A Bayesian approach to multivariate adaptive localization in ensemble-based data assimilation with time-dependent extensions

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dc.contributor.authorPopov, Andrey A.en
dc.contributor.authorSandu, Adrianen
dc.contributor.departmentComputer Scienceen
dc.date.accessioned2019-12-19T17:37:48Zen
dc.date.available2019-12-19T17:37:48Zen
dc.date.issued2019-06-14en
dc.description.abstractEver since its inception, the ensemble Kalman filter (EnKF) has elicited many heuristic approaches that sought to improve it. One such method is covariance localization, which alleviates spurious correlations due to finite ensemble sizes by using relevant spatial correlation information. Adaptive localization techniques account for how correlations change in time and space, in order to obtain improved covariance estimates. This work develops a Bayesian approach to adaptive Schur-product localization for the deterministic ensemble Kalman filter (DEnKF) and extends it to support multiple radii of influence. We test the proposed adaptive localization using the toy Lorenz’96 problem and a more realistic 1.5-layer quasi-geostrophic model. Results with the toy problem show that the multivariate approach informs us that strongly observed variables can tolerate larger localization radii. The univariate approach leads to markedly improved filter performance for the realistic geophysical model, with a reduction in error by as much as 33 %.en
dc.description.sponsorshipThis research has been supported by the Air Force Office of Scientific Research (grant no. DDDAS FA9550-17- 1-0015), the National Science Foundation, Division of Computing and Communication Foundations (grant no. CCF-1613905), and the National Science Foundation, Division of Advanced Cyberinfrastructure (grant no. ACI-17097276).en
dc.format.extent14 pagesen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.5194/npg-26-109-2019en
dc.identifier.urihttp://hdl.handle.net/10919/96034en
dc.identifier.volume26en
dc.language.isoenen
dc.publisherCopernicus Publicationsen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.titleA Bayesian approach to multivariate adaptive localization in ensemble-based data assimilation with time-dependent extensionsen
dc.title.serialNonlinear Processes in Geophysicsen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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