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

Handle URI:
http://hdl.handle.net/10754/599567
Title:
Sea Surface Temperature Modeling using Radial Basis Function Networks With a Dynamically Weighted Particle Filter
Authors:
Ryu, Duchwan; Liang, Faming; Mallick, Bani K.
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.
Publisher:
Informa UK Limited
Journal:
Journal of the American Statistical Association
KAUST Grant Number:
KUS-C1-016-04
Issue Date:
Mar-2013
DOI:
10.1080/01621459.2012.734151
Type:
Article
ISSN:
0162-1459; 1537-274X
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.
Appears in Collections:
Publications Acknowledging KAUST Support

Full metadata record

DC FieldValue Language
dc.contributor.authorRyu, Duchwanen
dc.contributor.authorLiang, Famingen
dc.contributor.authorMallick, Bani K.en
dc.date.accessioned2016-02-28T05:53:30Zen
dc.date.available2016-02-28T05:53:30Zen
dc.date.issued2013-03en
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.en
dc.identifier.issn0162-1459en
dc.identifier.issn1537-274Xen
dc.identifier.doi10.1080/01621459.2012.734151en
dc.identifier.urihttp://hdl.handle.net/10754/599567en
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.en
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.en
dc.publisherInforma UK Limiteden
dc.subjectBayesian nonparametric regressionen
dc.subjectDynamic modelen
dc.subjectDynamically weighted importance samplingen
dc.subjectRadial basis function networksen
dc.titleSea Surface Temperature Modeling using Radial Basis Function Networks With a Dynamically Weighted Particle Filteren
dc.typeArticleen
dc.identifier.journalJournal of the American Statistical Associationen
dc.contributor.institutionMedical College of Georgia, Augusta, United Statesen
dc.contributor.institutionTexas A and M University, College Station, United Statesen
kaust.grant.numberKUS-C1-016-04en
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