Articles | Volume 26, issue 2
Nonlin. Processes Geophys., 26, 109–122, 2019
https://doi.org/10.5194/npg-26-109-2019
Nonlin. Processes Geophys., 26, 109–122, 2019
https://doi.org/10.5194/npg-26-109-2019

Research article 14 Jun 2019

Research article | 14 Jun 2019

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

Andrey A. Popov and Adrian Sandu

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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Andrey Popov on behalf of the Authors (28 Jan 2019)  Author's response    Manuscript
ED: Reconsider after major revisions (further review by editor and referees) (05 Feb 2019) by Zoltan Toth
AR by Andrey Popov on behalf of the Authors (19 Mar 2019)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (28 Mar 2019) by Zoltan Toth
RR by Anonymous Referee #2 (16 Apr 2019)
RR by Anonymous Referee #1 (01 May 2019)
ED: Publish subject to minor revisions (review by editor) (03 May 2019) by Zoltan Toth
AR by Andrey Popov on behalf of the Authors (12 May 2019)  Author's response    Manuscript
ED: Publish as is (21 May 2019) by Zoltan Toth
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Short summary
This work has to do with a small part of existing algorithms that are used in applications such as predicting the weather. We provide empirical evidence that our new technique works well on small but representative models. This might lead to creation of a better weather forecast and potentially save lives as in the case of hurricane prediction.