62 citations as recorded by crossref.

1. Ensemble variational assimilation as a probabilistic estimator – Part 2: The fully non-linear case M. Jardak & O. Talagrand https://doi.org/10.5194/npg-25-589-2018
2. An international initiative of predicting the SARS-CoV-2 pandemic using ensemble data assimilation G. Evensen et al. https://doi.org/10.3934/fods.2021001
3. A fast, single-iteration ensemble Kalman smoother for sequential data assimilation C. Grudzien & M. Bocquet https://doi.org/10.5194/gmd-15-7641-2022
4. 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
5. Improving soil moisture prediction of a high-resolution land surface model by parameterising pedotransfer functions through assimilation of SMAP satellite data E. Pinnington et al. https://doi.org/10.5194/hess-25-1617-2021
6. Behavior of the iterative ensemble-based variational method in nonlinear problems S. Nakano https://doi.org/10.5194/npg-28-93-2021
7. 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
8. Data assimilation in the geosciences: An overview of methods, issues, and perspectives A. Carrassi et al. https://doi.org/10.1002/wcc.535
9. Accurate deep learning-based filtering for chaotic dynamics by identifying instabilities without an ensemble M. Bocquet et al. https://doi.org/10.1063/5.0230837
10. Adaptive covariance inflation in the ensemble Kalman filter by Gaussian scale mixtures P. Raanes et al. https://doi.org/10.1002/qj.3386
11. A candidate secular variation model for IGRF-13 based on MHD dynamo simulation and 4DEnVar data assimilation T. Minami et al. https://doi.org/10.1186/s40623-020-01253-8
12. Accounting for model errors in iterative ensemble smoothers G. Evensen https://doi.org/10.1007/s10596-019-9819-z
13. Limits of constant-parameter constitutive models for hydrogels under inertial cavitation T. Chu et al. https://doi.org/10.1039/D6SM00547K
14. A Gaussian Mixture Model Smoother for Continuous Nonlinear Stochastic Dynamical Systems: Applications T. Lolla & P. Lermusiaux https://doi.org/10.1175/MWR-D-16-0065.1
15. Gaussian approximations in filters and smoothers for data assimilation M. Morzfeld & D. Hodyss https://doi.org/10.1080/16000870.2019.1600344
16. 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
17. Data assimilation in atmospheric chemistry models: current status and future prospects for coupled chemistry meteorology models M. Bocquet et al. https://doi.org/10.5194/acp-15-5325-2015
18. The assessment of potential observability for joint chemical states and emissions in atmospheric modelings X. Wu et al. https://doi.org/10.1007/s00477-021-02113-x
19. 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
20. 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
21. Online learning of both state and dynamics using ensemble Kalman filters M. Bocquet et al. https://doi.org/10.3934/fods.2020015
22. Particle-Assisted Laser-Induced Inertial Cavitation for High Strain-Rate Soft Material Characterization S. Buyukozturk et al. https://doi.org/10.1007/s11340-022-00861-7
23. Joint inference of groundwater–recharge and hydraulic–conductivity fields from head data using the ensemble Kalman filter D. Erdal & O. Cirpka https://doi.org/10.5194/hess-20-555-2016
24. Evaluation of iterative Kalman smoother schemes for multi-decadal past climate analysis with comprehensive Earth system models J. García-Pintado & A. Paul https://doi.org/10.5194/gmd-11-5051-2018
25. Calibration and uncertainty analysis of integrated SWAT-MODFLOW model based on iterative ensemble smoother method for watershed scale river-aquifer interactions assessment B. Yifru et al. https://doi.org/10.1007/s12145-023-01071-y
26. TorchDA: A Python package for performing data assimilation with deep learning forward and transformation functions S. Cheng et al. https://doi.org/10.1016/j.cpc.2024.109359
27. The Land Variational Ensemble Data Assimilation Framework: LAVENDAR v1.0.0 E. Pinnington et al. https://doi.org/10.5194/gmd-13-55-2020
28. High-resolution data assimilation through stochastic subgrid tensor and parameter estimation from 4DEnVar Y. Yang & E. Memin https://doi.org/10.1080/16000870.2017.1308772
29. Ensemble Kalman Methods for High-Dimensional Hierarchical Dynamic Space-Time Models M. Katzfuss et al. https://doi.org/10.1080/01621459.2019.1592753
30. Competitive renaissance through digital transformation S. Denicolai et al. https://doi.org/10.1016/j.emj.2022.08.006
31. Estimating model evidence using data assimilation A. Carrassi et al. https://doi.org/10.1002/qj.2972
32. Elucidating the performance of data assimilation neural networks for chaotic dynamics M. Bocquet et al. https://doi.org/10.5194/npg-33-401-2026
