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dc.contributor.authorAretxabaleta, Alfredo L.  Concept link
dc.contributor.authorSmith, Keston W.  Concept link
dc.date.accessioned2011-04-13T18:48:38Z
dc.date.available2012-03-09T09:32:34Z
dc.date.issued2011-01-07
dc.identifier.urihttps://hdl.handle.net/1912/4460
dc.descriptionAuthor Posting. © The Author(s), 2011. This is the author's version of the work. It is posted here by permission of Springer for personal use, not for redistribution. The definitive version was published in Computational Geosciences 15 (2011): 627-636, doi:10.1007/s10596-011-9229-3.en_US
dc.description.abstractAn approach to analyze regime change in spatial time series data sets is followed and extended to jointly analyze a dynamical model depicting regime shift and observational data informing the same process. We analyze changes in the joint model-data regime and covariability within each regime. The method is applied to two observational data sets of equatorial sea surface temperature (TAO/TRITON array and satellite) and compared with the predicted data by the ECCO-JPL modeling system.en_US
dc.description.sponsorshipFunding for this work was provided by Spanish National Program on Space, under contract ESP2005-06823-C05. A. Aretxabaleta has been additionally supported by a Juan de la Cierva grant of the Spanish Government. K. Smith was supported by NSF Grant DMS-0934653.en_US
dc.format.mimetypeapplication/pdf
dc.language.isoen_USen_US
dc.relation.urihttps://doi.org/10.1007/s10596-011-9229-3
dc.subjectSkill assessmenten_US
dc.subjectData clusteringen_US
dc.subjectGaussian Mixture Modelsen_US
dc.subjectENSOen_US
dc.titleAnalyzing state-dependent model–data comparison in multi-regime systemsen_US
dc.typePreprinten_US


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