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	Comparison of ensemble-based state and parameter estimation methods for soil moisture data assimilation
	Chen, Weijing1,2; Huang, Chunlin2,3; Shen, Huanfeng1; Li, Xin2,4
刊名	ADVANCES IN WATER RESOURCES
	2015-12-01
卷号	86页码:425-438
关键词	Data assimilation Soil moisture Brightness temperature State-parameter estimation Common Land Model
ISSN号	0309-1708
DOI	10.1016/j.advwatres.2015.08.003
通讯作者	Huang, Chunlin(huangcl@lzb.ac.cn)
英文摘要	Model parameters are a source of uncertainty that can easily cause systematic deviation and significantly affect the accuracy of soil moisture generation in assimilation systems. This study addresses the issue of retrieving model parameters related to soil moisture via the simultaneous estimation of states and parameters based on the Common Land Model (CoLM). The state-parameter estimation algorithms AEnKF (Augmented Ensemble Kalman Filter), DEnKF (Dual Ensemble Kalman Filter) and SODA (Simultaneous optimization and data assimilation) are entirely implemented within an EnKF framework to investigate how the three algorithms can correct model parameters and improve the accuracy of soil moisture estimation. The analysis is illustrated by assimilating the surface soil moisture levels from varying observation intervals using data from Mongolian plateau sites. Furthermore, a radiation transfer model is introduced as an observation operator to analyze the influence of brightness temperature assimilation on states and parameters that are estimated at different microwave signal frequencies. Three cases were analyzed for both soil moisture and brightness temperature assimilation, focusing on the progressive incorporation of parameter uncertainty, forcing data uncertainty and model uncertainty. It has been demonstrated that EnKF is outperformed by all other methods, as it consistently maintains a bias. State-parameter estimation algorithms can provide a more accurate estimation of soil moisture than EnKF. AEnKF is the most robust method, with the lowest RMSE values for retrieving states and parameters dealing only with parameter uncertainty, but it possesses disadvantages related to increasing sources of uncertainty and decreasing numbers of observations. SODA performs well under the complex situations in which DEnKF shows slight disadvantages in terms of statistical indicators: however, the former consumes far more memory and time than the latter. (C) 2015 Elsevier Ltd. All rights reserved,
收录类别	SCI
WOS关键词	LAND DATA ASSIMILATION ; BRIGHTNESS TEMPERATURE OBSERVATIONS ; HYDROLOGIC DATA ASSIMILATION ; QUASI-GEOSTROPHIC MODEL ; SIMULATED RADAR DATA ; ROOT KALMAN FILTER ; MICROPHYSICAL PARAMETERS ; ATMOSPHERIC STATE ; SURFACE MODEL ; VEGETATION
WOS研究方向	Water Resources
WOS类目	Water Resources
语种	英语
出版者	ELSEVIER SCI LTD
WOS记录号	WOS:000365623500014
内容类型	期刊论文
URI标识	http://www.corc.org.cn/handle/1471x/2557177
专题	寒区旱区环境与工程研究所
通讯作者	Huang, Chunlin
作者单位	1.Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Hubei, Peoples R China 2.Chinese Acad Sci, Cold & Arid Reg Environm & Engn Res Inst, Key Lab Remote Sensing Gansu Prov, Lanzhou 730000, Gansu, Peoples R China 3.Chinese Acad Sci, Cold & Arid Reg Environm & Engn Res Inst, Heihe Remote Sensing Expt Res Stn, Lanzhou 730000, Gansu, Peoples R China 4.Chinese Acad Sci, CAS Ctr Excellence Tibetan Plateau Earth Sci, Beijing 100101, Peoples R China
推荐引用方式 GB/T 7714	Chen, Weijing,Huang, Chunlin,Shen, Huanfeng,et al. Comparison of ensemble-based state and parameter estimation methods for soil moisture data assimilation[J]. ADVANCES IN WATER RESOURCES,2015,86:425-438.
APA	Chen, Weijing,Huang, Chunlin,Shen, Huanfeng,&Li, Xin.(2015).Comparison of ensemble-based state and parameter estimation methods for soil moisture data assimilation.ADVANCES IN WATER RESOURCES,86,425-438.
MLA	Chen, Weijing,et al."Comparison of ensemble-based state and parameter estimation methods for soil moisture data assimilation".ADVANCES IN WATER RESOURCES 86(2015):425-438.

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