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

Fillion, Anthony; Bocquet, Marc; Gratton, Serge

doi:https://doi.org/10.5194/npg-25-315-2018

Articles | Volume 25, issue 2

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...

https://doi.org/10.5194/npg-25-315-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | 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, Marc Bocquet, and Serge Gratton

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Final revised paper (published on 27 Apr 2018)
Preprint (discussion started on 16 Nov 2017)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC1: 'Review of the manuscript “Quasi static ensemble variational data assimilation” by Anthony Fillion, Marc Bocquet and Serge Gratton', Carlos Pires, 15 Dec 2017
RC2: 'Comments on the NPGD paper by Fillion et. al.', Anonymous Referee #2, 19 Jan 2018
AC1: 'Response to Referee 1', Anthony Fillion, 20 Feb 2018
AC2: 'Response to Referee 2', Anthony Fillion, 20 Feb 2018

Peer-review completion

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 Natale 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 Natale Alberto Carrassi

Short summary

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.