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dc.contributor.authorFalchetti, Silvia
dc.contributor.authorAlvarez, Alberto
dc.contributor.authorOnken, Reiner
dc.date.accessioned2019-06-27T15:15:12Z
dc.date.available2019-06-27T15:15:12Z
dc.date.issued2019/06
dc.identifier.govdocCMRE-PR-2019-133en_US
dc.identifier.urihttp://hdl.handle.net/20.500.12489/873
dc.description.abstractThis study investigates the performance of a multivariate Ensemble Kalman Filter coupled with a relocatable limited-area configuration of the Regional Ocean Modeling System to predict ocean states by assimilating a heterogeneous data set involving underwater gliders and ship observations. In particular, two different ensemble initialization techniques are exploited and evaluated with the dataset collected during the REP13-MED experiment conducted by CMRE on 5-20 August 2013 in the Ligurian Sea. Results show that the forecast skill is significantly improved when the free ensemble is initialized from a long term climatology of the Mediterranean Forecast System. In particular the results obtained reveal significant increased skills in salinity forecasting in comparison with the previous ensemble initialization technique [6].en_US
dc.format6 p. : ill. ; digital, PDF fileen_US
dc.language.isoenen_US
dc.publisherCMREen_US
dc.sourceIn: Proceedings of the OCEANS 2015 MTS/IEEE Conference, 18-21 May 2015, Genoa, Italy, doi: 10.1109/OCEANS-Genova.2015.7271359en_US
dc.subjectData assimilationen_US
dc.subjectKalman filteringen_US
dc.subjectOcean predictionen_US
dc.subjectUnderwater glidersen_US
dc.subjectSensor networksen_US
dc.subjectPhysical oceanographyen_US
dc.subjectMilitary oceanographyen_US
dc.subjectLigurian Seaen_US
dc.titleA relocatable EnKF ocean data assimilation tool for heterogeneous observational networksen_US
dc.typeReprint (PR)en_US
dc.typePapers and Articlesen_US


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