Articles | Volume 25, issue 3
Nonlin. Processes Geophys., 25, 589–604, 2018
https://doi.org/10.5194/npg-25-589-2018

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

Nonlin. Processes Geophys., 25, 589–604, 2018
https://doi.org/10.5194/npg-25-589-2018

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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Interactive discussion

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 Mohamed Jardak on behalf of the Authors (04 Jul 2018)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (05 Jul 2018) by 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 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.