Articles | Volume 24, issue 11
https://doi.org/10.5194/hess-24-5187-2020
© Author(s) 2020. 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-24-5187-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Assimilating shallow soil moisture observations into land models with a water budget constraint
Bo Dan
National Marine Data and Information Service, Tianjin, China
College of Global Change and Earth System Science, Beijing Normal University, Beijing, China
Xiaogu Zheng
Key Laboratory of Regional Climate-Environment Research for East Asia, Institute of Atmospheric Physics,
Chinese Academy of Sciences, Beijing, China
College of Global Change and Earth System Science, Beijing Normal University, Beijing, China
Tao Li
Institute of Statistics, Xi'an University of Finance and Economics, Xi'an, China
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Short summary
Data assimilation is a procedure to generate an optimal combination of the state variable in geoscience, based on the model outputs and observations. The ensemble Kalman filter (EnKF) scheme is a widely used assimilation method in soil moisture estimation. This study proposed several modifications of EnKF for improving this assimilation. The study shows that the quality of the assimilation result is improved, while the degree of water budget imbalance is reduced.
Data assimilation is a procedure to generate an optimal combination of the state variable in...