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Assimilating atmospheric observations into the ocean using strongly coupled ensemble data assimilation
Author(s) -
Sluka Travis C.,
Penny Stephen G.,
Kalnay Eugenia,
Miyoshi Takemasa
Publication year - 2016
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1002/2015gl067238
Subject(s) - data assimilation , covariance , kalman filter , atmosphere (unit) , atmospheric model , environmental science , ensemble kalman filter , domain (mathematical analysis) , computer science , meteorology , mathematics , physics , extended kalman filter , statistics , mathematical analysis , artificial intelligence
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.