A generalized Gaussian distribution based uncertainty sampling approach and its application in actual evapotranspiration assimilation
文献类型：期刊论文
| 作者 | Chen, Shaohui |
| 刊名 | JOURNAL OF HYDROLOGY
 |
| 出版日期 | 2017-09-01 |
| 卷号 | 552页码:745-764 |
| 关键词 | Actual evapotranspiration assimilation Generalized Gaussian distribution Data assimilation uncertainty Normal distribution |
| ISSN号 | 0022-1694 |
| DOI | 10.1016/j.jhydrol.2017.07.036 |
| 通讯作者 | Chen, Shaohui(chensh@igsnrr.ac.cn) |
| 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. |
| WOS关键词 | ENSEMBLE KALMAN FILTER ; SEQUENTIAL DATA ASSIMILATION ; ARID REGIONS ; MODEL ; FRAMEWORK ; TUTORIAL ; SYSTEMS |
| 资助项目 | National 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] |
| WOS研究方向 | Engineering ; Geology ; Water Resources |
| 语种 | 英语 |
| WOS记录号 | WOS:000411541800058 |
| 出版者 | ELSEVIER SCIENCE BV |
| 资助机构 | National Key Research and Development Program of China ; General Program of National Natural Science Foundation of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/62227]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Chen, Shaohui |
| 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 |
| 推荐引用方式 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. |
入库方式： OAI收割
来源：地理科学与资源研究所
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