A nested sampling particle filter for nonlinear data assimilation

Handle URI:
http://hdl.handle.net/10754/563498
Title:
A nested sampling particle filter for nonlinear data assimilation
Authors:
Elsheikh, Ahmed H.; Hoteit, Ibrahim ( 0000-0002-3751-4393 ) ; Wheeler, Mary Fanett
Abstract:
We 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.
KAUST Department:
Earth Science and Engineering Program; Physical Sciences and Engineering (PSE) Division; Environmental Science and Engineering Program; Earth Fluid Modeling and Prediction Group
Publisher:
Wiley-Blackwell
Journal:
Quarterly Journal of the Royal Meteorological Society
Issue Date:
15-Apr-2014
DOI:
10.1002/qj.2245
Type:
Article
ISSN:
00359009
Appears in Collections:
Articles; Environmental Science and Engineering Program; Physical Sciences and Engineering (PSE) Division; Earth Science and Engineering Program

Full metadata record

DC FieldValue Language
dc.contributor.authorElsheikh, Ahmed H.en
dc.contributor.authorHoteit, Ibrahimen
dc.contributor.authorWheeler, Mary Fanetten
dc.date.accessioned2015-08-03T11:52:56Zen
dc.date.available2015-08-03T11:52:56Zen
dc.date.issued2014-04-15en
dc.identifier.issn00359009en
dc.identifier.doi10.1002/qj.2245en
dc.identifier.urihttp://hdl.handle.net/10754/563498en
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.en
dc.publisherWiley-Blackwellen
dc.subjectNested samplingen
dc.subjectParticle filtersen
dc.subjectSequential data assimilationen
dc.titleA nested sampling particle filter for nonlinear data assimilationen
dc.typeArticleen
dc.contributor.departmentEarth Science and Engineering Programen
dc.contributor.departmentPhysical Sciences and Engineering (PSE) Divisionen
dc.contributor.departmentEnvironmental Science and Engineering Programen
dc.contributor.departmentEarth Fluid Modeling and Prediction Groupen
dc.identifier.journalQuarterly Journal of the Royal Meteorological Societyen
dc.contributor.institutionInstitute for Computational Engineering and Sciences (ICES), University of Texas, Austin, TX, United Statesen
dc.contributor.institutionInstitute of Petroleum Engineering, Heriot-Watt University, Edinburgh, United Kingdomen
kaust.authorHoteit, Ibrahimen
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