65 citations as recorded by crossref.

1. 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
2. Representation learning with unconditional denoising diffusion models for dynamical systems T. Finn et al. https://doi.org/10.5194/npg-31-409-2024
3. Twin experiments with the equivalent weights particle filter and HadCM3 P. Browne & P. van Leeuwen https://doi.org/10.1002/qj.2621
4. Characterizing viscoelastic materials via ensemble-based data assimilation of bubble collapse observations J. Spratt et al. https://doi.org/10.1016/j.jmps.2021.104455
5. Combining a Fully Connected Neural Network With an Ensemble Kalman Filter to Emulate a Dynamic Model in Data Assimilation M. Fan et al. https://doi.org/10.1109/ACCESS.2021.3120482
6. A sparse matrix formulation of model-based ensemble Kalman filter H. Gryvill & H. Tjelmeland https://doi.org/10.1007/s11222-023-10228-0
7. A fast, single-iteration ensemble Kalman smoother for sequential data assimilation C. Grudzien & M. Bocquet https://doi.org/10.5194/gmd-15-7641-2022
8. 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
9. Deterministic ensemble Kalman filter based on two localization techniques for mitigating sampling errors with a quasi-geostrophic model M. Chang et al. https://doi.org/10.1007/s00703-024-01015-1
10. A non-Gaussian analysis scheme using rank histograms for ensemble data assimilation S. Metref et al. https://doi.org/10.5194/npg-21-869-2014
11. Adaptive covariance inflation in the ensemble Kalman filter by Gaussian scale mixtures P. Raanes et al. https://doi.org/10.1002/qj.3386
12. On the influence of model nonlinearity and localization on ensemble Kalman smoothing L. Nerger et al. https://doi.org/10.1002/qj.2293
13. Bayesian inference of chaotic dynamics by merging data assimilation, machine learning and expectation-maximization M. Bocquet et al. https://doi.org/10.3934/fods.2020004
14. The Community Inversion Framework v1.0: a unified system for atmospheric inversion studies A. Berchet et al. https://doi.org/10.5194/gmd-14-5331-2021
15. 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
16. Covariance Matrix Estimation for Ensemble-Based Kalman Filters with Multiple Ensembles S. Gratton et al. https://doi.org/10.1007/s11004-023-10063-z
17. An Adjoint-Based Adaptive Ensemble Kalman Filter H. Song et al. https://doi.org/10.1175/MWR-D-12-00244.1
18. Reinforcement Twinning: From digital twins to model-based reinforcement learning L. Schena et al. https://doi.org/10.1016/j.jocs.2024.102421
19. Chaotic System Prediction Using Data Assimilation and Machine Learning G. Yanan et al. https://doi.org/10.1051/e3sconf/202018502025
20. Four-dimensional ensemble variational data assimilation and the unstable subspace M. Bocquet & A. Carrassi https://doi.org/10.1080/16000870.2017.1304504
21. State-of-the-art stochastic data assimilation methods for high-dimensional non-Gaussian problems S. Vetra-Carvalho et al. https://doi.org/10.1080/16000870.2018.1445364
22. Ensemble-based seismic inversion for a stratified medium M. Gineste et al. https://doi.org/10.1190/geo2019-0017.1
23. An Iterative EnKF for Strongly Nonlinear Systems P. Sakov et al. https://doi.org/10.1175/MWR-D-11-00176.1
24. Impact of non‐stationarity on hybrid ensemble filters: A study with a doubly stochastic advection‐diffusion‐decay model M. Tsyrulnikov & A. Rakitko https://doi.org/10.1002/qj.3556
25. Data assimilation in the geosciences: An overview of methods, issues, and perspectives A. Carrassi et al. https://doi.org/10.1002/wcc.535
26. 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
27. Effect of model error representation in the Yellow and East China Sea modeling system based on the ensemble Kalman filter K. Kwon et al. https://doi.org/10.1007/s10236-015-0909-8
28. Spatially-aware diffusion models with cross-attention for global field reconstruction with sparse observations Y. Zhuang et al. https://doi.org/10.1016/j.cma.2024.117623
29. A potential implicit particle method for high-dimensional systems B. Weir et al. https://doi.org/10.5194/npg-20-1047-2013
30. Uncertainty quantification of pollutant source retrieval: comparison of Bayesian methods with application to the Chernobyl and Fukushima Daiichi accidental releases of radionuclides Y. Liu et al. https://doi.org/10.1002/qj.3138
31. Enhanced Adaptive Inflation Algorithm for Ensemble Filters M. El Gharamti https://doi.org/10.1175/MWR-D-17-0187.1
32. 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
