globalchange  > 气候变化事实与影响
DOI: 10.1175/JCLI-D-13-00091.1
Scopus记录号: 2-s2.0-84901836584
论文题名:
Ensemble-based parameter estimation in a coupled GCM using the adaptive spatial average method
作者: Liu Y.; Liu Z.; Zhang S.; Rong X.; Jacob R.; Wu S.; Lu F.
刊名: Journal of Climate
ISSN: 8948755
出版年: 2014
卷: 27, 期:11
起始页码: 4002
结束页码: 4014
语种: 英语
Scopus关键词: Climate models ; Climatology ; Climate research ; Estimated parameter ; Faster convergence ; Ocean-atmosphere ; Spatial average ; Parameter estimation ; algorithm ; atmosphere-ocean coupling ; climate modeling ; convergence ; ensemble forecasting ; general circulation model ; signal-to-noise ratio
英文摘要: Ensemble-based parameter estimation for a climate model is emerging as an important topic in climate research. For a complex systemsuch as a coupled ocean-atmosphere general circulationmodel, the sensitivity and response of amodel variable to amodel parameter could vary spatially and temporally.Here, an adaptive spatial average (ASA) algorithmis proposed to increase the efciency of parameter estimation.Rened from a previous spatial average method, the ASA uses the ensemble spread as the criterion for selecting "good"values from the spatially varying posterior estimated parameter values; these good values are then averaged to give the nal global uniform posterior parameter. In comparison with existing methods, the ASA pa-rameter estimation has a superior performance: faster convergence and enhanced signal-to-noise ratio. © 2014 American Meteorological Society.
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/51382
Appears in Collections:气候变化事实与影响

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作者单位: Center for Climate Research, Department of Atmospheric and Oceanic Sciences, University of Wisconsin-Madison, Madison, WI, United States; Laboratory for Ocean-Atmosphere Studies, Peking University, Beijing, China; Center for Climate Research, Department of Atmospheric and Oceanic Sciences, University of Wisconsin-Madison, Madison, WI, United States; GFDL/NOAA, Princeton University, Princeton, NJ, United States; Chinese Academy of Meteorological Sciences, Beijing, China; Mathematics and Computer Science Division, Argonne National Laboratory, Lemont, IL, United States

Recommended Citation:
Liu Y.,Liu Z.,Zhang S.,et al. Ensemble-based parameter estimation in a coupled GCM using the adaptive spatial average method[J]. Journal of Climate,2014-01-01,27(11)
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