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    Sea Surface Temperature Modeling using Radial Basis Function Networks With a Dynamically Weighted Particle Filter

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    Type
    Article
    Authors
    Ryu, Duchwan
    Liang, Faming
    Mallick, Bani K.
    KAUST Grant Number
    KUS-C1-016-04
    Date
    2013-03
    Permanent link to this record
    http://hdl.handle.net/10754/599567
    
    Metadata
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    Abstract
    The sea surface temperature (SST) is an important factor of the earth climate system. A deep understanding of SST is essential for climate monitoring and prediction. In general, SST follows a nonlinear pattern in both time and location and can be modeled by a dynamic system which changes with time and location. In this article, we propose a radial basis function network-based dynamic model which is able to catch the nonlinearity of the data and propose to use the dynamically weighted particle filter to estimate the parameters of the dynamic model. We analyze the SST observed in the Caribbean Islands area after a hurricane using the proposed dynamic model. Comparing to the traditional grid-based approach that requires a supercomputer due to its high computational demand, our approach requires much less CPU time and makes real-time forecasting of SST doable on a personal computer. Supplementary materials for this article are available online. © 2013 American Statistical Association.
    Citation
    Ryu D, Liang F, Mallick BK (2013) Sea Surface Temperature Modeling using Radial Basis Function Networks With a Dynamically Weighted Particle Filter. Journal of the American Statistical Association 108: 111–123. Available: http://dx.doi.org/10.1080/01621459.2012.734151.
    Sponsors
    The authors thank the editor, associated editor, and referees for their constructive comments/suggestions that led to significant improvement of this article. Liang's research was supported in part by grants from the National Science Foundation (DMS-1007457 and DMS-1106494) and the award (KUS-C1-016-04) made by King Abdullah University of Science and Technology (KAUST). Mallick's research was supported by the award number KUS-CI-016-04 made by KAUST.
    Publisher
    Informa UK Limited
    Journal
    Journal of the American Statistical Association
    DOI
    10.1080/01621459.2012.734151
    ae974a485f413a2113503eed53cd6c53
    10.1080/01621459.2012.734151
    Scopus Count
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