Ensemble-based chemical data assimilation I: An idealized setting

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dc.contributor.authorConstantinescu, Emil M.en
dc.contributor.authorSandu, Adrianen
dc.contributor.authorChai, Tianfengen
dc.contributor.authorCarmichael, Gregory R.en
dc.contributor.departmentComputer Scienceen
dc.date.accessioned2013-06-19T14:35:47Zen
dc.date.available2013-06-19T14:35:47Zen
dc.date.issued2006-03-01en
dc.description.abstractData assimilation is the process of integrating observational data and model predictions to obtain an optimal representation of the state of the atmosphere. As more chemical observations in the troposphere are becoming available, chemical data assimilation is expected to play an essential role in air quality forecasting, similar to the role it has in numerical weather prediction. Considerable progress has been made recently in the development of variational tools for chemical data assimilation. In this paper we assess the performance of the ensemble Kalman filter (EnKF). Results in an idealized setting show that EnKF is promising for chemical data assimilation.en
dc.format.mimetypeapplication/pdfen
dc.identifierhttp://eprints.cs.vt.edu/archive/00000743/en
dc.identifier.sourceurlhttp://eprints.cs.vt.edu/archive/00000743/01/enkf_ideal.pdfen
dc.identifier.trnumberTR-06-06en
dc.identifier.urihttp://hdl.handle.net/10919/20199en
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.titleEnsemble-based chemical data assimilation I: An idealized settingen
dc.typeTechnical reporten
dc.type.dcmitypeTexten

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