A Hybrid Approach to Estimating Error Covariances in Variational Data Assimilation

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dc.contributor.authorCheng, Haiyanen
dc.contributor.authorJardak, Mohameden
dc.contributor.authorAlexe, Mihaien
dc.contributor.authorSandu, Adrianen
dc.contributor.departmentComputer Scienceen
dc.date.accessioned2013-06-19T14:36:51Zen
dc.date.available2013-06-19T14:36:51Zen
dc.date.issued2009-03-01en
dc.description.abstractData Assimilation (DA) involves the combination of observational data with the underlying dynamical principles governing the system under observation. In this work we combine the advantages of the two prominent advanced data assimilation systems, the 4D-Var and the ensemble methods. The proposed method consists of identifying the subspace spanned by the major 4D-Var error reduction directions. These directions are then removed from the background covariance through a Galerkin-type projection. This generates an updated error covariance information at both end points of an assimilation window. The error covariance information is updated between assimilation windows to capture the ``error of the day''. Numerical results using our new hybrid approach on a nonlinear model demonstrate how the background covariance matrix leads to an error covariance update that improves the 4D-Var DA results.en
dc.format.mimetypeapplication/pdfen
dc.identifierhttp://eprints.cs.vt.edu/archive/00001066/en
dc.identifier.sourceurlhttp://eprints.cs.vt.edu/archive/00001066/01/Hybrid.pdfen
dc.identifier.trnumberTR-09-04en
dc.identifier.urihttp://hdl.handle.net/10919/19396en
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.subjectMathematical softwareen
dc.titleA Hybrid Approach to Estimating Error Covariances in Variational Data Assimilationen
dc.typeTechnical reporten
dc.type.dcmitypeTexten

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