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dc.contributor.authorElsheikh, Ahmed H.
dc.contributor.authorHoteit, Ibrahim
dc.contributor.authorWheeler, Mary Fanett
dc.date.accessioned2015-08-03T11:52:56Z
dc.date.available2015-08-03T11:52:56Z
dc.date.issued2014-04-15
dc.identifier.issn00359009
dc.identifier.doi10.1002/qj.2245
dc.identifier.urihttp://hdl.handle.net/10754/563498
dc.description.abstractWe present an efficient nonlinear data assimilation filter that combines particle filtering with the nested sampling algorithm. Particle filters (PF) utilize a set of weighted particles as a discrete representation of probability distribution functions (PDF). These particles are propagated through the system dynamics and their weights are sequentially updated based on the likelihood of the observed data. Nested sampling (NS) is an efficient sampling algorithm that iteratively builds a discrete representation of the posterior distributions by focusing a set of particles to high-likelihood regions. This would allow the representation of the posterior PDF with a smaller number of particles and reduce the effects of the curse of dimensionality. The proposed nested sampling particle filter (NSPF) iteratively builds the posterior distribution by applying a constrained sampling from the prior distribution to obtain particles in high-likelihood regions of the search space, resulting in a reduction of the number of particles required for an efficient behaviour of particle filters. Numerical experiments with the 3-dimensional Lorenz63 and the 40-dimensional Lorenz96 models show that NSPF outperforms PF in accuracy with a relatively smaller number of particles. © 2013 Royal Meteorological Society.
dc.publisherWiley-Blackwell
dc.subjectNested sampling
dc.subjectParticle filters
dc.subjectSequential data assimilation
dc.titleA nested sampling particle filter for nonlinear data assimilation
dc.typeArticle
dc.contributor.departmentEarth Science and Engineering Program
dc.contributor.departmentPhysical Sciences and Engineering (PSE) Division
dc.contributor.departmentEnvironmental Science and Engineering Program
dc.contributor.departmentEarth Fluid Modeling and Prediction Group
dc.identifier.journalQuarterly Journal of the Royal Meteorological Society
dc.contributor.institutionInstitute for Computational Engineering and Sciences (ICES), University of Texas, Austin, TX, United States
dc.contributor.institutionInstitute of Petroleum Engineering, Heriot-Watt University, Edinburgh, United Kingdom
kaust.personHoteit, Ibrahim


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