52 citations as recorded by crossref.

1. Quasi-static ensemble variational data assimilation: a theoretical and numerical study with the iterative ensemble Kalman smoother A. Fillion et al. https://doi.org/10.5194/npg-25-315-2018
2. Levenberg--Marquardt Methods Based on Probabilistic Gradient Models and Inexact Subproblem Solution, with Application to Data Assimilation E. Bergou et al. https://doi.org/10.1137/140974687
3. Accuracy of Some Approximate Gaussian Filters for the Navier--Stokes Equation in the Presence of Model Error M. Branicki et al. https://doi.org/10.1137/17M1146865
4. Covariance Matrix Estimation for Ensemble-Based Kalman Filters with Multiple Ensembles S. Gratton et al. https://doi.org/10.1007/s11004-023-10063-z
5. Elucidating the performance of data assimilation neural networks for chaotic dynamics M. Bocquet et al. https://doi.org/10.5194/npg-33-401-2026
6. Ensemble-based seismic inversion for a stratified medium M. Gineste et al. https://doi.org/10.1190/geo2019-0017.1
7. Derivative‐free optimization for large‐scale nonlinear data assimilation problems S. Gratton et al. https://doi.org/10.1002/qj.2169
8. Ensemble Kalman Filter Meets Model Predictive Control in Chaotic Systems Y. Sawada https://doi.org/10.2151/sola.2024-053
9. Comparison of five different ways to assimilate data for a simplistic weakly nonlinear parameter estimation problem T. Mannseth https://doi.org/10.1007/s10596-015-9490-y
10. Comparison of simulation-based algorithms for parameter estimation and state reconstruction in nonlinear state-space models T. Chau et al. https://doi.org/10.3934/dcdss.2022054
11. An iterative ensemble Kalman filter in the presence of additive model error P. Sakov et al. https://doi.org/10.1002/qj.3213
12. Joint state and parameter estimation with an iterative ensemble Kalman smoother M. Bocquet & P. Sakov https://doi.org/10.5194/npg-20-803-2013
13. An adaptive covariance relaxation method for ensemble data assimilation Y. Ying & F. Zhang https://doi.org/10.1002/qj.2576
14. An Iterative Ensemble Kalman Smoother in Presence of Additive Model Error A. Fillion et al. https://doi.org/10.1137/19M1244147
15. Learning stochastic closures using ensemble Kalman inversion T. Schneider et al. https://doi.org/10.1093/imatrm/tnab003
16. Tests of different flavours of EnKF on a simple model N. Bowler et al. https://doi.org/10.1002/qj.2055
17. Surrogate model based iterative ensemble smoother for subsurface flow data assimilation H. Chang et al. https://doi.org/10.1016/j.advwatres.2016.12.001
18. A fast, single-iteration ensemble Kalman smoother for sequential data assimilation C. Grudzien & M. Bocquet https://doi.org/10.5194/gmd-15-7641-2022
19. Behavior of the iterative ensemble-based variational method in nonlinear problems S. Nakano https://doi.org/10.5194/npg-28-93-2021
20. Data Assimilation in Density‐Dependent Subsurface Flows via Localized Iterative Ensemble Kalman Filter C. Xia et al. https://doi.org/10.1029/2017WR022369
21. An iterative ensemble Kalman smoother M. Bocquet & P. Sakov https://doi.org/10.1002/qj.2236
22. Chaotic dynamics and the role of covariance inflation for reduced rank Kalman filters with model error C. Grudzien et al. https://doi.org/10.5194/npg-25-633-2018
23. Exploring the influence of spatio-temporal scale differences in coupled data assimilation L. Garcia-Oliva et al. https://doi.org/10.5194/npg-32-439-2025
24. Levenberg–Marquardt forms of the iterative ensemble smoother for efficient history matching and uncertainty quantification Y. Chen & D. Oliver https://doi.org/10.1007/s10596-013-9351-5
25. Expanding the validity of the ensemble Kalman filter without the intrinsic need for inflation M. Bocquet et al. https://doi.org/10.5194/npg-22-645-2015
26. A modified iterative ensemble Kalman filter data assimilation method B. Xu et al. https://doi.org/10.1088/1755-1315/81/1/012197
27. Jointly updating the mean size and spatial distribution of facies in reservoir history matching H. Chang & D. Zhang https://doi.org/10.1007/s10596-015-9478-7
