Ensemble variational assimilation as a probabilistic estimator –  Part 2: The fully  non-linear case

Jardak, Mohamed; Talagrand, Olivier

doi:https://doi.org/10.5194/npg-25-589-2018

Articles | Volume 25, issue 3

https://doi.org/10.5194/npg-25-589-2018

© 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-25-589-2018

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

Articles | Volume 25, issue 3

Research article

|

24 Aug 2018

Research article |

| 24 Aug 2018

Ensemble variational assimilation as a probabilistic estimator – Part 2: The fully non-linear case

Mohamed Jardak and Olivier Talagrand

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Final revised paper (published on 24 Aug 2018)
Preprint (discussion started on 24 Jan 2018)

Interactive discussion

Status: closed

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

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- Supplement

RC1: 'Comments on Ensemble Variational Assimilation as a Probabilistic Estimator. Part II: The fully nonlinear case, by M. Jardak and O. Talagrand', Massimo Bonavita, 11 Feb 2018
- AC1: 'Response to M. Bonavita Part2', Mohamed Jardak, 09 Apr 2018
RC2: 'Review of Ensemble Variational Assimilation as a Probabilistic Estimator. Variational Assimilation as a Probabil Part II: The fully non-linear case by M. Jardak and O. Talagrand', Marc Bocquet, 12 Mar 2018
- AC2: 'Response to M. Bocquet Part2', Mohamed Jardak, 09 Apr 2018

Peer-review completion

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

AR by Mohamed Jardak on behalf of the Authors (04 Jul 2018) Author's response Manuscript

ED: Referee Nomination & Report Request started (05 Jul 2018) by Natale Alberto Carrassi

RR by Massimo Bonavita (11 Jul 2018)

RR by Marc Bocquet (18 Jul 2018)

ED: Publish subject to technical corrections (19 Jul 2018) by Natale Alberto Carrassi

AR by Mohamed Jardak on behalf of the Authors (01 Aug 2018) Author's response Manuscript

Short summary

EnsVAR is fundamentally successful in that, even in conditions where Bayesianity cannot be expected, it produces ensembles which possess a high degree of statistical reliability. In non-linear strong-constraint cases, EnsVAR has been successful here only through the use of quasi-static variational assimilation. In the weak-constraint case, without QSVA, EnsVAR provided new evidence as to the favourable effect.