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

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    1-s2.0-S2211675315000263-main.pdf
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    PDF
    Description:
    Accepted Manuscript
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    Type
    Article
    Authors
    Dreano, Denis cc
    Mallick, Bani
    Hoteit, Ibrahim cc
    KAUST Department
    Applied Mathematics and Computational Science Program
    Computer, 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
    2015-04-27
    Online Publication Date
    2015-04-27
    Print Publication Date
    2015-08
    Permanent link to this record
    http://hdl.handle.net/10754/552121
    
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    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.
    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
    DOI
    10.1016/j.spasta.2015.04.002
    Additional Links
    http://linkinghub.elsevier.com/retrieve/pii/S2211675315000263
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.spasta.2015.04.002
    Scopus Count
    Collections
    Articles; Applied Mathematics and Computational Science Program; Physical Science and Engineering (PSE) Division; Earth Science and Engineering Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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