40 citations as recorded by crossref.

1. Data assimilation in the geosciences: An overview of methods, issues, and perspectives A. Carrassi et al. 10.1002/wcc.535
2. Revising the stochastic iterative ensemble smoother P. Raanes et al. 10.5194/npg-26-325-2019
3. 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
4. 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
5. A fast, single-iteration ensemble Kalman smoother for sequential data assimilation C. Grudzien & M. Bocquet 10.5194/gmd-15-7641-2022
6. Time‐correlated model error in the (ensemble) Kalman smoother J. Amezcua & P. van Leeuwen 10.1002/qj.3378
7. On Temporal Scale Separation in Coupled Data Assimilation with the Ensemble Kalman Filter M. Tondeur et al. 10.1007/s10955-020-02525-z
8. A variational Bayesian approach for ensemble filtering of stochastically parametrized systems B. Ait‐El‐Fquih et al. 10.1002/qj.4481
9. Efficient Implementation of an Iterative Ensemble Smoother for Data Assimilation and Reservoir History Matching G. Evensen et al. 10.3389/fams.2019.00047
10. Four-dimensional ensemble variational data assimilation and the unstable subspace M. Bocquet & A. Carrassi 10.1080/16000870.2017.1304504
11. On the consistency of the local ensemble square root Kalman filter perturbation update M. Bocquet & A. Farchi 10.1080/16000870.2019.1613142
12. 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
13. Geophysical flows under location uncertainty, Part II Quasi-geostrophy and efficient ensemble spreading V. Resseguier et al. 10.1080/03091929.2017.1312101
14. 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
15. Asymptotic Forecast Uncertainty and the Unstable Subspace in the Presence of Additive Model Error C. Grudzien et al. 10.1137/17M114073X
16. Review article: Comparison of local particle filters and new implementations A. Farchi & M. Bocquet 10.5194/npg-25-765-2018
17. Inferring the instability of a dynamical system from the skill of data assimilation exercises Y. Chen et al. 10.5194/npg-28-633-2021
18. Dynamical effects of inflation in ensemble‐based data assimilation under the presence of model error G. Scheffler et al. 10.1002/qj.4307
19. 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
20. A sparse matrix formulation of model-based ensemble Kalman filter H. Gryvill & H. Tjelmeland 10.1007/s11222-023-10228-0
21. Adaptive covariance inflation in the ensemble Kalman filter by Gaussian scale mixtures P. Raanes et al. 10.1002/qj.3386
22. Fast and Accurate Estimation of Evapotranspiration for Smart Agriculture W. Li & D. Tartakovsky 10.1029/2023WR034535
23. Relevance of conservative numerical schemes for an Ensemble Kalman Filter S. Dubinkina 10.1002/qj.3219
24. Ensemble-based seismic inversion for a stratified medium M. Gineste et al. 10.1190/geo2019-0017.1
25. A particle‐filter based adaptive inflation scheme for the ensemble Kalman filter B. Ait‐El‐Fquih & I. Hoteit 10.1002/qj.3716
26. Chaotic System Prediction Using Data Assimilation and Machine Learning G. Yanan et al. 10.1051/e3sconf/202018502025
27. 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
28. 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
29. An anisotropic formulation of the parametric Kalman filter assimilation O. Pannekoucke 10.1080/16000870.2021.1926660
30. Ensemble-based statistical interpolation with Gaussian anamorphosis for the spatial analysis of precipitation C. Lussana et al. 10.5194/npg-28-61-2021
31. Enhanced Adaptive Inflation Algorithm for Ensemble Filters M. El Gharamti 10.1175/MWR-D-17-0187.1
32. A Hierarchical Bayes Ensemble Kalman Filter M. Tsyrulnikov & A. Rakitko 10.1016/j.physd.2016.07.009
33. Estimation of Evapotranspiration Rates and Root Water Uptake Profiles From Soil Moisture Sensor Array Data W. Li et al. 10.1029/2021WR030747
34. Degenerate Kalman Filter Error Covariances and Their Convergence onto the Unstable Subspace M. Bocquet et al. 10.1137/16M1068712
35. Accurate deep learning-based filtering for chaotic dynamics by identifying instabilities without an ensemble M. Bocquet et al. 10.1063/5.0230837
36. Combining data assimilation and machine learning to infer unresolved scale parametrization J. Brajard et al. 10.1098/rsta.2020.0086
37. A shadowing-based inflation scheme for ensemble data assimilation T. Bellsky & L. Mitchell 10.1016/j.physd.2018.05.002
38. A Four‐Dimensional Variational Constrained Neural Network‐Based Data Assimilation Method W. Wang et al. 10.1029/2023MS003687
39. Covariance Matrix Estimation for Ensemble-Based Kalman Filters with Multiple Ensembles S. Gratton et al. 10.1007/s11004-023-10063-z
40. Flow-dependent assimilation of sea surface temperature in isopycnal coordinates with the Norwegian Climate Prediction Model F. Counillon et al. 10.3402/tellusa.v68.32437