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

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  • Duchwan Ryu
  • Faming Liang
  • Bani K. Mallick

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.

Suggested Citation

  • Duchwan Ryu & Faming Liang & Bani K. Mallick, 2013. "Sea Surface Temperature Modeling using Radial Basis Function Networks With a Dynamically Weighted Particle Filter," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(501), pages 111-123, March.
    • Handle: RePEc:taf:jnlasa:v:108:y:2013:i:501:p:111-123
    DOI: 10.1080/01621459.2012.734151
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    References listed on IDEAS

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    1. Liu J. S & Liang F. & Wong W.H., 2001. "A Theory for Dynamic Weighting in Monte Carlo Computation," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 561-573, June.
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    Cited by:

    1. Suvo Chatterjee & Shrabanti Chowdhury & Duchwan Ryu & Sanjib Basu, 2023. "Bayesian functional data analysis over dependent regions and its application for identification of differentially methylated regions," Biometrics, The International Biometric Society, vol. 79(4), pages 3294-3306, December.
    2. Duchwan Ryu & Devrim Bilgili & Önder Ergönül & Faming Liang & Nader Ebrahimi, 2018. "A Bayesian Generalized Linear Model for Crimean–Congo Hemorrhagic Fever Incidents," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 23(1), pages 153-170, March.

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