42 citations as recorded by crossref.

1. Ensemble variational assimilation as a probabilistic estimator – Part 2: The fully non-linear case M. Jardak & O. Talagrand 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. 10.3934/fods.2021001
3. Using global Bayesian optimization in ensemble data assimilation: parameter estimation, tuning localization and inflation, or all of the above S. Lunderman et al. 10.1080/16000870.2021.1924952
4. Assimilation of lidar signals: application to aerosol forecasting in the western Mediterranean basin Y. Wang et al. 10.5194/acp-14-12031-2014
5. Variational particle smoothers and their localization M. Morzfeld et al. 10.1002/qj.3256
6. Localization and the iterative ensemble Kalman smoother M. Bocquet 10.1002/qj.2711
7. 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. 10.5194/hess-25-1617-2021
8. 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
9. Behavior of the iterative ensemble-based variational method in nonlinear problems S. Nakano 10.5194/npg-28-93-2021
10. Earth System Modeling 2.0: A Blueprint for Models That Learn From Observations and Targeted High‐Resolution Simulations T. Schneider et al. 10.1002/2017GL076101
11. Parameter and state estimation with ensemble Kalman filter based algorithms for convective‐scale applications Y. Ruckstuhl & T. Janjić 10.1002/qj.3257
12. Data assimilation in the geosciences: An overview of methods, issues, and perspectives A. Carrassi et al. 10.1002/wcc.535
13. Adaptive covariance inflation in the ensemble Kalman filter by Gaussian scale mixtures P. Raanes et al. 10.1002/qj.3386
14. Revising the stochastic iterative ensemble smoother P. Raanes et al. 10.5194/npg-26-325-2019
15. A candidate secular variation model for IGRF-13 based on MHD dynamo simulation and 4DEnVar data assimilation T. Minami et al. 10.1186/s40623-020-01253-8
16. Hybrid Levenberg–Marquardt and weak-constraint ensemble Kalman smoother method J. Mandel et al. 10.5194/npg-23-59-2016
17. Accounting for model errors in iterative ensemble smoothers G. Evensen 10.1007/s10596-019-9819-z
18. 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 10.1002/qj.3209
19. Ensemble-based seismic inversion for a stratified medium M. Gineste et al. 10.1190/geo2019-0017.1
20. A Gaussian Mixture Model Smoother for Continuous Nonlinear Stochastic Dynamical Systems: Applications T. Lolla & P. Lermusiaux 10.1175/MWR-D-16-0065.1
21. A model-independent iterative ensemble smoother for efficient history-matching and uncertainty quantification in very high dimensions J. White 10.1016/j.envsoft.2018.06.009
22. Gaussian approximations in filters and smoothers for data assimilation M. Morzfeld & D. Hodyss 10.1080/16000870.2019.1600344
23. 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
24. Data assimilation in atmospheric chemistry models: current status and future prospects for coupled chemistry meteorology models M. Bocquet et al. 10.5194/acp-15-5325-2015
25. The assessment of potential observability for joint chemical states and emissions in atmospheric modelings X. Wu et al. 10.1007/s00477-021-02113-x
26. Coupled Stratospheric Chemistry–Meteorology Data Assimilation. Part II: Weak and Strong Coupling R. Ménard et al. 10.3390/atmos10120798
27. A New Approach to Solving the Problem of Atmospheric Air Pollution in the Industrial City Z. Oralbekova et al. 10.1155/2021/8970949
28. 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
29. Expanding the validity of the ensemble Kalman filter without the intrinsic need for inflation M. Bocquet et al. 10.5194/npg-22-645-2015
30. Online learning of both state and dynamics using ensemble Kalman filters M. Bocquet et al. 10.3934/fods.2020015
31. Joint inference of groundwater–recharge and hydraulic–conductivity fields from head data using the ensemble Kalman filter D. Erdal & O. Cirpka 10.5194/hess-20-555-2016
32. Characterizing viscoelastic materials via ensemble-based data assimilation of bubble collapse observations J. Spratt et al. 10.1016/j.jmps.2021.104455
33. Evaluation of iterative Kalman smoother schemes for multi-decadal past climate analysis with comprehensive Earth system models J. García-Pintado & A. Paul 10.5194/gmd-11-5051-2018
34. The Land Variational Ensemble Data Assimilation Framework: LAVENDAR v1.0.0 E. Pinnington et al. 10.5194/gmd-13-55-2020
35. 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. 10.5194/gmd-9-3933-2016
36. High-resolution data assimilation through stochastic subgrid tensor and parameter estimation from 4DEnVar Y. Yang & E. Mémin 10.1080/16000870.2017.1308772
37. Four-dimensional ensemble variational data assimilation and the unstable subspace M. Bocquet & A. Carrassi 10.1080/16000870.2017.1304504
38. Ensemble Kalman Methods for High-Dimensional Hierarchical Dynamic Space-Time Models M. Katzfuss et al. 10.1080/01621459.2019.1592753
39. Estimating model evidence using data assimilation A. Carrassi et al. 10.1002/qj.2972
40. Combining ensemble Kalman filter and reservoir computing to predict spatiotemporal chaotic systems from imperfect observations and models F. Tomizawa & Y. Sawada 10.5194/gmd-14-5623-2021
41. Comparison of simulation-based algorithms for parameter estimation and state reconstruction in nonlinear state-space models T. Chau et al. 10.3934/dcdss.2022054
42. An iterative ensemble Kalman filter in the presence of additive model error P. Sakov et al. 10.1002/qj.3213