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dc.contributor.authorDreano, Denis
dc.contributor.authorMallick, Bani
dc.contributor.authorHoteit, Ibrahim
dc.date.accessioned2015-05-03T13:36:39Z
dc.date.available2015-05-03T13:36:39Z
dc.date.issued2015-04-27
dc.identifier.citationFiltering remotely sensed chlorophyll concentrations in the Red Sea using a space-time covariance model and a Kalman filter 2015 Spatial Statistics
dc.identifier.issn22116753
dc.identifier.doi10.1016/j.spasta.2015.04.002
dc.identifier.urihttp://hdl.handle.net/10754/552121
dc.description.abstractA statistical model is proposed to filter satellite-derived chlorophyll concentration from the Red Sea, and to predict future chlorophyll concentrations. The seasonal trend is first estimated after filling missing chlorophyll data using an Empirical Orthogonal Function (EOF)-based algorithm (Data Interpolation EOF). The anomalies are then modeled as a stationary Gaussian process. A method proposed by Gneiting (2002) is used to construct positive-definite space-time covariance models for this process. After choosing an appropriate statistical model and identifying its parameters, Kriging is applied in the space-time domain to make a one step ahead prediction of the anomalies. The latter serves as the prediction model of a reduced-order Kalman filter, which is applied to assimilate and predict future chlorophyll concentrations. The proposed method decreases the root mean square (RMS) prediction error by about 11% compared with the seasonal average.
dc.publisherElsevier BV
dc.relation.urlhttp://linkinghub.elsevier.com/retrieve/pii/S2211675315000263
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Spatial Statistics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Spatial Statistics, 27 April 2015. DOI: 10.1016/j.spasta.2015.04.002
dc.subjectSpace-time statistics
dc.subjectCovariance models
dc.subjectKalman filter
dc.subjectGeostatistics
dc.subjectChlorophyll concentration
dc.titleFiltering remotely sensed chlorophyll concentrations in the Red Sea using a space-time covariance model and a Kalman filter
dc.typeArticle
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentEarth Fluid Modeling and Prediction Group
dc.contributor.departmentEarth Science and Engineering Program
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.identifier.journalSpatial Statistics
dc.eprint.versionPost-print
dc.contributor.institutionDepartment of Statistics, Texas A&M University, United States
kaust.personHoteit, Ibrahim
kaust.personDreano, Denis
refterms.dateFOA2017-04-27T00:00:00Z
dc.date.published-online2015-04-27
dc.date.published-print2015-08


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