Filtering remotely sensed chlorophyll concentrations in the Red Sea using a space-time covariance model and a Kalman filter

Handle URI:
http://hdl.handle.net/10754/552121
Title:
Filtering remotely sensed chlorophyll concentrations in the Red Sea using a space-time covariance model and a Kalman filter
Authors:
Dreano, Denis ( 0000-0001-7956-5538 ) ; Mallick, Bani; Hoteit, Ibrahim ( 0000-0002-3751-4393 )
Abstract:
A 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.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Filtering remotely sensed chlorophyll concentrations in the Red Sea using a space-time covariance model and a Kalman filter 2015 Spatial Statistics
Publisher:
Elsevier BV
Journal:
Spatial Statistics
Issue Date:
27-Apr-2015
DOI:
10.1016/j.spasta.2015.04.002
Type:
Article
ISSN:
22116753
Additional Links:
http://linkinghub.elsevier.com/retrieve/pii/S2211675315000263
Appears in Collections:
Articles; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorDreano, Denisen
dc.contributor.authorMallick, Banien
dc.contributor.authorHoteit, Ibrahimen
dc.date.accessioned2015-05-03T13:36:39Zen
dc.date.available2015-05-03T13:36:39Zen
dc.date.issued2015-04-27en
dc.identifier.citationFiltering remotely sensed chlorophyll concentrations in the Red Sea using a space-time covariance model and a Kalman filter 2015 Spatial Statisticsen
dc.identifier.issn22116753en
dc.identifier.doi10.1016/j.spasta.2015.04.002en
dc.identifier.urihttp://hdl.handle.net/10754/552121en
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.en
dc.publisherElsevier BVen
dc.relation.urlhttp://linkinghub.elsevier.com/retrieve/pii/S2211675315000263en
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.2015.04.002en
dc.subjectSpace-time statisticsen
dc.subjectCovariance modelsen
dc.subjectKalman filteren
dc.subjectGeostatisticsen
dc.subjectChlorophyll concentrationen
dc.titleFiltering remotely sensed chlorophyll concentrations in the Red Sea using a space-time covariance model and a Kalman filteren
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalSpatial Statisticsen
dc.eprint.versionPost-printen
dc.contributor.institutionDepartment of Statistics, Texas A&M University, United Statesen
kaust.authorHoteit, Ibrahimen
kaust.authorDreano, Denisen
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