DOI: 10.1002/2015GL067238
论文题名: Assimilating atmospheric observations into the ocean using strongly coupled ensemble data assimilation
作者: Sluka T.C. ; Penny S.G. ; Kalnay E. ; Miyoshi T.
刊名: Geophysical Research Letters
ISSN: 0094-9454
EISSN: 1944-9185
出版年: 2016
卷: 43, 期: 2 起始页码: 752
结束页码: 759
语种: 英语
英文关键词: coupled models
; data assimilation
; ensemble Kalman filter
Scopus关键词: Errors
; Kalman filters
; Atmospheric observations
; Coupled models
; Data assimilation
; Ensemble data assimilation
; Ensemble Kalman Filter
; Intermediate complexity
; Observing system simulation experiments
; Ocean-atmosphere coupled model
; Oceanography
; atmosphere-ocean coupling
; atmosphere-ocean system
; climate modeling
; covariance analysis
; data assimilation
; error analysis
; Kalman filter
; numerical model
; observational method
英文摘要: The local ensemble transform Kalman filter (LETKF) is used to develop a strongly coupled data assimilation (DA) system for an intermediate complexity ocean-atmosphere coupled model. Strongly coupled DA uses the cross-domain error covariance from a coupled-model background ensemble to allow observations in one domain to directly impact the state of the other domain during the analysis update. This method is compared to weakly coupled DA in which the coupled model is used for the background, but the cross-domain error covariance is not utilized. We perform an observing system simulation experiment with atmospheric observations only. Strongly coupled DA reduces the ocean analysis errors compared to weakly coupled DA, and the higher accuracy of the ocean also improves the atmosphere. The LETKF system design presented should allow for easy implementation of strongly coupled DA with other types of coupled models. © 2016. American Geophysical Union. All Rights Reserved.
URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84958064179&doi=10.1002%2f2015GL067238&partnerID=40&md5=08071e4b9bf3ba59d2864c6237af7541
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/10417
Appears in Collections: 科学计划与规划 气候变化与战略
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作者单位: Department of Atmospheric and Oceanic Science, University of Maryland, College Park, MD, United States
Recommended Citation:
Sluka T.C.,Penny S.G.,Kalnay E.,et al. Assimilating atmospheric observations into the ocean using strongly coupled ensemble data assimilation[J]. Geophysical Research Letters,2016-01-01,43(2).