Show simple item record

Treatment of the error due to unresolved scales in sequential dataassimilation.

dc.contributor.authorCarrassi, A.
dc.contributor.authorVannitsem, S.
dc.coverage.temporal21st century
dc.date2011
dc.date.accessioned2016-03-07T16:17:07Z
dc.date.accessioned2021-12-09T09:54:25Z
dc.date.available2016-03-07T16:17:07Z
dc.date.available2021-12-09T09:54:25Z
dc.identifier.urihttps://orfeo.belnet.be/handle/internal/8902
dc.descriptionA novel method to account for model error due to unresolved scales in sequential data assimilation is proposed. An equation for the model error covariance required in the extended Kalman filter update is derived along with an approximation suitable for application with large scale dynamics typical in environmental modeling. The approach, referred to as Short Time Extended Kalman Filter (STEKF), is tested in the context of a low order chaotic dynamical system and it is compared with an EKF implementing a multiplicative covariance inflation, a practical procedure often used to account for model error in data assimilation. The results show that the performance of the STEKF is significantly better than that of the classical EKF with no additional computational cost and encourages the implementation of this approach in more realistic contexts.
dc.languageeng
dc.publisherIRM
dc.publisherKMI
dc.publisherRMI
dc.relation.ispartofseriesInternational Journal of Bifurcation and Chaos, 21
dc.titleTreatment of the error due to unresolved scales in sequential dataassimilation.
dc.typeArticle
dc.subject.frascatiEarth and related Environmental sciences
dc.audienceGeneral Public
dc.audienceScientific
dc.subject.freeError
dc.subject.freeShort Time Extended Kalman Filter (STEKF)
dc.source.issueInternational Journal of Bifurcation and Chaos, 21
dc.source.page3619-3626
Orfeo.peerreviewedNot pertinent


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record