Articles | Volume 22, issue 6
https://doi.org/10.5194/npg-22-645-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/npg-22-645-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
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
CEREA, Joint laboratory École des Ponts ParisTech and EDF R&D, Université Paris-Est, Champs-sur-Marne, France
P. N. Raanes
Nansen Environmental and Remote Sensing Center, Bergen, Norway
Mathematical Institute, University of Oxford, Oxford, UK
A. Hannart
IFAECI, CNRS-CONICET-UBA, Buenos Aires, Argentina
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39 citations as recorded by crossref.
- Data assimilation in the geosciences: An overview of methods, issues, and perspectives A. Carrassi et al. 10.1002/wcc.535
- Revising the stochastic iterative ensemble smoother P. Raanes et al. 10.5194/npg-26-325-2019
- Combining a Fully Connected Neural Network With an Ensemble Kalman Filter to Emulate a Dynamic Model in Data Assimilation M. Fan et al. 10.1109/ACCESS.2021.3120482
- Combining data assimilation and machine learning to emulate a dynamical model from sparse and noisy observations: A case study with the Lorenz 96 model J. Brajard et al. 10.1016/j.jocs.2020.101171
- A fast, single-iteration ensemble Kalman smoother for sequential data assimilation C. Grudzien & M. Bocquet 10.5194/gmd-15-7641-2022
- Time‐correlated model error in the (ensemble) Kalman smoother J. Amezcua & P. van Leeuwen 10.1002/qj.3378
- On Temporal Scale Separation in Coupled Data Assimilation with the Ensemble Kalman Filter M. Tondeur et al. 10.1007/s10955-020-02525-z
- A variational Bayesian approach for ensemble filtering of stochastically parametrized systems B. Ait‐El‐Fquih et al. 10.1002/qj.4481
- Efficient Implementation of an Iterative Ensemble Smoother for Data Assimilation and Reservoir History Matching G. Evensen et al. 10.3389/fams.2019.00047
- Four-dimensional ensemble variational data assimilation and the unstable subspace M. Bocquet & A. Carrassi 10.1080/16000870.2017.1304504
- On the consistency of the local ensemble square root Kalman filter perturbation update M. Bocquet & A. Farchi 10.1080/16000870.2019.1613142
- Application of a local attractor dimension to reduced space strongly coupled data assimilation for chaotic multiscale systems C. Quinn et al. 10.5194/npg-27-51-2020
- Geophysical flows under location uncertainty, Part II Quasi-geostrophy and efficient ensemble spreading V. Resseguier et al. 10.1080/03091929.2017.1312101
- Chaotic dynamics and the role of covariance inflation for reduced rank Kalman filters with model error C. Grudzien et al. 10.5194/npg-25-633-2018
- Asymptotic Forecast Uncertainty and the Unstable Subspace in the Presence of Additive Model Error C. Grudzien et al. 10.1137/17M114073X
- Review article: Comparison of local particle filters and new implementations A. Farchi & M. Bocquet 10.5194/npg-25-765-2018
- Inferring the instability of a dynamical system from the skill of data assimilation exercises Y. Chen et al. 10.5194/npg-28-633-2021
- Dynamical effects of inflation in ensemble‐based data assimilation under the presence of model error G. Scheffler et al. 10.1002/qj.4307
- Impact of non‐stationarity on hybrid ensemble filters: A study with a doubly stochastic advection‐diffusion‐decay model M. Tsyrulnikov & A. Rakitko 10.1002/qj.3556
- A sparse matrix formulation of model-based ensemble Kalman filter H. Gryvill & H. Tjelmeland 10.1007/s11222-023-10228-0
- Adaptive covariance inflation in the ensemble Kalman filter by Gaussian scale mixtures P. Raanes et al. 10.1002/qj.3386
- Fast and Accurate Estimation of Evapotranspiration for Smart Agriculture W. Li & D. Tartakovsky 10.1029/2023WR034535
- Relevance of conservative numerical schemes for an Ensemble Kalman Filter S. Dubinkina 10.1002/qj.3219
- Ensemble-based seismic inversion for a stratified medium M. Gineste et al. 10.1190/geo2019-0017.1
- A particle‐filter based adaptive inflation scheme for the ensemble Kalman filter B. Ait‐El‐Fquih & I. Hoteit 10.1002/qj.3716
- Chaotic System Prediction Using Data Assimilation and Machine Learning G. Yanan et al. 10.1051/e3sconf/202018502025
- Quasi-static ensemble variational data assimilation: a theoretical and numerical study with the iterative ensemble Kalman smoother A. Fillion et al. 10.5194/npg-25-315-2018
- A low-order coupled chemistry meteorology model for testing online and offline data assimilation schemes: L95-GRS (v1.0) J. Haussaire & M. Bocquet 10.5194/gmd-9-393-2016
- An anisotropic formulation of the parametric Kalman filter assimilation O. Pannekoucke 10.1080/16000870.2021.1926660
- Ensemble-based statistical interpolation with Gaussian anamorphosis for the spatial analysis of precipitation C. Lussana et al. 10.5194/npg-28-61-2021
- Enhanced Adaptive Inflation Algorithm for Ensemble Filters M. El Gharamti 10.1175/MWR-D-17-0187.1
- A Hierarchical Bayes Ensemble Kalman Filter M. Tsyrulnikov & A. Rakitko 10.1016/j.physd.2016.07.009
- Estimation of Evapotranspiration Rates and Root Water Uptake Profiles From Soil Moisture Sensor Array Data W. Li et al. 10.1029/2021WR030747
- Degenerate Kalman Filter Error Covariances and Their Convergence onto the Unstable Subspace M. Bocquet et al. 10.1137/16M1068712
- Accurate deep learning-based filtering for chaotic dynamics by identifying instabilities without an ensemble M. Bocquet et al. 10.1063/5.0230837
- Combining data assimilation and machine learning to infer unresolved scale parametrization J. Brajard et al. 10.1098/rsta.2020.0086
- A shadowing-based inflation scheme for ensemble data assimilation T. Bellsky & L. Mitchell 10.1016/j.physd.2018.05.002
- A Four‐Dimensional Variational Constrained Neural Network‐Based Data Assimilation Method W. Wang et al. 10.1029/2023MS003687
- Covariance Matrix Estimation for Ensemble-Based Kalman Filters with Multiple Ensembles S. Gratton et al. 10.1007/s11004-023-10063-z
Saved (final revised paper)
Latest update: 13 Dec 2024
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.
The popular data assimilation technique known as the ensemble Kalman filter (EnKF) suffers from...
