Autoregressive Models of Background Errors for Chemical Data Assimilation

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dc.contributor.authorConstantinescu, Emil M.en
dc.contributor.authorChai, Tianfengen
dc.contributor.authorSandu, Adrianen
dc.contributor.authorCarmichael, Gregory R.en
dc.contributor.departmentComputer Scienceen
dc.date.accessioned2013-06-19T14:35:45Zen
dc.date.available2013-06-19T14:35:45Zen
dc.date.issued2006-10-01en
dc.description.abstractThe 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
dc.format.mimetypeapplication/pdfen
dc.identifierhttp://eprints.cs.vt.edu/archive/00000926/en
dc.identifier.sourceurlhttp://eprints.cs.vt.edu/archive/00000926/01/ar_background.pdfen
dc.identifier.trnumberTR-06-22en
dc.identifier.urihttp://hdl.handle.net/10919/19556en
dc.language.isoenen
dc.publisherDepartment of Computer Science, Virginia Polytechnic Institute & State Universityen
dc.relation.ispartofComputer Science Technical Reportsen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectNumerical analysisen
dc.titleAutoregressive Models of Background Errors for Chemical Data Assimilationen
dc.typeTechnical reporten
dc.type.dcmitypeTexten

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