Articles | Volume 19, issue 18
https://doi.org/10.5194/gmd-19-9177-2026
© 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/gmd-19-9177-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
28 Sep 2026
Development and technical paper |
| 28 Sep 2026
| 28 Sep 2026
Effectively assimilate satellite land surface temperature into offline land surface models within ensemble-based assimilation frameworks
Yunhao Fu
State Key Laboratory of Climate System Prediction and Risk Management (CPRM)/Institute of Climate Application Research (ICAR), Nanjing University of Information Science and Technology, Nanjing 210044, China
School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing 210044, China
State Key Laboratory of Climate System Prediction and Risk Management (CPRM)/Institute of Climate Application Research (ICAR), Nanjing University of Information Science and Technology, Nanjing 210044, China
School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, China
Jingjia Luo
State Key Laboratory of Climate System Prediction and Risk Management (CPRM)/Institute of Climate Application Research (ICAR), Nanjing University of Information Science and Technology, Nanjing 210044, China
School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, China
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Guangxin He, Wang Zhang, Xiaoran Zhuang, Yuxuan Feng, Juanzhen Sun, Yubao Qiu, Lei Lei, and Jingjia Luo
EGUsphere, https://doi.org/10.5194/egusphere-2025-6488, https://doi.org/10.5194/egusphere-2025-6488, 2026
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
Short summary
Accurate weather forecasting is vital for safety but faces challenges with calculation errors. We developed a new predictive model that enhances accuracy by reducing data redundancy and optimizing information flow. Experiments with real radar data show our approach significantly outperforms existing methods, particularly for heavy rainfall. This model maintains clear details over time, offering a robust tool for timely severe weather warnings and effective disaster prevention.
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Short summary
It is challenging to assimilate land surface temperature (LST) owing to its fast temporally varying nature. This study proposes a scheme by jointly updating the soil temperature and soil moisture. Results show marginal enhancement in LST, yet soil temperature bias over Northeast Asia (NA) drops sharply. Snow temperature and snow depth over NA, and soil moisture in the humid tropics also improve significantly. These consistent improvements demonstrate the effectiveness of the proposed scheme.
It is challenging to assimilate land surface temperature (LST) owing to its fast temporally...
