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dc.contributor.authorRyu, Duchwan
dc.contributor.authorLiang, Faming
dc.contributor.authorMallick, Bani K.
dc.date.accessioned2016-02-28T05:53:30Z
dc.date.available2016-02-28T05:53:30Z
dc.date.issued2013-03
dc.identifier.citationRyu 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.
dc.identifier.issn0162-1459
dc.identifier.issn1537-274X
dc.identifier.doi10.1080/01621459.2012.734151
dc.identifier.urihttp://hdl.handle.net/10754/599567
dc.description.abstractThe 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.
dc.description.sponsorshipThe 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.
dc.publisherInforma UK Limited
dc.subjectBayesian nonparametric regression
dc.subjectDynamic model
dc.subjectDynamically weighted importance sampling
dc.subjectRadial basis function networks
dc.titleSea Surface Temperature Modeling using Radial Basis Function Networks With a Dynamically Weighted Particle Filter
dc.typeArticle
dc.identifier.journalJournal of the American Statistical Association
dc.contributor.institutionMedical College of Georgia, Augusta, United States
dc.contributor.institutionTexas A and M University, College Station, United States
kaust.grant.numberKUS-C1-016-04


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