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dc.contributor.authorConstantinescu, Emil M.en_US
dc.contributor.authorChai, Tianfengen_US
dc.contributor.authorSandu, Adrianen_US
dc.contributor.authorCarmichael, Gregory Ren_US
dc.date.accessioned2013-05-29T14:02:19Zen_US
dc.date.accessioned2013-06-19T14:35:45Z
dc.date.available2013-05-29T14:02:19Zen_US
dc.date.available2013-06-19T14:35:45Z
dc.date.issued2006-10-01
dc.identifierhttp://eprints.cs.vt.edu/archive/00000926/en_US
dc.identifier.urihttp://hdl.handle.net/10919/19556
dc.descriptionThe task of providing an optimal analysis of the state of the atmosphere requires the development of dynamic data-driven systems that efficiently integrate the observational data and the models. Data assimilation (DA) is the process of adjusting the states or parameters of a model in such a way that its outcome (prediction) is close, in some distance metric, to observed (real) states. It is widely accepted that a key ingredient of successful data assimilation is a realistic estimation of the background error distribution. This paper introduces a new method for estimating the background errors which are modeled using autoregressive processes. The proposed approach is computationally inexpensive and captures the error correlations along the flow lines.en_US
dc.format.mimetypeapplication/pdfen_US
dc.publisherDepartment of Computer Science, Virginia Polytechnic Institute & State Universityen_US
dc.subjectNumerical analysisen_US
dc.titleAutoregressive Models of Background Errors for Chemical Data Assimilationen_US
dc.typeTechnical reporten_US
dc.identifier.trnumberTR-06-22en_US
dc.type.dcmitypeTexten_US
dc.identifier.sourceurlhttp://eprints.cs.vt.edu/archive/00000926/01/ar_background.pdf


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