Articles | Volume 13, issue 8
Geosci. Model Dev., 13, 3607–3625, 2020
https://doi.org/10.5194/gmd-13-3607-2020
Geosci. Model Dev., 13, 3607–3625, 2020
https://doi.org/10.5194/gmd-13-3607-2020

Development and technical paper 19 Aug 2020

Development and technical paper | 19 Aug 2020

An offline framework for high-dimensional ensemble Kalman filters to reduce the time to solution

Yongjun Zheng et al.

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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Clément Albergel on behalf of the Authors (25 Feb 2020)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (02 Mar 2020) by Adrian Sandu
RR by Elias D. Nino-Ruiz (26 Apr 2020)
ED: Publish subject to minor revisions (review by editor) (10 Jun 2020) by Adrian Sandu
AR by Clément Albergel on behalf of the Authors (19 Jun 2020)  Author's response    Manuscript
ED: Publish as is (07 Jul 2020) by Adrian Sandu
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Short summary
This study proposes a sophisticated dynamically running job scheme as well as an innovative parallel IO algorithm to reduce the time to solution of an offline framework for high-dimensional ensemble Kalman filters. The offline and online modes of ensemble Kalman filters are built to comprehensively assess their time to solution efficiencies. The offline mode is substantially faster than the online mode in terms of time to solution, especially for large-scale assimilation problems.