46 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. 10.5194/npg-25-315-2018
2. Levenberg–Marquardt forms of the iterative ensemble smoother for efficient history matching and uncertainty quantification Y. Chen & D. Oliver 10.1007/s10596-013-9351-5
3. Expanding the validity of the ensemble Kalman filter without the intrinsic need for inflation M. Bocquet et al. 10.5194/npg-22-645-2015
4. A modified iterative ensemble Kalman filter data assimilation method B. Xu et al. 10.1088/1755-1315/81/1/012197
5. Jointly updating the mean size and spatial distribution of facies in reservoir history matching H. Chang & D. Zhang 10.1007/s10596-015-9478-7
6. Levenberg--Marquardt Methods Based on Probabilistic Gradient Models and Inexact Subproblem Solution, with Application to Data Assimilation E. Bergou et al. 10.1137/140974687
7. Online learning of both state and dynamics using ensemble Kalman filters M. Bocquet et al. 10.3934/fods.2020015
8. Accuracy of Some Approximate Gaussian Filters for the Navier--Stokes Equation in the Presence of Model Error M. Branicki et al. 10.1137/17M1146865
9. 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
10. On the convergence of a non-linear ensemble Kalman smoother E. Bergou et al. 10.1016/j.apnum.2018.11.008
11. Covariance Matrix Estimation for Ensemble-Based Kalman Filters with Multiple Ensembles S. Gratton et al. 10.1007/s11004-023-10063-z
12. Adaptive covariance inflation in the ensemble Kalman filter by Gaussian scale mixtures P. Raanes et al. 10.1002/qj.3386
13. Four-dimensional ensemble variational data assimilation and the unstable subspace M. Bocquet & A. Carrassi 10.1080/16000870.2017.1304504
14. Improving CFD atmospheric simulations at local scale for wind resource assessment using the iterative ensemble Kalman smoother C. Defforge et al. 10.1016/j.jweia.2019.03.030
15. Ensemble-based seismic inversion for a stratified medium M. Gineste et al. 10.1190/geo2019-0017.1
16. Derivative‐free optimization for large‐scale nonlinear data assimilation problems S. Gratton et al. 10.1002/qj.2169
17. Analog ensemble data assimilation and a method for constructing analogs with variational autoencoders I. Grooms 10.1002/qj.3910
18. Comparison of five different ways to assimilate data for a simplistic weakly nonlinear parameter estimation problem T. Mannseth 10.1007/s10596-015-9490-y
19. Comparison of simulation-based algorithms for parameter estimation and state reconstruction in nonlinear state-space models T. Chau et al. 10.3934/dcdss.2022054
20. An iterative ensemble Kalman filter in the presence of additive model error P. Sakov et al. 10.1002/qj.3213
21. Ensemble based regional ocean data assimilation system for the Indian Ocean: Implementation and evaluation B. Baduru et al. 10.1016/j.ocemod.2019.101470
22. Joint state and parameter estimation with an iterative ensemble Kalman smoother M. Bocquet & P. Sakov 10.5194/npg-20-803-2013
23. 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. 10.1364/OE.22.020894
24. Ensemble Transform Kalman Incremental Smoother and Its Application to Data Assimilation and Prediction Z. Lin et al. 10.3389/fams.2021.687743
25. An adaptive covariance relaxation method for ensemble data assimilation Y. Ying & F. Zhang 10.1002/qj.2576
26. An Iterative Ensemble Kalman Smoother in Presence of Additive Model Error A. Fillion et al. 10.1137/19M1244147
27. A Hierarchical Bayes Ensemble Kalman Filter M. Tsyrulnikov & A. Rakitko 10.1016/j.physd.2016.07.009
28. Ensemble Kalman Filtering with Residual Nudging: An Extension to State Estimation Problems with Nonlinear Observation Operators X. Luo & I. Hoteit 10.1175/MWR-D-13-00328.1
29. Learning stochastic closures using ensemble Kalman inversion T. Schneider et al. 10.1093/imatrm/tnab003
30. Tests of different flavours of EnKF on a simple model N. Bowler et al. 10.1002/qj.2055
31. An ETKF approach for initial state and parameter estimation in ice sheet modelling B. Bonan et al. 10.5194/npg-21-569-2014
32. Covariance Inflation in the Ensemble Kalman Filter: A Residual Nudging Perspective and Some Implications X. Luo & I. Hoteit 10.1175/MWR-D-13-00067.1
33. Revising the stochastic iterative ensemble smoother P. Raanes et al. 10.5194/npg-26-325-2019
34. Surrogate model based iterative ensemble smoother for subsurface flow data assimilation H. Chang et al. 10.1016/j.advwatres.2016.12.001
35. Multivariate state and parameter estimation with data assimilation applied to sea-ice models using a Maxwell elasto-brittle rheology Y. Chen et al. 10.5194/tc-18-2381-2024
36. 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. 10.5194/hess-24-325-2020
37. A fast, single-iteration ensemble Kalman smoother for sequential data assimilation C. Grudzien & M. Bocquet 10.5194/gmd-15-7641-2022
38. Behavior of the iterative ensemble-based variational method in nonlinear problems S. Nakano 10.5194/npg-28-93-2021
39. Data Assimilation in Density‐Dependent Subsurface Flows via Localized Iterative Ensemble Kalman Filter C. Xia et al. 10.1029/2017WR022369
40. An iterative ensemble Kalman smoother M. Bocquet & P. Sakov 10.1002/qj.2236
41. A Comparison of Ensemble Kalman Filters for Storm Surge Assimilation M. Altaf et al. 10.1175/MWR-D-13-00266.1
42. Hybrid Levenberg–Marquardt and weak-constraint ensemble Kalman smoother method J. Mandel et al. 10.5194/npg-23-59-2016
43. 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
44. Data assimilation in the geosciences: An overview of methods, issues, and perspectives A. Carrassi et al. 10.1002/wcc.535
45. A potential implicit particle method for high-dimensional systems B. Weir et al. 10.5194/npg-20-1047-2013
46. Calibration and Uncertainty Quantification of Convective Parameters in an Idealized GCM O. Dunbar et al. 10.1029/2020MS002454