Articles | Volume 25, issue 2
https://doi.org/10.5194/npg-25-315-2018
https://doi.org/10.5194/npg-25-315-2018
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

Related authors

A Probabilistic Approach to Wildfire Spread Prediction Using a Denoising Diffusion Surrogate Model
Wenbo Yu, Anirbit Ghosh, Tobias Sebastian Finn, Rossella Arcucci, Marc Bocquet, and Sibo Cheng
EGUsphere, https://doi.org/10.5194/egusphere-2025-2836,https://doi.org/10.5194/egusphere-2025-2836, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Quantification of CO2 hotspot emissions from OCO-3 SAM CO2 satellite images using deep learning methods
Joffrey Dumont Le Brazidec, Pierre Vanderbecken, Alban Farchi, Grégoire Broquet, Gerrit Kuhlmann, and Marc Bocquet
Geosci. Model Dev., 18, 3607–3622, https://doi.org/10.5194/gmd-18-3607-2025,https://doi.org/10.5194/gmd-18-3607-2025, 2025
Short summary
Four-dimensional variational data assimilation with a sea-ice thickness emulator
Charlotte Durand, Tobias Sebastian Finn, Alban Farchi, Marc Bocquet, Julien Brajard, and Laurent Bertino
EGUsphere, https://doi.org/10.5194/egusphere-2024-4028,https://doi.org/10.5194/egusphere-2024-4028, 2025
Short summary
Data-driven emulation of melt ponds on Arctic sea ice
Simon Driscoll, Alberto Carrassi, Julien Brajard, Laurent Bertino, Einar Ólason, Marc Bocquet, and Amos Lawless
EGUsphere, https://doi.org/10.5194/egusphere-2024-2476,https://doi.org/10.5194/egusphere-2024-2476, 2024
Short summary
Representation learning with unconditional denoising diffusion models for dynamical systems
Tobias Sebastian Finn, Lucas Disson, Alban Farchi, Marc Bocquet, and Charlotte Durand
Nonlin. Processes Geophys., 31, 409–431, https://doi.org/10.5194/npg-31-409-2024,https://doi.org/10.5194/npg-31-409-2024, 2024
Short summary

Related subject area

Subject: Predictability, probabilistic forecasts, data assimilation, inverse problems | Topic: Climate, atmosphere, ocean, hydrology, cryosphere, biosphere
Explaining the high skill of reservoir computing methods in El Niño prediction
Francesco Guardamagna, Claudia Wieners, and Henk A. Dijkstra
Nonlin. Processes Geophys., 32, 201–224, https://doi.org/10.5194/npg-32-201-2025,https://doi.org/10.5194/npg-32-201-2025, 2025
Short summary
Multilevel Monte Carlo methods for ensemble variational data assimilation
Mayeul Destouches, Paul Mycek, Selime Gürol, Anthony T. Weaver, Serge Gratton, and Ehouarn Simon
Nonlin. Processes Geophys., 32, 167–187, https://doi.org/10.5194/npg-32-167-2025,https://doi.org/10.5194/npg-32-167-2025, 2025
Short summary
Dynamic–statistic combined ensemble prediction and impact factors of China's summer precipitation
Xiaojuan Wang, Zihan Yang, Shuai Li, Qingquan Li, and Guolin Feng
Nonlin. Processes Geophys., 32, 117–130, https://doi.org/10.5194/npg-32-117-2025,https://doi.org/10.5194/npg-32-117-2025, 2025
Short summary
Bridging Data Assimilation and Control: Ensemble Model Predictive Control for High-Dimensional Nonlinear Systems
Kenta Kurosawa, Atsushi Okazaki, Fumitoshi Kawasaki, and Shunji Kotsuki
EGUsphere, https://doi.org/10.5194/egusphere-2025-595,https://doi.org/10.5194/egusphere-2025-595, 2025
Short summary
Evaluation of Effectiveness of Intervention Strategy in Control Simulation Experiment through Comparison with Model Predictive Control
Rikuto Nagai, Yang Bai, Masaki Ogura, Shunji Kotsuki, and Naoki Wakamiya
Nonlin. Processes Geophys. Discuss., https://doi.org/10.5194/npg-2024-26,https://doi.org/10.5194/npg-2024-26, 2024
Revised manuscript accepted for NPG
Short summary

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., https://doi.org/10.1137/1.9781611971484, 1996.
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.
Bocquet, M.: Localization and the iterative ensemble Kalman smoother, Q. J. Roy. Meteor. Soc., 142, 1075–1089, https://doi.org/10.1002/qj.2711, 2016.
Download
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.
Share