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    Variational particle smoothers and their localization

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    Author
    Morzfeld, M.
    Hodyss, D.
    Poterjoy, J.
    Affiliation
    Univ Arizona, Dept Math
    Issue Date
    2018-04
    Keywords
    data assimilation
    particle filters
    variational methods
    
    Metadata
    Show full item record
    Publisher
    WILEY
    Citation
    Morzfeld M, Hodyss D, Poterjoy J. Variational particle smoothers and their localization. Q J R Meteorol Soc. 2018;144:806–825. https://doi.org/10.1002/qj.3256
    Journal
    QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY
    Rights
    © 2018 Royal Meteorological Society.
    Collection Information
    This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
    Abstract
    Given the success of 4D-variational methods (4D-Var) in numerical weather prediction, and recent efforts to merge ensemble Kalman filters with 4D-Var, we revisit how one can use importance sampling and particle filtering ideas within a 4D-Var framework. This leads us to variational particle smoothers (varPS) and we study how weight-localization can prevent the collapse of varPS in high-dimensional problems. We also discuss the relevance of (localized) weights in near-Gaussian problems. We test our ideas on the Lorenz'96 model of dimensions n = 40, n = 400, and n = 2, 000. In our numerical experiments the localized varPS does not collapse and yields results comparable to ensemble formulations of 4D-Var, while tuned EnKFs and the local particle filter lead to larger estimation errors. Additional numerical experiments suggest that using localized weights may not yield significant advantages over unweighted or linearized solutions in near-Gaussian problems.
    Note
    12 month embargo; published online: 10 February 2018
    ISSN
    00359009
    DOI
    10.1002/qj.3256
    Version
    Final accepted manuscript
    Sponsors
    Office of Naval Research [N00173-17-2-C003, PE-0601153N]; National Science Foundation [DMS-1619630]; Alfred P. Sloan Foundation; National Research Council Research Associateship Program fellowship
    Additional Links
    http://doi.wiley.com/10.1002/qj.3256
    ae974a485f413a2113503eed53cd6c53
    10.1002/qj.3256
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