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DOI10.1016/j.jhydrol.2017.07.036
A generalized Gaussian distribution based uncertainty sampling approach and its application in actual evapotranspiration assimilation
Chen, Shaohui
通讯作者Chen, Shaohui
来源期刊JOURNAL OF HYDROLOGY
ISSN0022-1694
EISSN1879-2707
出版年2017
卷号552页码:745-764
英文摘要

It 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.


英文关键词Actual evapotranspiration assimilation Generalized Gaussian distribution Data assimilation uncertainty Normal distribution
类型Article
语种英语
国家Peoples R China
收录类别SCI-E
WOS记录号WOS:000411541800058
WOS关键词ENSEMBLE KALMAN FILTER ; SEQUENTIAL DATA ASSIMILATION ; ARID REGIONS ; MODEL ; FRAMEWORK ; TUTORIAL ; SYSTEMS
WOS类目Engineering, Civil ; Geosciences, Multidisciplinary ; Water Resources
WOS研究方向Engineering ; Geology ; Water Resources
来源机构中国科学院地理科学与资源研究所
资源类型期刊论文
条目标识符http://119.78.100.177/qdio/handle/2XILL650/200590
作者单位Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China
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Chen, Shaohui. A generalized Gaussian distribution based uncertainty sampling approach and its application in actual evapotranspiration assimilation[J]. 中国科学院地理科学与资源研究所,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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