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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
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Volume 25, issue 2
Volume 25, issue 2
Nonlin. Processes Geophys., 25, 315-334, 2018
https://doi.org/10.5194/npg-25-315-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Special issue: Numerical modeling, predictability and data assimilation in...

Nonlin. Processes Geophys., 25, 315-334, 2018
https://doi.org/10.5194/npg-25-315-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Volume 25, issue 2

Research article 27 Apr 2018

Research article | 27 Apr 2018

Quasi-static ensemble variational data assimilation: a theoretical and numerical study with the iterative ensemble Kalman smoother

Anthony Fillion et al.
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AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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AR: Author's response | RR: Referee report | ED: Editor decision
AR by Anthony Fillion on behalf of the Authors (20 Feb 2018)  Author's response    Manuscript
ED: Publish subject to minor revisions (review by editor) (12 Mar 2018) by Alberto Carrassi
AR by Anthony Fillion on behalf of the Authors (14 Mar 2018)  Author's response    Manuscript
ED: Publish as is (15 Mar 2018) by Alberto Carrassi
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Numerical modeling, predictability and data assimilation in...
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This study generalizes a paper by Pires et al. (1996) to state-of-the-art data assimilation techniques, such as the iterative ensemble Kalman smoother (IEnKS). We show that the longer the time window over which observations are assimilated, the better the accuracy of the IEnKS. Beyond a critical time length that we estimate, we show that this accuracy finally degrades. We show that the use of the quasi-static minimizations but generalized to the IEnKS yields a significantly improved accuracy.
This study generalizes a paper by Pires et al. (1996) to state-of-the-art data assimilation...
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