33. High-resolution inversion of methane emissions over Europe using the Community Inversion Framework and FLEXPART A. Mengistu et al. https://doi.org/10.5194/acp-26-12295-2026
34. Using global Bayesian optimization in ensemble data assimilation: parameter estimation, tuning localization and inflation, or all of the above S. Lunderman et al. https://doi.org/10.1080/16000870.2021.1924952
35. Assimilation of lidar signals: application to aerosol forecasting in the western Mediterranean basin Y. Wang et al. https://doi.org/10.5194/acp-14-12031-2014
36. Variational particle smoothers and their localization M. Morzfeld et al. https://doi.org/10.1002/qj.3256
37. 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
38. Nonlinear Data Assimilation by Deep Learning Embedded in an Ensemble Kalman Filter T. TSUYUKI & R. TAMURA https://doi.org/10.2151/jmsj.2022-027
39. Comparison of ensemble assimilation methods in a hydrological model dedicated to agricultural best management practices E. Rouzies et al. https://doi.org/10.5194/hess-30-1-2026
40. Earth System Modeling 2.0: A Blueprint for Models That Learn From Observations and Targeted High‐Resolution Simulations T. Schneider et al. https://doi.org/10.1002/2017GL076101
41. Parameter and state estimation with ensemble Kalman filter based algorithms for convective‐scale applications Y. Ruckstuhl & T. Janjić https://doi.org/10.1002/qj.3257
42. Efficient derivative-free Bayesian inference for large-scale inverse problems D. Huang et al. https://doi.org/10.1088/1361-6420/ac99fa
43. Revising the stochastic iterative ensemble smoother P. Raanes et al. https://doi.org/10.5194/npg-26-325-2019
44. Hybrid Levenberg–Marquardt and weak-constraint ensemble Kalman smoother method J. Mandel et al. https://doi.org/10.5194/npg-23-59-2016
45. Optimal and scalable methods to approximate the solutions of large‐scale Bayesian problems: theory and application to atmospheric inversion and data assimilation N. Bousserez & D. Henze https://doi.org/10.1002/qj.3209
46. Ensemble-based seismic inversion for a stratified medium M. Gineste et al. https://doi.org/10.1190/geo2019-0017.1
47. A Regional multi-Air Pollutant Assimilation System (RAPAS v1.0) for emission estimates: system development and application S. Feng et al. https://doi.org/10.5194/gmd-16-5949-2023
48. A model-independent iterative ensemble smoother for efficient history-matching and uncertainty quantification in very high dimensions J. White https://doi.org/10.1016/j.envsoft.2018.06.009
49. Coupled Stratospheric Chemistry–Meteorology Data Assimilation. Part II: Weak and Strong Coupling R. Ménard et al. https://doi.org/10.3390/atmos10120798
50. A New Approach to Solving the Problem of Atmospheric Air Pollution in the Industrial City Z. Oralbekova et al. https://doi.org/10.1155/2021/8970949
51. Φ-DVAE: Physics-informed dynamical variational autoencoders for unstructured data assimilation A. Glyn-Davies et al. https://doi.org/10.1016/j.jcp.2024.113293
52. Training Physics‐Based Machine‐Learning Parameterizations With Gradient‐Free Ensemble Kalman Methods I. Lopez‐Gomez et al. https://doi.org/10.1029/2022MS003105
53. Data assimilation for Bayesian updating of predicted embankment response using monitoring data A. Amavasai et al. https://doi.org/10.1016/j.compgeo.2023.105936
54. 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
55. Accounting for model error in air quality forecasts: an application of 4DEnVar to the assimilation of atmospheric composition using QG-Chem 1.0 E. Emili et al. https://doi.org/10.5194/gmd-9-3933-2016
56. Four-dimensional ensemble variational data assimilation and the unstable subspace M. Bocquet & A. Carrassi https://doi.org/10.1080/16000870.2017.1304504
57. Surrogate modeling for the climate sciences dynamics with machine learning and data assimilation M. Bocquet https://doi.org/10.3389/fams.2023.1133226
58. From points to waves: Fast ocean wave spatial–temporal fields estimation using ensemble transform Kalman filter with optical measurement F. Wang et al. https://doi.org/10.1016/j.coastaleng.2024.104690
59. Combining ensemble Kalman filter and reservoir computing to predict spatiotemporal chaotic systems from imperfect observations and models F. Tomizawa & Y. Sawada https://doi.org/10.5194/gmd-14-5623-2021
60. 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
61. Hypothesis-driven modeling of the human lung–ventilator system: A characterization tool for Acute Respiratory Distress Syndrome research J. Stroh et al. https://doi.org/10.1016/j.jbi.2022.104275
62. An iterative ensemble Kalman filter in the presence of additive model error P. Sakov et al. https://doi.org/10.1002/qj.3213