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dc.contributor.authorCarrassi, A.
dc.contributor.authorVannitsem, S.
dc.contributor.authorZupanski, D.
dc.contributor.authorZupanski, M.
dc.coverage.temporal21st century
dc.date2009
dc.date.accessioned2016-03-07T16:17:03Z
dc.date.accessioned2021-12-09T09:54:19Z
dc.date.available2016-03-07T16:17:03Z
dc.date.available2021-12-09T09:54:19Z
dc.identifier.urihttps://orfeo.belnet.be/handle/internal/8879
dc.descriptionThe performance of the maximum likelihood ensemble filter (MLEF), is investigated in the context of generic systems featuring the essential ingredients of unstable dynamics and on a spatially extended system displaying chaos. The main objective is to clarify the response of the filter to different regimes of motion and highlighting features which may help its optimization in more realistic applications. It is found that, in view of the minimization procedure involved in the filter analysis update, the algorithm provides accurate estimates even in the presence of prominent non-linearities. Most importantly, the filter ensemble size can be designed in connection to the properties of the system attractor (Kaplan–Yorke dimension), thus facilitating the filter setup and limiting the computational cost by using an optimal ensemble. As a corollary, this latter finding indicates that the ensemble perturbations in the MLEF reflect the intrinsic system error dynamics rather than a sampling of realizations of an unknown error covariance.
dc.languageeng
dc.publisherIRM
dc.publisherKMI
dc.publisherRMI
dc.relation.ispartofseriesTellus, 61A
dc.titleThe maximum likelihood ensemble filter performances in chaotic systems
dc.typeArticle
dc.subject.frascatiEarth and related Environmental sciences
dc.audienceGeneral Public
dc.audienceScientific
dc.subject.freeMLEF
dc.subject.freeKaplan-Yorke dimension
dc.source.issueTellus, 61A
dc.source.page587-600
Orfeo.peerreviewedNot pertinent


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