Articles | Volume 18, issue 17
https://doi.org/10.5194/amt-18-4249-2025
© Author(s) 2025. 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-18-4249-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
09 Sep 2025
Research article |
| 09 Sep 2025
| 09 Sep 2025
Reconstruction of 3D precipitation measurements from FY-3G MWRI-RM imaging and sounding channels
Yunfan Yang
State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing, China
CMA Earth System Modeling and Prediction Centre (CEMC), China Meteorological Administration, Beijing, China
State Key Laboratory of Severe Weather Meteorological Science and Technology (LASW), Beijing, 100081, China
Haofei Sun
State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing, China
National Satellite Meteorological Center, China Meteorological Administration, Beijing, 100081, China
Jiapeng Yan
State Key Laboratory of Severe Weather Meteorological Science and Technology (LASW), Beijing, 100081, China
Zhiqiu Gao
State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing, China
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Short summary
Our research improves satellite-based precipitation monitoring by using deep learning to reconstruct radar observations from passive microwave radiances. Active radar is costly, so we focus on a more accessible approach. Using data from the Fengyun-3G satellite, we successfully tracked severe weather like Typhoon Khanun and heavy rainfall in Beijing in 2023. This method enhances global precipitation data and helps better understand extreme weather.
Our research improves satellite-based precipitation monitoring by using deep learning to...
