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https://doi.org/10.5194/hess-2019-696
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2019-696
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 03 Feb 2020

Submitted as: research article | 03 Feb 2020

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This preprint is currently under review for the journal HESS.

Assimilating Shallow Soil Moisture Observations into Land Models with a Water Budget Constraint

Bo Dan1, Xiaogu Zheng2, Guocan Wu3, and Tao Li4 Bo Dan et al.
  • 1National Marine Data and Information Service, Tianjin, China
  • 2Key Laboratory of Regional Climate-Environment Research for East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
  • 3College of Global Change and Earth System Science, Beijing Normal University, Beijing, China
  • 4Institute of Statistics, Xi'an University of Finance and Economics, Xi'an, China

Abstract. Incorporating observations of shallow soil moisture content into land models is an important step in assimilating satellite observations of soil moisture content. In this study, several modifications of an ensemble Kalman filter (EnKF) are proposed for improving this assimilation. It was found that a forecast error inflation-based approach improves the soil moisture content in shallow layers, but it can increase the analysis error in deep layers. To mitigate the problem in deep layers while maintaining the improvement in shallow layers, a vertical localization-based approach was introduced in this study. During the data assimilation process, although updating the forecast state using observations can reduce the analysis error, the water balance based on the physics in the model could be destroyed. To alleviate the imbalance in the water budget, a weak water balance constrain filter is adopted.

The proposed weakly constrained EnKF that includes forecast error inflation and vertical localization was applied to a synthetic experiment and two real data experiments. The results of the assimilation process suggest that the inflation approach effectively reduce both the short-lived analysis error and the analysis bias in shallow layers, while the vertical localization approach avoids increase in analysis error in deep layers. The weak constraint on the water balance reduces the degree of the water budget imbalance at the price of a small increase in the analysis error.

Bo Dan et al.

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Latest update: 18 Feb 2020
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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. Ensemble Kalman Filter (EnKF) 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 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...
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