AffiliationUniv Arizona, Dept Math
MetadataShow full item record
CitationMorzfeld 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
Rights© 2018 Royal Meteorological Society.
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AbstractGiven 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.
Note12 month embargo; published online: 10 February 2018
VersionFinal accepted manuscript
SponsorsOffice 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