Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/86572
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dc.contributor.authorBellsky, T.-
dc.contributor.authorBerwald, J.-
dc.contributor.authorMitchell, L.-
dc.date.issued2014-
dc.identifier.citationMonthly Weather Review, 2014; 142(6):2150-2164-
dc.identifier.issn0027-0644-
dc.identifier.issn1520-0493-
dc.identifier.urihttp://hdl.handle.net/2440/86572-
dc.description.abstractThe authors study parameter estimation for nonglobal parameters in a low-dimensional chaotic model using the local ensemble transform Kalman filter (LETKF). By modifying existing techniques for using observational data to estimate global parameters, they present a methodology whereby spatially varying parameters can be estimated using observations only within a localized region of space. Taking a low-dimensional nonlinear chaotic conceptual model for atmospheric dynamics as a numerical test bed, the authors show that this parameter estimation methodology accurately estimates parameters that vary in both space and time, as well as parameters representing physics absent from the model.-
dc.description.statementofresponsibilityThomas Bellsky, Jesse Berwald, Lewis Mitchell-
dc.language.isoen-
dc.publisherAmerican Meteorological Society-
dc.rightsCopyright status unknown-
dc.source.urihttp://dx.doi.org/10.1175/mwr-d-13-00200.1-
dc.subjectNonlinear dynamics; Kalman filters; Data assimilation; Parameterization-
dc.titleNonglobal parameter estimation using local ensemble Kalman filtering-
dc.typeJournal article-
dc.identifier.doi10.1175/MWR-D-13-00200.1-
pubs.publication-statusPublished-
dc.identifier.orcidMitchell, L. [0000-0001-8191-1997]-
Appears in Collections:Aurora harvest 2
Mathematical Sciences publications

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