Articles | Volume 19, issue 6
https://doi.org/10.5194/amt-19-2061-2026
https://doi.org/10.5194/amt-19-2061-2026
Research article
 | 
25 Mar 2026
Research article |  | 25 Mar 2026

All-sky temperature and humidity retrieval from the MWRI-RM onboard the FY-3G satellite

Minghua Liu, Wei Han, Yunfan Yang, Haofei Sun, and Ruoying Yin

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Cited articles

Aires, F., Prigent, C., Rossow, W. B., and Rothstein, M.: A new neural network approach including first guess for retrieval of atmospheric water vapor, cloud liquid water path, surface temperature, and emissivities over land from satellite microwave observations, J. Geophys. Res., 106, 14887–14907, https://doi.org/10.1029/2001JD900085, 2001. 
Blackwell, W. J.: A neural-network technique for the retrieval of atmospheric temperature and moisture profiles from high spectral resolution sounding data, IEEE Trans. Geosci. Remote Sens., 43, 2535–2546, https://doi.org/10.1109/tgrs.2005.855071, 2005. 
Boucher, E.: Designing Deep-Learning models for surface and atmospheric retrievals from the IASI infrared sounder, PhD thesis, Sorbonne Université, Paris, France, NNT: 2024SORUS145, https://theses.hal.science/tel-04701253v1 (last access: 23 March 2026), 2024. 
Boucher, E. and Aires, F.: Machine learning for satellite retrievals: linear dampening of the extremes and an extreme-oriented learning formulation, Environ. Res. Lett., 18, 024025, https://doi.org/10.1088/1748-9326/acb3e3, 2023. 
Boukabara, S.-A., Garrett, K., Chen, W., Iturbide-Sanchez, F., Grassotti, C., Kongoli, C., Weng, F., Liu, Q., Baelen, B., and Sun, N.: MiRS: An all-weather 1DVAR satellite data assimilation and retrieval system, IEEE Trans. Geosci. Remote Sens., 49, 3249–3272, https://doi.org/10.1109/TGRS.2011.2158438, 2011. 
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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.
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