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    A nested sampling particle filter for nonlinear data assimilation

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    Type
    Article
    Authors
    Elsheikh, Ahmed H.
    Hoteit, Ibrahim cc
    Wheeler, Mary Fanett
    KAUST Department
    Earth Fluid Modeling and Prediction Group
    Earth Science and Engineering Program
    Environmental Science and Engineering Program
    Physical Science and Engineering (PSE) Division
    Date
    2014-04-15
    Online Publication Date
    2014-04-15
    Print Publication Date
    2014-07
    Permanent link to this record
    http://hdl.handle.net/10754/563498
    
    Metadata
    Show full item record
    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.
    Citation
    Elsheikh, A. H., Hoteit, I., & Wheeler, M. F. (2014). A nested sampling particle filter for nonlinear data assimilation. Quarterly Journal of the Royal Meteorological Society, 140(682), 1640–1653. doi:10.1002/qj.2245
    Publisher
    Wiley
    Journal
    Quarterly Journal of the Royal Meteorological Society
    DOI
    10.1002/qj.2245
    ae974a485f413a2113503eed53cd6c53
    10.1002/qj.2245
    Scopus Count
    Collections
    Articles; Environmental Science and Engineering Program; Physical Science and Engineering (PSE) Division; Earth Science and Engineering Program

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