47 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 Methods Based on Probabilistic Gradient Models and Inexact Subproblem Solution, with Application to Data Assimilation E. Bergou et al. 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. 10.1137/17M1146865
4. Covariance Matrix Estimation for Ensemble-Based Kalman Filters with Multiple Ensembles S. Gratton et al. 10.1007/s11004-023-10063-z
5. Ensemble-based seismic inversion for a stratified medium M. Gineste et al. 10.1190/geo2019-0017.1
6. Derivative‐free optimization for large‐scale nonlinear data assimilation problems S. Gratton et al. 10.1002/qj.2169
7. Comparison of five different ways to assimilate data for a simplistic weakly nonlinear parameter estimation problem T. Mannseth 10.1007/s10596-015-9490-y
8. Comparison of simulation-based algorithms for parameter estimation and state reconstruction in nonlinear state-space models T. Chau et al. 10.3934/dcdss.2022054
9. An iterative ensemble Kalman filter in the presence of additive model error P. Sakov et al. 10.1002/qj.3213
10. Joint state and parameter estimation with an iterative ensemble Kalman smoother M. Bocquet & P. Sakov 10.5194/npg-20-803-2013
11. An adaptive covariance relaxation method for ensemble data assimilation Y. Ying & F. Zhang 10.1002/qj.2576
12. An Iterative Ensemble Kalman Smoother in Presence of Additive Model Error A. Fillion et al. 10.1137/19M1244147
13. A Review of Innovation-Based Methods to Jointly Estimate Model and Observation Error Covariance Matrices in Ensemble Data Assimilation P. Tandeo et al. 10.1175/MWR-D-19-0240.1
14. Learning stochastic closures using ensemble Kalman inversion T. Schneider et al. 10.1093/imatrm/tnab003
15. Tests of different flavours of EnKF on a simple model N. Bowler et al. 10.1002/qj.2055
16. Surrogate model based iterative ensemble smoother for subsurface flow data assimilation H. Chang et al. 10.1016/j.advwatres.2016.12.001
17. A fast, single-iteration ensemble Kalman smoother for sequential data assimilation C. Grudzien & M. Bocquet 10.5194/gmd-15-7641-2022
18. Behavior of the iterative ensemble-based variational method in nonlinear problems S. Nakano 10.5194/npg-28-93-2021
19. Data Assimilation in Density‐Dependent Subsurface Flows via Localized Iterative Ensemble Kalman Filter C. Xia et al. 10.1029/2017WR022369
20. An iterative ensemble Kalman smoother M. Bocquet & P. Sakov 10.1002/qj.2236
21. 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
22. 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
23. Expanding the validity of the ensemble Kalman filter without the intrinsic need for inflation M. Bocquet et al. 10.5194/npg-22-645-2015
24. A modified iterative ensemble Kalman filter data assimilation method B. Xu et al. 10.1088/1755-1315/81/1/012197
25. Jointly updating the mean size and spatial distribution of facies in reservoir history matching H. Chang & D. Zhang 10.1007/s10596-015-9478-7
26. Online learning of both state and dynamics using ensemble Kalman filters M. Bocquet et al. 10.3934/fods.2020015
27. 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
28. On the convergence of a non-linear ensemble Kalman smoother E. Bergou et al. 10.1016/j.apnum.2018.11.008
29. Adaptive covariance inflation in the ensemble Kalman filter by Gaussian scale mixtures P. Raanes et al. 10.1002/qj.3386
30. Four-dimensional ensemble variational data assimilation and the unstable subspace M. Bocquet & A. Carrassi 10.1080/16000870.2017.1304504
31. 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
32. Analog ensemble data assimilation and a method for constructing analogs with variational autoencoders I. Grooms 10.1002/qj.3910
33. Ensemble based regional ocean data assimilation system for the Indian Ocean: Implementation and evaluation B. Baduru et al. 10.1016/j.ocemod.2019.101470
34. 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
35. Ensemble Transform Kalman Incremental Smoother and Its Application to Data Assimilation and Prediction Z. Lin et al. 10.3389/fams.2021.687743
36. A Hierarchical Bayes Ensemble Kalman Filter M. Tsyrulnikov & A. Rakitko 10.1016/j.physd.2016.07.009
37. 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
38. An ETKF approach for initial state and parameter estimation in ice sheet modelling B. Bonan et al. 10.5194/npg-21-569-2014
39. 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
40. Revising the stochastic iterative ensemble smoother P. Raanes et al. 10.5194/npg-26-325-2019
41. 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
42. 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
43. A Comparison of Ensemble Kalman Filters for Storm Surge Assimilation M. Altaf et al. 10.1175/MWR-D-13-00266.1
44. Hybrid Levenberg–Marquardt and weak-constraint ensemble Kalman smoother method J. Mandel et al. 10.5194/npg-23-59-2016
45. Data assimilation in the geosciences: An overview of methods, issues, and perspectives A. Carrassi et al. 10.1002/wcc.535
46. A potential implicit particle method for high-dimensional systems B. Weir et al. 10.5194/npg-20-1047-2013
47. Calibration and Uncertainty Quantification of Convective Parameters in an Idealized GCM O. Dunbar et al. 10.1029/2020MS002454