10.1109/TGRS.2024.3403373
- The Chalmers Cloud Ice Climatology: retrieval implementation and validation A. Amell et al. 10.5194/amt-17-4337-2024
- Synergistic Retrievals of Ice Cloud Microphysics by Spaceborne Submillimeter and Infrared Observations S. Li et al. 10.1109/TGRS.2024.3453248
- Precipitation Retrieval from FY-3G/MWRI-RM Based on SMOTE-LGBM Y. Lv et al. 10.3390/atmos15111268
- GPROF V7 and beyond: assessment of current and potential future versions of the GPROF passive microwave precipitation retrievals against ground radar measurements over the continental US and the Pacific Ocean S. Pfreundschuh et al. 10.5194/amt-17-515-2024
- The Ice Cloud Imager: retrieval of frozen water column properties E. May et al. 10.5194/amt-17-5957-2024
- Constraining the Multiscale Structure of Geophysical Fields in Machine Learning: The Case of Precipitation C. Guilloteau et al. 10.1109/LGRS.2023.3284278
- A New Deep-Learning-Based Framework for Ice Water Path Retrieval From Microwave Humidity Sounder-II Aboard FengYun-3D Satellite W. Wang et al. 10.1109/TGRS.2024.3352654
- Assessing sampling and retrieval errors of GPROF precipitation estimates over the Netherlands L. Bogerd et al. 10.5194/amt-17-247-2024
- A random forest algorithm for the prediction of cloud liquid water content from combined CloudSat–CALIPSO observations R. Schulte et al. 10.5194/amt-17-3583-2024
- GPROF V7 and beyond: assessment of current and potential future versions of the GPROF passive microwave precipitation retrievals against ground radar measurements over the continental US and the Pacific Ocean S. Pfreundschuh et al. 10.5194/amt-17-515-2024
- GPROF-NN: a neural-network-based implementation of the Goddard Profiling Algorithm S. Pfreundschuh et al. 10.5194/amt-15-5033-2022
12 citations as recorded by crossref.
- Multiscale and Multilevel Feature Fusion Network for Quantitative Precipitation Estimation With Passive Microwave Z. Wang et al. 10.1109/TGRS.2024.3396379
- Understanding Regional Passive Microwave Precipitation Bias Using Radar-Derived Information E. Goldenstern & C. Kummerow 10.1109/TGRS.2024.3470552
- Unsupervised Domain Adaptation to Mitigate Out-of-Distribution Problem of Spatial Radiometer Images: Application to Quantitative Precipitation Estimation V. Sambath et al. 10.1109/TGRS.2024.3403373
- The Chalmers Cloud Ice Climatology: retrieval implementation and validation A. Amell et al. 10.5194/amt-17-4337-2024
- Synergistic Retrievals of Ice Cloud Microphysics by Spaceborne Submillimeter and Infrared Observations S. Li et al. 10.1109/TGRS.2024.3453248
- Precipitation Retrieval from FY-3G/MWRI-RM Based on SMOTE-LGBM Y. Lv et al. 10.3390/atmos15111268
- GPROF V7 and beyond: assessment of current and potential future versions of the GPROF passive microwave precipitation retrievals against ground radar measurements over the continental US and the Pacific Ocean S. Pfreundschuh et al. 10.5194/amt-17-515-2024
- The Ice Cloud Imager: retrieval of frozen water column properties E. May et al. 10.5194/amt-17-5957-2024
- Constraining the Multiscale Structure of Geophysical Fields in Machine Learning: The Case of Precipitation C. Guilloteau et al. 10.1109/LGRS.2023.3284278
- A New Deep-Learning-Based Framework for Ice Water Path Retrieval From Microwave Humidity Sounder-II Aboard FengYun-3D Satellite W. Wang et al. 10.1109/TGRS.2024.3352654
- Assessing sampling and retrieval errors of GPROF precipitation estimates over the Netherlands L. Bogerd et al. 10.5194/amt-17-247-2024
- A random forest algorithm for the prediction of cloud liquid water content from combined CloudSat–CALIPSO observations R. Schulte et al. 10.5194/amt-17-3583-2024
2 citations as recorded by crossref.
- GPROF V7 and beyond: assessment of current and potential future versions of the GPROF passive microwave precipitation retrievals against ground radar measurements over the continental US and the Pacific Ocean S. Pfreundschuh et al. 10.5194/amt-17-515-2024
- GPROF-NN: a neural-network-based implementation of the Goddard Profiling Algorithm S. Pfreundschuh et al. 10.5194/amt-15-5033-2022
Latest update: 20 Nov 2024
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...
