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Accounting for observation errors in image data assimilation

hal.structure.identifierMathematics and computing applied to oceanic and atmospheric flows [AIRSEA]
dc.contributor.authorCHABOT, Vincent
hal.structure.identifierMathematics and computing applied to oceanic and atmospheric flows [AIRSEA]
dc.contributor.authorNODET, Maëlle
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
dc.contributor.authorPAPADAKIS, Nicolas
hal.structure.identifierMathematics and computing applied to oceanic and atmospheric flows [AIRSEA]
dc.contributor.authorVIDARD, Arthur
dc.date.accessioned2024-04-04T03:18:51Z
dc.date.available2024-04-04T03:18:51Z
dc.date.created2013-12-20
dc.date.issued2015-02-13
dc.identifier.issn0280-6495
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/194450
dc.description.abstractEnThis paper deals with the assimilation of image-type data. Such kind of data, such as satellite images have good properties (dense coverage in space and time), but also one crucial problem for data assimilation: they are affected by spatially correlated errors. Classical approaches in data assimilation assume uncorrelated noise, because the proper description and numerical manipulation of non-diagonal error covariance matrices is complex.This paper propose a simple way to provide observation error covariance matrices adapted to spatially correlated errors. This is done using various image transformations: multiscale (wavelets, Fourier, curvelets), gradients, gradient orientations. These transformations are described and compared to classical approaches, such as pixel-to-pixel comparison and observation thinning. We provide simple yet effective covariance matrices for each of these transformations, which take into account the observation error correlations and improve the results. The effectiveness of the proposed approach is demonstrated on twin experiments performed on a 2D shallow-water model.
dc.language.isoen
dc.publisherCo-Action Publishing
dc.title.enAccounting for observation errors in image data assimilation
dc.typeArticle de revue
dc.identifier.doi10.3402/tellusa.v67.23629
dc.subject.halMathématiques [math]/Optimisation et contrôle [math.OC]
dc.subject.halMathématiques [math]/Analyse numérique [math.NA]
bordeaux.journalTellus A
bordeaux.page19
bordeaux.volume67
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.issue23629
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.peerReviewedoui
hal.identifierhal-00923735
hal.version1
hal.popularnon
hal.audienceInternationale
dc.subject.itwavelet
dc.subject.itobservation operator
dc.subject.itVariationnal data assimilation
dc.subject.itapproximate covariance matrices
dc.subject.itcorrelated observation errors
dc.subject.itimage assimilation
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-00923735v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Tellus%20A&rft.date=2015-02-13&rft.volume=67&rft.issue=23629&rft.spage=19&rft.epage=19&rft.eissn=0280-6495&rft.issn=0280-6495&rft.au=CHABOT,%20Vincent&NODET,%20Ma%C3%ABlle&PAPADAKIS,%20Nicolas&VIDARD,%20Arthur&rft.genre=article


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