Articles | Volume 25, issue 3
Nonlin. Processes Geophys., 25, 589–604, 2018

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

Nonlin. Processes Geophys., 25, 589–604, 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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Cited articles

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Bardsley, J. M., Solonen, A., Haario, H., and Laine, M.: Randomize-Then-Optimize: A Method for Sampling from Posterior Distributions in Nonlinear Inverse Problems, SIAM J. Sci. Comput., 36, A1895–A1910, 2014. a
Bocquet, M. and Sakov, P.: Joint state and parameter estimation with an iterative ensemble Kalman smoother, Nonlin. Processes Geophys., 20, 803–818,, 2013. a, b
Bocquet, M. and Sakov, P.: An iterative ensemble Kalman smoother, Q. J. Roy. Meteor. Soc., 140, 1521–1535,, 2014. a, b
Bocquet, M. and Carrassi, A.: Four-dimensional ensemble variational data assimilation and the unstable subspace, Tellus A, 69, 1304504,, 2017. a
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.