28. Online learning of both state and dynamics using ensemble Kalman filters M. Bocquet et al. https://doi.org/10.3934/fods.2020015
29. A low-order coupled chemistry meteorology model for testing online and offline data assimilation schemes: L95-GRS (v1.0) J. Haussaire & M. Bocquet https://doi.org/10.5194/gmd-9-393-2016
30. On the convergence of a non-linear ensemble Kalman smoother E. Bergou et al. https://doi.org/10.1016/j.apnum.2018.11.008
31. Adaptive covariance inflation in the ensemble Kalman filter by Gaussian scale mixtures P. Raanes et al. https://doi.org/10.1002/qj.3386
32. Four-dimensional ensemble variational data assimilation and the unstable subspace M. Bocquet & A. Carrassi https://doi.org/10.1080/16000870.2017.1304504
33. Improving CFD atmospheric simulations at local scale for wind resource assessment using the iterative ensemble Kalman smoother C. Defforge et al. https://doi.org/10.1016/j.jweia.2019.03.030
34. Analog ensemble data assimilation and a method for constructing analogs with variational autoencoders I. Grooms https://doi.org/10.1002/qj.3910
35. Ensemble based regional ocean data assimilation system for the Indian Ocean: Implementation and evaluation B. Baduru et al. https://doi.org/10.1016/j.ocemod.2019.101470
36. An integral approach using InSAR and data assimilation to disentangle and quantify multi-depth driven subsidence causes in the Ravenna coastland, Northern Italy M. Verberne et al. https://doi.org/10.1016/j.gete.2025.100710
37. Local ensemble transform Kalman filter, a fast non-stationary control law for adaptive optics on ELTs: theoretical aspects and first simulation results M. Gray et al. https://doi.org/10.1364/OE.22.020894
38. Ensemble Transform Kalman Incremental Smoother and Its Application to Data Assimilation and Prediction Z. Lin et al. https://doi.org/10.3389/fams.2021.687743
39. A Hierarchical Bayes Ensemble Kalman Filter M. Tsyrulnikov & A. Rakitko https://doi.org/10.1016/j.physd.2016.07.009
40. Ensemble Kalman Filtering with Residual Nudging: An Extension to State Estimation Problems with Nonlinear Observation Operators X. Luo & I. Hoteit https://doi.org/10.1175/MWR-D-13-00328.1
41. Data assimilation of multi-stage fractured horizontal well in unconventional oil and gas reservoir: Field case study H. Chang et al. https://doi.org/10.1016/j.geoen.2025.213659
42. An ETKF approach for initial state and parameter estimation in ice sheet modelling B. Bonan et al. https://doi.org/10.5194/npg-21-569-2014
43. Covariance Inflation in the Ensemble Kalman Filter: A Residual Nudging Perspective and Some Implications X. Luo & I. Hoteit https://doi.org/10.1175/MWR-D-13-00067.1
44. Revising the stochastic iterative ensemble smoother P. Raanes et al. https://doi.org/10.5194/npg-26-325-2019
45. Variational data-consistent assimilation R. Spence et al. https://doi.org/10.1016/j.cma.2026.118804
46. Multivariate state and parameter estimation with data assimilation applied to sea-ice models using a Maxwell elasto-brittle rheology Y. Chen et al. https://doi.org/10.5194/tc-18-2381-2024
47. An ensemble square root filter for the joint assimilation of surface soil moisture and leaf area index within the Land Data Assimilation System LDAS-Monde: application over the Euro-Mediterranean region B. Bonan et al. https://doi.org/10.5194/hess-24-325-2020
48. A Comparison of Ensemble Kalman Filters for Storm Surge Assimilation M. Altaf et al. https://doi.org/10.1175/MWR-D-13-00266.1
49. Hybrid Levenberg–Marquardt and weak-constraint ensemble Kalman smoother method J. Mandel et al. https://doi.org/10.5194/npg-23-59-2016
50. Data assimilation in the geosciences: An overview of methods, issues, and perspectives A. Carrassi et al. https://doi.org/10.1002/wcc.535
51. A potential implicit particle method for high-dimensional systems B. Weir et al. https://doi.org/10.5194/npg-20-1047-2013
52. Calibration and Uncertainty Quantification of Convective Parameters in an Idealized GCM O. Dunbar et al. https://doi.org/10.1029/2020MS002454