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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
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NPG | Articles | Volume 26, issue 3
Nonlin. Processes Geophys., 26, 175–193, 2019
https://doi.org/10.5194/npg-26-175-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Nonlin. Processes Geophys., 26, 175–193, 2019
https://doi.org/10.5194/npg-26-175-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 24 Jul 2019

Research article | 24 Jul 2019

Data assimilation using adaptive, non-conservative, moving mesh models

Ali Aydoğdu et al.

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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 Ali Aydoğdu on behalf of the Authors (17 Jun 2019)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (26 Jun 2019) by Wansuo Duan
RR by Anonymous Referee #1 (26 Jun 2019)
RR by Anonymous Referee #2 (27 Jun 2019)
ED: Publish as is (01 Jul 2019) by Wansuo Duan
Publications Copernicus
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Short summary
Computational models involving adaptive meshes can both evolve dynamically and be remeshed. Remeshing means that the state vector dimension changes in time and across ensemble members, making the ensemble Kalman filter (EnKF) unsuitable for assimilation of observational data. We develop a modification in which analysis is performed on a fixed uniform grid onto which the ensemble is mapped, with resolution relating to the remeshing criteria. The approach is successfully tested on two 1-D models.
Computational models involving adaptive meshes can both evolve dynamically and be remeshed....
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