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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 2
Nonlin. Processes Geophys., 25, 315–334, 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, 315–334, 2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 27 Apr 2018

Research article | 27 Apr 2018

Quasi-static ensemble variational data assimilation: a theoretical and numerical study with the iterative ensemble Kalman smoother

Anthony Fillion et al.

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Related subject area

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Cited articles

Asch, M., Bocquet, M., and Nodet, M.: Data assimilation: methods, algorithms, and applications, Fundamentals of Algorithms, Society for Industrial and Applied Mathematics, Philadelphia, USA, 306 pp., 2016.
Bishop, C.: Pattern Recognition and Machine Learning, Information Science and Statistics, Springer-Verlag, New York, USA, 738 pp., 2006.
Björck, Å.: Numerical methods for least squares problems, Society for Industrial and Applied Mathematics, Philadelphia, USA, 408 pp.,, 1996.
Bocquet, M.: Ensemble Kalman filtering without the intrinsic need for inflation, Nonlin. Processes Geophys., 18, 735–750,, 2011.
Bocquet, M.: Localization and the iterative ensemble Kalman smoother, Q. J. Roy. Meteor. Soc., 142, 1075–1089,, 2016.
Publications Copernicus
Short summary
This study generalizes a paper by Pires et al. (1996) to state-of-the-art data assimilation techniques, such as the iterative ensemble Kalman smoother (IEnKS). We show that the longer the time window over which observations are assimilated, the better the accuracy of the IEnKS. Beyond a critical time length that we estimate, we show that this accuracy finally degrades. We show that the use of the quasi-static minimizations but generalized to the IEnKS yields a significantly improved accuracy.
This study generalizes a paper by Pires et al. (1996) to state-of-the-art data assimilation...