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dc.contributor.authorSong, Hajoon
dc.contributor.authorHoteit, Ibrahim
dc.contributor.authorCornuelle, Bruce D.
dc.contributor.authorLuo, Xiaodong
dc.contributor.authorSubramanian, Aneesh C.
dc.date.accessioned2014-06-16T13:41:40Z
dc.date.available2014-06-16T13:41:40Z
dc.date.issued2013-10
dc.date.submitted2013-03
dc.identifier.issn0027-0644
dc.identifier.issn1520-0493
dc.identifier.urihttp://hdl.handle.net/1721.1/87992
dc.description.abstractA new hybrid ensemble Kalman filter/four-dimensional variational data assimilation (EnKF/4D-VAR) approach is introduced to mitigate background covariance limitations in the EnKF. The work is based on the adaptive EnKF (AEnKF) method, which bears a strong resemblance to the hybrid EnKF/three-dimensional variational data assimilation (3D-VAR) method. In the AEnKF, the representativeness of the EnKF ensemble is regularly enhanced with new members generated after back projection of the EnKF analysis residuals to state space using a 3D-VAR [or optimal interpolation (OI)] scheme with a preselected background covariance matrix. The idea here is to reformulate the transformation of the residuals as a 4D-VAR problem, constraining the new member with model dynamics and the previous observations. This should provide more information for the estimation of the new member and reduce dependence of the AEnKF on the assumed stationary background covariance matrix. This is done by integrating the analysis residuals backward in time with the adjoint model. Numerical experiments are performed with the Lorenz-96 model under different scenarios to test the new approach and to evaluate its performance with respect to the EnKF and the hybrid EnKF/3D-VAR. The new method leads to the least root-mean-square estimation errors as long as the linear assumption guaranteeing the stability of the adjoint model holds. It is also found to be less sensitive to choices of the assimilation system inputs and parameters.en_US
dc.language.isoen_US
dc.publisherAmerican Meteorological Societyen_US
dc.relation.isversionofhttp://dx.doi.org/10.1175/MWR-D-12-00244.1en_US
dc.rightsArticle is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.en_US
dc.sourceAmerican Meteorological Societyen_US
dc.titleAn Adjoint-Based Adaptive Ensemble Kalman Filteren_US
dc.typeArticleen_US
dc.identifier.citationSong, Hajoon, Ibrahim Hoteit, Bruce D. Cornuelle, Xiaodong Luo, and Aneesh C. Subramanian. “An Adjoint-Based Adaptive Ensemble Kalman Filter.” Monthly Weather Review 141, no. 10 (October 2013): 3343–3359. © 2013 American Meteorological Societyen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Earth, Atmospheric, and Planetary Sciencesen_US
dc.contributor.mitauthorSong, Hajoonen_US
dc.relation.journalMonthly Weather Reviewen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dspace.orderedauthorsSong, Hajoon; Hoteit, Ibrahim; Cornuelle, Bruce D.; Luo, Xiaodong; Subramanian, Aneesh C.en_US
dc.identifier.orcidhttps://orcid.org/0000-0003-1895-9124
mit.licensePUBLISHER_POLICYen_US


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