Articles | Volume 19, issue 6
https://doi.org/10.5194/amt-19-2061-2026
© Author(s) 2026. 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-19-2061-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
25 Mar 2026
Research article |
| 25 Mar 2026
| 25 Mar 2026
All-sky temperature and humidity retrieval from the MWRI-RM onboard the FY-3G satellite
Minghua Liu
School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, 210044, China
CMA Earth System Modeling and Prediction Centre (CEMC), China Meteorological Administration, Beijing, 100081, China
State Key Laboratory of Severe Weather (LaSW), Chinese Academy of Meteorological Sciences, China Meteorological Administration, Beijing, 100081, China
Yunfan Yang
State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
Haofei Sun
State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
Ruoying Yin
CMA Earth System Modeling and Prediction Centre (CEMC), China Meteorological Administration, Beijing, 100081, China
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The past 24 h TC trajectories and meteorological field data were used to forecast TC tracks in the northwestern Pacific from hours 6–72 based on GRU_CNN, which we proposed in this paper and which has better prediction results than traditional single deep-learning methods. The historical steering flow of cyclones has a significant effect on improving the accuracy of short-term forecasting, while, in long-term forecasting, the SST and geopotential height will have a particular impact.
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Short summary
This research develops a machine learning approach to estimate atmospheric temperature and relative humidity profiles using satellite and weather data. The results showed that our method could accurately retrieve profiles with a high degree of precision. However, we found some limitations in very humid conditions, suggesting that further improvements to the model are needed. Our findings could help enhance the reliability of atmospheric measurements and contribute to better weather predictions.
This research develops a machine learning approach to estimate atmospheric temperature and...
