Articles | Volume 22, issue 6
https://doi.org/10.5194/npg-22-645-2015
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, P. N. Raanes, and A. Hannart

Related authors

Bridging classical data assimilation and optimal transport
Marc Bocquet, Pierre J. Vanderbecken, Alban Farchi, Joffrey Dumont Le Brazidec, and Yelva Roustan
EGUsphere, https://doi.org/10.5194/egusphere-2023-2755,https://doi.org/10.5194/egusphere-2023-2755, 2023
Short summary
Representation learning with unconditional denoising diffusion models for dynamical systems
Tobias Sebastian Finn, Lucas Disson, Alban Farchi, Marc Bocquet, and Charlotte Durand
EGUsphere, https://doi.org/10.5194/egusphere-2023-2261,https://doi.org/10.5194/egusphere-2023-2261, 2023
Short summary
Multivariate state and parameter estimation with data assimilation on sea-ice models using a Maxwell-Elasto-Brittle rheology
Yumeng Chen, Polly Smith, Alberto Carrassi, Ivo Pasmans, Laurent Bertino, Marc Bocquet, Tobias Sebastian Finn, Pierre Rampal, and Véronique Dansereau
EGUsphere, https://doi.org/10.5194/egusphere-2023-1809,https://doi.org/10.5194/egusphere-2023-1809, 2023
Short summary
Data-driven surrogate modeling of high-resolution sea-ice thickness in the Arctic
Charlotte Durand, Tobias Sebastian Finn, Alban Farchi, Marc Bocquet, and Einar Òlason
EGUsphere, https://doi.org/10.5194/egusphere-2023-1384,https://doi.org/10.5194/egusphere-2023-1384, 2023
Short summary
Deep learning applied to CO2 power plant emissions quantification using simulated satellite images
Joffrey Dumont Le Brazidec, Pierre Vanderbecken, Alban Farchi, Grégoire Broquet, Gerrit Kuhlmann, and Marc Bocquet
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-142,https://doi.org/10.5194/gmd-2023-142, 2023
Revised manuscript accepted for GMD
Short summary

Related subject area

Subject: Predictability, probabilistic forecasts, data assimilation, inverse problems | Topic: Climate, atmosphere, ocean, hydrology, cryosphere, biosphere
Robust weather-adaptive post-processing using model output statistics random forests
Thomas Muschinski, Georg J. Mayr, Achim Zeileis, and Thorsten Simon
Nonlin. Processes Geophys., 30, 503–514, https://doi.org/10.5194/npg-30-503-2023,https://doi.org/10.5194/npg-30-503-2023, 2023
Short summary
Comparative study of strongly and weakly coupled data assimilation with a global land–atmosphere coupled model
Kenta Kurosawa, Shunji Kotsuki, and Takemasa Miyoshi
Nonlin. Processes Geophys., 30, 457–479, https://doi.org/10.5194/npg-30-457-2023,https://doi.org/10.5194/npg-30-457-2023, 2023
Short summary
How far can the statistical error estimation problem be closed by collocated data?
Annika Vogel and Richard Ménard
Nonlin. Processes Geophys., 30, 375–398, https://doi.org/10.5194/npg-30-375-2023,https://doi.org/10.5194/npg-30-375-2023, 2023
Short summary
Using orthogonal vectors to improve the ensemble space of the ensemble Kalman filter and its effect on data assimilation and forecasting
Yung-Yun Cheng, Shu-Chih Yang, Zhe-Hui Lin, and Yung-An Lee
Nonlin. Processes Geophys., 30, 289–297, https://doi.org/10.5194/npg-30-289-2023,https://doi.org/10.5194/npg-30-289-2023, 2023
Short summary
Review article: Towards strongly coupled ensemble data assimilation with additional improvements from machine learning
Eugenia Kalnay, Travis Sluka, Takuma Yoshida, Cheng Da, and Safa Mote
Nonlin. Processes Geophys., 30, 217–236, https://doi.org/10.5194/npg-30-217-2023,https://doi.org/10.5194/npg-30-217-2023, 2023
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.