Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 18, issue 17
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.
https://doi.org/10.5194/amt-18-4249-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 17
Research article
 | 
09 Sep 2025
Research article |  | 09 Sep 2025

Reconstruction of 3D precipitation measurements from FY-3G MWRI-RM imaging and sounding channels

Yunfan Yang, Wei Han, Haofei Sun, Jun Li, Jiapeng Yan, and Zhiqiu Gao

Viewed

Total article views: 7,863 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
3,815 3,913 135 7,863 108 132
  • HTML: 3,815
  • PDF: 3,913
  • XML: 135
  • Total: 7,863
  • BibTeX: 108
  • EndNote: 132
Views and downloads (calculated since 11 Nov 2024)
Cumulative views and downloads (calculated since 11 Nov 2024)

Viewed (geographical distribution)

Total article views: 7,863 (including HTML, PDF, and XML) Thereof 7,621 with geography defined and 242 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Saved (final revised paper)

Latest update: 16 Aug 2026
Download
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.
Read more
Share