Filtering remotely sensed chlorophyll concentrations in the Red Sea using a space-time covariance model and a Kalman filter
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ArticleKAUST Department
Applied Mathematics and Computational Science ProgramComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Earth Fluid Modeling and Prediction Group
Earth Science and Engineering Program
Physical Science and Engineering (PSE) Division
Date
2015-04-27Online Publication Date
2015-04-27Print Publication Date
2015-08Permanent link to this record
http://hdl.handle.net/10754/552121
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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.Citation
Filtering remotely sensed chlorophyll concentrations in the Red Sea using a space-time covariance model and a Kalman filter 2015 Spatial StatisticsPublisher
Elsevier BVJournal
Spatial StatisticsAdditional Links
http://linkinghub.elsevier.com/retrieve/pii/S2211675315000263ae974a485f413a2113503eed53cd6c53
10.1016/j.spasta.2015.04.002