Hybrid Levenberg–Marquardt and weak-constraint ensemble Kalman smoother method

Mandel, J.; Bergou, E.; Gürol, S.; Gratton, S.; Kasanický, I.

doi:https://doi.org/10.5194/npg-23-59-2016

Articles | Volume 23, issue 2

https://doi.org/10.5194/npg-23-59-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

https://doi.org/10.5194/npg-23-59-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 23, issue 2

Research article

|

11 Mar 2016

Research article |

| 11 Mar 2016

Hybrid Levenberg–Marquardt and weak-constraint ensemble Kalman smoother method

J. Mandel, E. Bergou, S. Gürol, S. Gratton, and I. Kasanický

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Final revised paper (published on 11 Mar 2016)
Preprint (discussion started on 26 May 2015)

Interactive discussion

Status: closed

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

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- Supplement

RC C270: 'Review of Mandel et al, submitted to NPGD', Emmanuel Cosme, 09 Jul 2015
RC C283: 'Review of "Hybrid Levenberg-Marquardt and weak constraint ensemble Kalman smoother method''', Anonymous Referee #2, 21 Jul 2015
EC C296: 'Editor comment', Olivier Talagrand, 03 Aug 2015
- AC C431: 'Final response to the Reviewers and the Editor', Jan Mandel, 16 Sep 2015

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

AR by Jan Mandel on behalf of the Authors (30 Nov 2015) Author's response Manuscript

ED: Referee Nomination & Report Request started (03 Dec 2015) by Olivier Talagrand

RR by Anonymous Referee #2 (04 Jan 2016)

RR by Emmanuel Cosme (21 Jan 2016)

ED: Publish subject to minor revisions (further review by Editor) (22 Jan 2016) by Olivier Talagrand

AR by Jan Mandel on behalf of the Authors (01 Feb 2016) Author's response Manuscript

ED: Publish subject to technical corrections (04 Feb 2016) by Olivier Talagrand

AR by Jan Mandel on behalf of the Authors (06 Feb 2016) Manuscript

Short summary

A stochastic method, the ensemble Kalman smoother (EnKS), is proposed as a linear solver in four-dimensional variational data assimilation (4DVAR). The method approaches 4DVAR for large ensembles. Regularization provides global convergence, and it is implemented as an additional artificial observation. Since the EnKS is uncoupled from the insides of the 4DVAR, any version of EnKS can be used.