Popov, Andrey A.Subrahmanya, Amit N.Sandu, Adrian2023-02-272023-02-272022-06-221023-5809http://hdl.handle.net/10919/113987Rejuvenation in particle filters is necessary to prevent the collapse of the weights when the number of particles is insufficient to properly sample the high-probability regions of the state space. Rejuvenation is often implemented in a heuristic manner by the addition of random noise that widens the support of the ensemble. This work aims at improving canonical rejuvenation methodology by the introduction of additional prior information obtained from climatological samples; the dynamical particles used for importance sampling are augmented with samples obtained from stochastic covariance shrinkage. A localized variant of the proposed method is developed. Numerical experiments with the Lorenz '63 model show that modified filters significantly improve the analyses for low dynamical ensemble sizes. Furthermore, localization experiments with the Lorenz '96 model show that the proposed methodology is extendable to larger systems.Pages 241-25313 page(s)application/pdfenIn CopyrightData assimilationKalman filterArticle - Refereed2023-02-25https://doi.org/10.5194/npg-29-241-2022292Sandu, Adrian [0000-0002-5380-0103]1607-7946