Articles | Volume 22, issue 6
Nonlin. Processes Geophys., 22, 645–662, 2015
https://doi.org/10.5194/npg-22-645-2015
Nonlin. Processes Geophys., 22, 645–662, 2015
https://doi.org/10.5194/npg-22-645-2015

Research article 03 Nov 2015

Research article | 03 Nov 2015

Expanding the validity of the ensemble Kalman filter without the intrinsic need for inflation

M. Bocquet et al.

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Cited articles

Anderson, J. L.: An adaptive covariance inflation error correction algorithm for ensemble filters, Tellus A, 59, 210–224, 2007.
Anderson, J. L. and Anderson, S. L.: A Monte Carlo Implementation of the Nonlinear Filtering Problem to Produce Ensemble Assimilations and Forecasts, Mon. Weather Rev., 127, 2741–2758, 1999.
Bishop, C. H., Etherton, B. J., and Majumdar, S. J.: Adaptive Sampling with the Ensemble Transform Kalman Filter. Part I: Theoretical Aspects, Mon. Weather Rev., 129, 420–436, 2001.
Bishop, C. M. (Ed.): Pattern Recognition and Machine Learning, Springer-Verlag New-York Inc, 2006.
Bocquet, M.: Ensemble Kalman filtering without the intrinsic need for inflation, Nonlin. Processes Geophys., 18, 735–750, https://doi.org/10.5194/npg-18-735-2011, 2011.
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Short summary
The popular data assimilation technique known as the ensemble Kalman filter (EnKF) suffers from sampling errors due to the limited size of the ensemble. This deficiency is usually cured by inflating the sampled error covariances and by using localization. This paper further develops and discusses the finite-size EnKF, or EnKF-N, a variant of the EnKF that does not require inflation. It expands the use of the EnKF-N to a wider range of dynamical regimes.