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A generalized Gaussian distribution based uncertainty sampling approach and its application in actual evapotranspiration assimilation
Chen, Shaohui
2017-09-01
Source PublicationJOURNAL OF HYDROLOGY
ISSN0022-1694
Volume552Pages:745-764
Corresponding AuthorChen, Shaohui(chensh@igsnrr.ac.cn)
AbstractIt is extremely important for ensemble based actual evapotranspiration assimilation (AETA) to accurately sample the uncertainties. Traditionally, the perturbing ensemble is sampled from one prescribed multivariate normal distribution (MND). However, MND is under-represented in capturing the non-MND uncertainties caused by the nonlinear integration of land surface models while these hypernormal uncertainties can be better characterized by generalized Gaussian distribution (GGD) which takes MND as the special case. In this paper, one novel GGD based uncertainty sampling approach is outlined to create one hypernormal ensemble for the purpose of better improving land surface models with observation. With this sampling method, various assimilation methods can be tested in a common equation form. Experimental results on Noah LSM show that the outlined method is more powerful than MND in reducing the misfit between model forecasts and observations in terms of actual evapotranspiration, skin temperature, and soil moisture/temperature in the 1st layer, and also indicate that the energy and water balances constrain ensemble based assimilation to simultaneously optimize all state and diagnostic variables. Overall evaluation expounds that the outlined approach is a better alternative than the traditional MND method for seizing assimilation uncertainties, and it can serve as a useful tool for optimizing hydrological models with data assimilation. (C) 2017 Elsevier B.V. All rights reserved.
KeywordActual evapotranspiration assimilation Generalized Gaussian distribution Data assimilation uncertainty Normal distribution
DOI10.1016/j.jhydrol.2017.07.036
WOS KeywordENSEMBLE KALMAN FILTER ; SEQUENTIAL DATA ASSIMILATION ; ARID REGIONS ; MODEL ; FRAMEWORK ; TUTORIAL ; SYSTEMS
Indexed BySCI
Language英语
Funding ProjectNational Key Research and Development Program of China[2017 5130203101] ; General Program of National Natural Science Foundation of China[41671368] ; General Program of National Natural Science Foundation of China[41371348]
Funding OrganizationNational Key Research and Development Program of China ; General Program of National Natural Science Foundation of China
WOS Research AreaEngineering ; Geology ; Water Resources
WOS SubjectEngineering, Civil ; Geosciences, Multidisciplinary ; Water Resources
WOS IDWOS:000411541800058
PublisherELSEVIER SCIENCE BV
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Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/62228
Collection中国科学院地理科学与资源研究所
Corresponding AuthorChen, Shaohui
AffiliationChinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China
Recommended Citation
GB/T 7714
Chen, Shaohui. A generalized Gaussian distribution based uncertainty sampling approach and its application in actual evapotranspiration assimilation[J]. JOURNAL OF HYDROLOGY,2017,552:745-764.
APA Chen, Shaohui.(2017).A generalized Gaussian distribution based uncertainty sampling approach and its application in actual evapotranspiration assimilation.JOURNAL OF HYDROLOGY,552,745-764.
MLA Chen, Shaohui."A generalized Gaussian distribution based uncertainty sampling approach and its application in actual evapotranspiration assimilation".JOURNAL OF HYDROLOGY 552(2017):745-764.
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