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Volume 25, issue 3 | Copyright

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

Nonlin. Processes Geophys., 25, 589-604, 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 2: The fully non-linear case

Mohamed Jardak1,2 and Olivier Talagrand1 Mohamed Jardak and Olivier Talagrand
  • 1LMD/IPSL, CNRS, ENS, PSL Research University, 75231, Paris, France
  • 2Data Assimilation and Ensembles Research & Development Group, Met Office, Exeter, Devon, UK

Abstract. The method of ensemble variational assimilation (EnsVAR), also known as ensemble of data assimilations (EDA), is implemented in fully non-linear conditions on the Lorenz-96 chaotic 40-parameter model. In the case of strong-constraint assimilation, it requires 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 (EnKF) and particle filter (PF). 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 significantly biased. In the case of weak-constraint assimilation, EnsVAR is fully successful without need for QSVA.

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