Articles | Volume 23, issue 2
https://doi.org/10.5194/npg-23-59-2016
© Author(s) 2016. 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-23-59-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
11 Mar 2016
Research article |
| 11 Mar 2016
Hybrid Levenberg–Marquardt and weak-constraint ensemble Kalman smoother method
University of Colorado Denver, Denver, CO 80217-3364, USA
Institute of Computer Science, The Czech Academy of Sciences, 182 07 Prague, Czech Republic
E. Bergou
INRA, MaIAGE, Université Paris-Saclay, 78350 Jouy-en-Josas, France
S. Gürol
CERFACS, 31100 Toulouse, France
S. Gratton
CERFACS, 31100 Toulouse, France
INP-ENSEEIHT, 31071 Toulouse, France
I. Kasanický
Institute of Computer Science, The Czech Academy of Sciences, 182 07 Prague, Czech Republic
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- An iterative ensemble Kalman filter in the presence of additive model error P. Sakov et al. 10.1002/qj.3213
- A robust adaptive iterative ensemble smoother scheme for practical history matching applications X. Ma & L. Bi 10.1007/s10596-018-9786-9
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- Accounting for model error in air quality forecasts: an application of 4DEnVar to the assimilation of atmospheric composition using QG-Chem 1.0 E. Emili et al. 10.5194/gmd-9-3933-2016
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- Revising the stochastic iterative ensemble smoother P. Raanes et al. 10.5194/npg-26-325-2019
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13 citations as recorded by crossref.
- Latent space data assimilation by using deep learning M. Peyron et al. 10.1002/qj.4153
- Review article: Towards improved drought prediction in the Mediterranean region – modeling approaches and future directions B. Zellou et al. 10.5194/nhess-23-3543-2023
- An iterative ensemble Kalman filter in the presence of additive model error P. Sakov et al. 10.1002/qj.3213
- A robust adaptive iterative ensemble smoother scheme for practical history matching applications X. Ma & L. Bi 10.1007/s10596-018-9786-9
- An Iterative Ensemble Kalman Smoother in Presence of Additive Model Error A. Fillion et al. 10.1137/19M1244147
- Accounting for model error in air quality forecasts: an application of 4DEnVar to the assimilation of atmospheric composition using QG-Chem 1.0 E. Emili et al. 10.5194/gmd-9-3933-2016
- Four-dimensional variational inversion of black carbon emissions during ARCTAS-CARB with WRFDA-Chem J. Guerrette & D. Henze 10.5194/acp-17-7605-2017
- Data assimilation in the geosciences: An overview of methods, issues, and perspectives A. Carrassi et al. 10.1002/wcc.535
- Numerical linear algebra in data assimilation M. Freitag 10.1002/gamm.202000014
- Shadowing-Based Data Assimilation Method for Partially Observed Models B. de Leeuw & S. Dubinkina 10.1137/18M1223897
- Local Convergence Analysis of the Levenberg–Marquardt Framework for Nonzero-Residue Nonlinear Least-Squares Problems Under an Error Bound Condition R. Behling et al. 10.1007/s10957-019-01586-9
- Ensemble of 4DVARs (En4DVar) data assimilation in a coastal ocean circulation model, Part I: Methodology and ensemble statistics I. Pasmans & A. Kurapov 10.1016/j.ocemod.2019.101493
- Revising the stochastic iterative ensemble smoother P. Raanes et al. 10.5194/npg-26-325-2019
Saved (final revised paper)
Latest update: 29 Jun 2024
Short summary
A stochastic method, the ensemble Kalman smoother (EnKS), is proposed as a linear solver in four-dimensional variational data assimilation (4DVAR). The method approaches 4DVAR for large ensembles. Regularization provides global convergence, and it is implemented as an additional artificial observation. Since the EnKS is uncoupled from the insides of the 4DVAR, any version of EnKS can be used.
A stochastic method, the ensemble Kalman smoother (EnKS), is proposed as a linear solver in...
