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dc.contributor.authorFalchetti, Silvia
dc.contributor.authorAlvarez, Alberto
dc.date.accessioned2019-06-14T10:10:44Z
dc.date.available2019-06-14T10:10:44Z
dc.date.issued2019/05
dc.identifier.govdocCMRE-PR-2019-002en_US
dc.identifier.urihttp://hdl.handle.net/20.500.12489/743
dc.description.abstractData assimilation through an ensemble Kalman filter (EnKF) is not exempt from deficiencies, including the generation of long-range unphysical correlations that degrade its performance. The covariance localization technique has been proposed and used in previous research to mitigate this effect. However, an evaluation of its performance is usually hindered by the sparseness and unsustained collection of independent observations. This article assesses the performance of an ocean prediction system composed of a multivariate EnKF coupled with a regional configuration of the Regional Ocean Model System (ROMS) with a covariance localization solution and data assimilation from an ocean glider that operated over a limited region of the Ligurian Sea. Simultaneous with the operation of the forecast system, a high-quality data set was repeatedly collected with a CTD sensor, i.e., every day during the period from 5 to 20 August 2013 (approximately 4 to 5 times the synoptic time scale of the area), located on board the NR/V Alliance for model validation. Comparisons between the validation data set and the forecasts provide evidence that the performance of the prediction system with covariance localization is superior to that observed using only EnKF assimilation without localization or using a free run ensemble. Furthermore, it is shown that covariance localization also increases the robustness of the model to the location of the assimilated data. Our analysis reveals that improvements are detected with regard to not only preventing the occurrence of spurious correlations but also preserving the spatial coherence in the updated covariance matrix. Covariance localization has been shown to be relevant in operational frameworks where short-term forecasts (on the order of days) are required.en_US
dc.format17 p. : ill. ; digital, PDF fileen_US
dc.language.isoenen_US
dc.publisherCMREen_US
dc.sourceIn: Journal of Marine Systems, issue 180 (2018), pp. 76-89.en_US
dc.subjectMilitary Oceanographyen_US
dc.subjectPhysical oceanographyen_US
dc.subjectData assimilationen_US
dc.subjectMED-REP13 trialen_US
dc.subjectKalman filteringen_US
dc.subjectCovarianceen_US
dc.subjectOcean circulationen_US
dc.subjectROMS (Regional Ocean Modelling System) modelen_US
dc.subjectUnderwater glidersen_US
dc.titleThe impact of covariance localization on the performance of an ocean EnKF system assimilating glider data in the Ligurian Seaen_US
dc.typeReprint (PR)en_US
dc.typePapers and Articlesen_US


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