DOI: 10.5194/hess-24-5187-2020
论文题名: Assimilating shallow soil moisture observations into land models with a water budget constraint
作者: Dan B. ; Zheng X. ; Wu G. ; Li T.
刊名: Hydrology and Earth System Sciences
ISSN: 1027-5606
出版年: 2020
卷: 24, 期: 11 起始页码: 5187
结束页码: 5201
语种: 英语
Scopus关键词: Budget control
; Errors
; Forecasting
; Kalman filters
; Moisture determination
; Data assimilation
; Ensemble Kalman Filter
; Forecast errors
; Shallow layers
; Shallow soils
; Spatial average
; Synthetic experiments
; Vertical localization
; Soil moisture
; data assimilation
; error analysis
; forecasting method
; inflation
; Kalman filter
; moisture content
; observational method
; shallow soil
; soil moisture
; spatial analysis
; water budget
英文摘要: Assimilating observations of shallow soil moisture content into land models is an important step in estimating 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. An additional bias-aware assimilation for reducing the analysis bias is also investigated. The results of the assimilation process suggest that the inflation approach effectively reduces the analysis error from 6.70% to 2.00% in shallow layers but increases from 6.38% to 12.49% in deep layers. The vertical localization approach leads to 6.59% of the analysis error in deep layers, and the bias-aware assimilation scheme further reduces this to 6.05 %. The spatial average of the water balance residual is 0.0487mm of weakly constrained EnKF scheme, and 0.0737mm of a weakly constrained EnKF scheme with inflation and localization, which are much smaller than the 0.1389mm of the EnKF scheme. © Author(s) 2020. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/162559
Appears in Collections: 气候变化与战略
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作者单位: Dan, B., National Marine Data and Information Service, Tianjin, China, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China; Zheng, X., Key Laboratory of Regional Climate-Environment Research for East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China; Wu, G., College of Global Change and Earth System Science, Beijing Normal University, Beijing, China; Li, T., Institute of Statistics, Xi'an University of Finance and Economics, Xi'an, China
Recommended Citation:
Dan B.,Zheng X.,Wu G.,et al. Assimilating shallow soil moisture observations into land models with a water budget constraint[J]. Hydrology and Earth System Sciences,2020-01-01,24(11)