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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union

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https://doi.org/10.5194/npg-2018-6
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Research article
24 Jan 2018
Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Nonlinear Processes in Geophysics (NPG).
Ensemble Variational Assimilation as a Probabilistic Estimator. Part II: The fully non-linear case
Mohamed Jardak1,2 and Olivier Talagrand1 1LMD/IPSL, CNRS, ENS, PSL Research University, 75231, Paris, France
2Data Assimilation and Ensembles Research & Development Group, Met Office, Exeter, Devon, UK
Abstract. In Part II, the method of Ensemble Variational Assimilation (EnsVAR) is implemented in fully nonlinear conditions on the Lorenz-96 chaotic 40-parameter model. In the case of strong-constraint assimilation, it requires to be used in association with the method of Quasi-Static Variational Assimilation (QSVA). It then produces ensembles which possess as much reliability and resolution as in the linear case, and its performance is at least as good as that of Ensemble Kalman Filter and Particle Filter. On the other hand, ensembles consisting of solutions that correspond to the absolute minimum of the objective function (as identified from the minimizations without QSVA) are signif- icantly biased. In the case of weak-constraint assimilation, EnsVAR is fully successful without need to resort to QSVA.

Citation: Jardak, M. and Talagrand, O.: Ensemble Variational Assimilation as a Probabilistic Estimator. Part II: The fully non-linear case, Nonlin. Processes Geophys. Discuss., https://doi.org/10.5194/npg-2018-6, in review, 2018.
Mohamed Jardak and Olivier Talagrand
Mohamed Jardak and Olivier Talagrand
Mohamed Jardak and Olivier Talagrand

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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 nonlinear 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.
EnsVAR is fundamentally successful in that, even in conditions where Bayesianity cannot be...
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