Publisher
WILEYCitation
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.3256Rights
© 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 2018ISSN
00359009DOI
10.1002/qj.3256Version
Final accepted manuscriptSponsors
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 fellowshipAdditional Links
http://doi.wiley.com/10.1002/qj.3256ae974a485f413a2113503eed53cd6c53
10.1002/qj.3256