Articles | Volume 25, issue 2
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, Marc Bocquet, and Serge Gratton

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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.
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.