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
 | Highlight paper
03 Nov 2015
Research article  | Highlight paper | 03 Nov 2015

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

M. Bocquet et al.

Related authors

New plume comparison metrics for the inversion of passive gases emissions
Pierre J. Vanderbecken, Joffrey Dumont Le Brazidec, Alban Farchi, Marc Bocquet, Yelva Roustan, Élise Potier, and Grégoire Broquet
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2022-226,https://doi.org/10.5194/amt-2022-226, 2022
Preprint under review for AMT
Short summary
A fast, single-iteration ensemble Kalman smoother for sequential data assimilation
Colin Grudzien and Marc Bocquet
Geosci. Model Dev., 15, 7641–7681, https://doi.org/10.5194/gmd-15-7641-2022,https://doi.org/10.5194/gmd-15-7641-2022, 2022
Short summary
Bayesian transdimensional inverse reconstruction of the 137Cs Fukushima-Daiichi release
Joffrey Dumont Le Brazidec, Marc Bocquet, Olivier Saunier, and Yelva Roustan
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-168,https://doi.org/10.5194/gmd-2022-168, 2022
Revised manuscript under review for GMD
Short summary
Quantification of uncertainties in the assessment of an atmospheric release source applied to the autumn 2017 106Ru event
Joffrey Dumont Le Brazidec, Marc Bocquet, Olivier Saunier, and Yelva Roustan
Atmos. Chem. Phys., 21, 13247–13267, https://doi.org/10.5194/acp-21-13247-2021,https://doi.org/10.5194/acp-21-13247-2021, 2021
Short summary
On the numerical integration of the Lorenz-96 model, with scalar additive noise, for benchmark twin experiments
Colin Grudzien, Marc Bocquet, and Alberto Carrassi
Geosci. Model Dev., 13, 1903–1924, https://doi.org/10.5194/gmd-13-1903-2020,https://doi.org/10.5194/gmd-13-1903-2020, 2020
Short summary

Related subject area

Subject: Predictability, probabilistic forecasts, data assimilation, inverse problems | Topic: Climate, atmosphere, ocean, hydrology, cryosphere, biosphere
Using a hybrid optimal interpolation–ensemble Kalman filter for the Canadian Precipitation Analysis
Dikraa Khedhaouiria, Stéphane Bélair, Vincent Fortin, Guy Roy, and Franck Lespinas
Nonlin. Processes Geophys., 29, 329–344, https://doi.org/10.5194/npg-29-329-2022,https://doi.org/10.5194/npg-29-329-2022, 2022
Short summary
Applying prior correlations for ensemble-based spatial localization
Chu-Chun Chang and Eugenia Kalnay
Nonlin. Processes Geophys., 29, 317–327, https://doi.org/10.5194/npg-29-317-2022,https://doi.org/10.5194/npg-29-317-2022, 2022
Short summary
A stochastic covariance shrinkage approach to particle rejuvenation in the ensemble transform particle filter
Andrey A. Popov, Amit N. Subrahmanya, and Adrian Sandu
Nonlin. Processes Geophys., 29, 241–253, https://doi.org/10.5194/npg-29-241-2022,https://doi.org/10.5194/npg-29-241-2022, 2022
Short summary
Control simulation experiment with Lorenz's butterfly attractor
Takemasa Miyoshi and Qiwen Sun
Nonlin. Processes Geophys., 29, 133–139, https://doi.org/10.5194/npg-29-133-2022,https://doi.org/10.5194/npg-29-133-2022, 2022
Short summary
Ensemble Riemannian data assimilation: towards large-scale dynamical systems
Sagar K. Tamang, Ardeshir Ebtehaj, Peter Jan van Leeuwen, Gilad Lerman, and Efi Foufoula-Georgiou
Nonlin. Processes Geophys., 29, 77–92, https://doi.org/10.5194/npg-29-77-2022,https://doi.org/10.5194/npg-29-77-2022, 2022
Short summary

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.
Download
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.