Articles | Volume 17, issue 8
https://doi.org/10.5194/gmd-17-3447-2024
© Author(s) 2024. 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-17-3447-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and preliminary validation of a land surface image assimilation system based on the Common Land Model
Wangbin Shen
Center of Data Assimilation for Research and Application, Nanjing University of Information Science and Technology, Nanjing, 210044, China
International Center for Climate and Environment Sciences, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
Zhengkun Qin
CORRESPONDING AUTHOR
Center of Data Assimilation for Research and Application, Nanjing University of Information Science and Technology, Nanjing, 210044, China
Juan Li
CMA Center for Earth System Modeling and Prediction, China Meteorological Administration, Beijing, 100081, China
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, 100081, China
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Short summary
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The subseasonal prediction of extreme hydroclimate events such as droughts/floods has remained stubbornly low for years. This paper presents a new international initiative which, for the first time, introduces spring land surface temperature anomalies over high mountains to improve precipitation prediction through remote effects of land–atmosphere interactions. More than 40 institutions worldwide are participating in this effort. The experimental protocol and preliminary results are presented.
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Short summary
In this study, a land surface image assimilation system capable of optimizing the spatial structure of the background field is constructed by introducing the curvelet analysis method and taking the similarity of image structure as a weak constraint. The findings demonstrate that the assimilation of surface soil moisture observation images effectively and reasonably enhances the spatial structure of soil moisture analysis field.
In this study, a land surface image assimilation system capable of optimizing the spatial...