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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 3
Nonlin. Processes Geophys., 25, 565–587, 2018
https://doi.org/10.5194/npg-25-565-2018
© 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, 565–587, 2018
https://doi.org/10.5194/npg-25-565-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 24 Aug 2018

Research article | 24 Aug 2018

Ensemble variational assimilation as a probabilistic estimator – Part 1: The linear and weak non-linear case

Mohamed Jardak and Olivier Talagrand

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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 (03 Jul 2018)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (04 Jul 2018) by Alberto Carrassi
RR by Massimo Bonavita (09 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 (30 Jul 2018)  Author's response    Manuscript
Publications Copernicus
Short summary
Ensemble variational assimilation (EnsVAR) has been implemented on two small-dimension non-linear chaotic toy models, as well as on a linearized version of those models. In the linear case, EnsVAR is exactly Bayesian and produced highly reliable ensembles. In the non-linear case, EnsVAR, implemented on temporal windows on the order of magnitude of the predictability time of the systems, shows as good performance as in the exactly linear case. EnsVar is as good an estimator as EnKF and PF.
Ensemble variational assimilation (EnsVAR) has been implemented on two small-dimension...
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