Articles | Volume 27, issue 7
https://doi.org/10.5194/hess-27-1583-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-27-1583-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Improving regional climate simulations based on a hybrid data assimilation and machine learning method
Xinlei He
State Key Laboratory of Earth Surface Processes and Resource Ecology, School of Natural Resources, Faculty of Geographical Science, Beijing Normal University, Beijing, China
Yanping Li
School of Environment and Sustainability, University of Saskatchewan, Saskatoon, SK, Canada
Shaomin Liu
CORRESPONDING AUTHOR
State Key Laboratory of Earth Surface Processes and Resource Ecology, School of Natural Resources, Faculty of Geographical Science, Beijing Normal University, Beijing, China
Tongren Xu
State Key Laboratory of Earth Surface Processes and Resource Ecology, School of Natural Resources, Faculty of Geographical Science, Beijing Normal University, Beijing, China
Fei Chen
National Center for Atmospheric Research, Boulder, CO, USA
Zhenhua Li
School of Environment and Sustainability, University of Saskatchewan, Saskatoon, SK, Canada
Zhe Zhang
School of Environment and Sustainability, University of Saskatchewan, Saskatoon, SK, Canada
Rui Liu
Institute of Urban Study, School of Environmental and Geographical
Sciences (SEGS), Shanghai Normal University, Shanghai, China
Lisheng Song
School of Geography and Tourism, Anhui Normal University, Wuhu, China
Ziwei Xu
CORRESPONDING AUTHOR
State Key Laboratory of Earth Surface Processes and Resource Ecology, School of Natural Resources, Faculty of Geographical Science, Beijing Normal University, Beijing, China
Zhixing Peng
State Key Laboratory of Earth Surface Processes and Resource Ecology, School of Natural Resources, Faculty of Geographical Science, Beijing Normal University, Beijing, China
Chen Zheng
Institute of Geographic Sciences and Natural Resources Research,
Chinese Academy of Sciences, Beijing, China
Model code and software
Weather Research and Forecasting Model WRF https://github.com/wrf-model
Short summary
This study highlights the role of integrating vegetation and multi-source soil moisture observations in regional climate models via a hybrid data assimilation and machine learning method. In particular, we show that this approach can improve land surface fluxes, near-surface atmospheric conditions, and land–atmosphere interactions by implementing detailed land characterization information in basins with complex underlying surfaces.
This study highlights the role of integrating vegetation and multi-source soil moisture...