33. Data assimilation for nonlinear systems with a hybrid nonlinear Kalman ensemble transform filter L. Nerger https://doi.org/10.1002/qj.4221
34. Ensemble variational method with adaptive covariance inflation for learning neural network-based turbulence models Q. Luo et al. https://doi.org/10.1063/5.0199175
35. Comparative evaluation of maximum likelihood ensemble filter and ensemble Kalman filter for real-time assimilation of streamflow data into operational hydrologic models A. Rafieeinasab et al. https://doi.org/10.1016/j.jhydrol.2014.06.052
36. An assessment of ocean climate reanalysis by the data assimilation system of KIOST from 1947 to 2012 Y. Kim et al. https://doi.org/10.1016/j.ocemod.2015.02.006
37. 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. https://doi.org/10.1016/j.jocs.2020.101171
38. Stochastic parametrization: An alternative to inflation in ensemble Kalman filters B. Dufée et al. https://doi.org/10.1002/qj.4247
39. Extending the Square Root Method to Account for Additive Forecast Noise in Ensemble Methods P. Raanes et al. https://doi.org/10.1175/MWR-D-14-00375.1
40. A hybrid particle-ensemble Kalman filter for problems with medium nonlinearity I. Grooms et al. https://doi.org/10.1371/journal.pone.0248266
41. On Temporal Scale Separation in Coupled Data Assimilation with the Ensemble Kalman Filter M. Tondeur et al. https://doi.org/10.1007/s10955-020-02525-z
42. Lyapunov vectors and assimilation in the unstable subspace: theory and applications L. Palatella et al. https://doi.org/10.1088/1751-8113/46/25/254020
43. Flow-dependent assimilation of sea surface temperature in isopycnal coordinates with the Norwegian Climate Prediction Model F. Counillon et al. https://doi.org/10.3402/tellusa.v68.32437
44. Combining inflation-free and iterative ensemble Kalman filters for strongly nonlinear systems M. Bocquet & P. Sakov https://doi.org/10.5194/npg-19-383-2012
45. A Hierarchical Bayes Ensemble Kalman Filter M. Tsyrulnikov & A. Rakitko https://doi.org/10.1016/j.physd.2016.07.009
46. An adaptive covariance relaxation method for ensemble data assimilation Y. Ying & F. Zhang https://doi.org/10.1002/qj.2576
47. Integration of microseismic monitoring data into coupled flow and geomechanical models with ensemble Kalman filter M. Tarrahi et al. https://doi.org/10.1002/2014WR016264
48. Comparing Adaptive Prior and Posterior Inflation for Ensemble Filters Using an Atmospheric General Circulation Model M. El Gharamti et al. https://doi.org/10.1175/MWR-D-18-0389.1
49. Objectified quantification of uncertainties in Bayesian atmospheric inversions A. Berchet et al. https://doi.org/10.5194/gmd-8-1525-2015
50. 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
51. Data Assimilation in Density‐Dependent Subsurface Flows via Localized Iterative Ensemble Kalman Filter C. Xia et al. https://doi.org/10.1029/2017WR022369
52. 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
53. An improved framework for the dynamic likelihood filtering approach to data assimilation D. Foster & J. Restrepo https://doi.org/10.1063/5.0083071
54. Observation-Informed Generalized Hybrid Error Covariance Models E. Satterfield et al. https://doi.org/10.1175/MWR-D-18-0016.1
55. Gaussian approximations in filters and smoothers for data assimilation M. Morzfeld & D. Hodyss https://doi.org/10.1080/16000870.2019.1600344
56. A flexible additive inflation scheme for treating model error in ensemble Kalman filters M. Sommer & T. Janjić https://doi.org/10.1002/qj.3254
57. Accounting for model error due to unresolved scales within ensemble Kalman filtering L. Mitchell & A. Carrassi https://doi.org/10.1002/qj.2451
58. 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
59. An iterative ensemble Kalman smoother M. Bocquet & P. Sakov https://doi.org/10.1002/qj.2236
60. 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
61. Online learning of both state and dynamics using ensemble Kalman filters M. Bocquet et al. https://doi.org/10.3934/fods.2020015
62. Data Assimilation Networks P. Boudier et al. https://doi.org/10.1029/2022MS003353
63. Boundary Conditions for the Parametric Kalman Filter Forecast M. Sabathier et al. https://doi.org/10.1029/2022MS003462
64. Interaction of Lyapunov vectors in the formulation of the nonlinear extension of the Kalman filter L. Palatella & A. Trevisan https://doi.org/10.1103/PhysRevE.91.042905
65. History matching using traditional and finite size ensemble Kalman filter H. Abdolhosseini & E. Khamehchi https://doi.org/10.1016/j.jngse.2015.10.041