59 citations as recorded by crossref.

1. Ensemble variational assimilation as a probabilistic estimator – Part 2: The fully non-linear case M. Jardak & O. Talagrand
2. An international initiative of predicting the SARS-CoV-2 pandemic using ensemble data assimilation G. Evensen et al.
3. A fast, single-iteration ensemble Kalman smoother for sequential data assimilation C. Grudzien & M. Bocquet
4. Bayesian inference of chaotic dynamics by merging data assimilation, machine learning and expectation-maximization M. Bocquet et al.
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.
6. Behavior of the iterative ensemble-based variational method in nonlinear problems S. Nakano
7. Multivariate state and parameter estimation with data assimilation applied to sea-ice models using a Maxwell elasto-brittle rheology Y. Chen et al.
8. Data assimilation in the geosciences: An overview of methods, issues, and perspectives A. Carrassi et al.
9. Accurate deep learning-based filtering for chaotic dynamics by identifying instabilities without an ensemble M. Bocquet et al.
10. Adaptive covariance inflation in the ensemble Kalman filter by Gaussian scale mixtures P. Raanes et al.
11. A candidate secular variation model for IGRF-13 based on MHD dynamo simulation and 4DEnVar data assimilation T. Minami et al.
12. Accounting for model errors in iterative ensemble smoothers G. Evensen
13. A Gaussian Mixture Model Smoother for Continuous Nonlinear Stochastic Dynamical Systems: Applications T. Lolla & P. Lermusiaux
14. Gaussian approximations in filters and smoothers for data assimilation M. Morzfeld & D. Hodyss
15. A low-order coupled chemistry meteorology model for testing online and offline data assimilation schemes: L95-GRS (v1.0) J. Haussaire & M. Bocquet
16. Data assimilation in atmospheric chemistry models: current status and future prospects for coupled chemistry meteorology models M. Bocquet et al.
17. The assessment of potential observability for joint chemical states and emissions in atmospheric modelings X. Wu et al.
18. Quasi-static ensemble variational data assimilation: a theoretical and numerical study with the iterative ensemble Kalman smoother A. Fillion et al.
19. Expanding the validity of the ensemble Kalman filter without the intrinsic need for inflation M. Bocquet et al.
20. Online learning of both state and dynamics using ensemble Kalman filters M. Bocquet et al.
21. Particle-Assisted Laser-Induced Inertial Cavitation for High Strain-Rate Soft Material Characterization S. Buyukozturk et al.
22. Joint inference of groundwater–recharge and hydraulic–conductivity fields from head data using the ensemble Kalman filter D. Erdal & O. Cirpka
23. Evaluation of iterative Kalman smoother schemes for multi-decadal past climate analysis with comprehensive Earth system models J. García-Pintado & A. Paul
24. 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.
25. TorchDA: A Python package for performing data assimilation with deep learning forward and transformation functions S. Cheng et al.
26. The Land Variational Ensemble Data Assimilation Framework: LAVENDAR v1.0.0 E. Pinnington et al.
27. High-resolution data assimilation through stochastic subgrid tensor and parameter estimation from 4DEnVar Y. Yang & E. Memin
28. Ensemble Kalman Methods for High-Dimensional Hierarchical Dynamic Space-Time Models M. Katzfuss et al.
29. Competitive renaissance through digital transformation S. Denicolai et al.
30. Estimating model evidence using data assimilation A. Carrassi et al.
31. Using global Bayesian optimization in ensemble data assimilation: parameter estimation, tuning localization and inflation, or all of the above S. Lunderman et al.
32. Assimilation of lidar signals: application to aerosol forecasting in the western Mediterranean basin Y. Wang et al.
33. Variational particle smoothers and their localization M. Morzfeld et al.
34. Ensemble Kalman Filtering with Residual Nudging: An Extension to State Estimation Problems with Nonlinear Observation Operators X. Luo & I. Hoteit
35. Nonlinear Data Assimilation by Deep Learning Embedded in an Ensemble Kalman Filter T. TSUYUKI & R. TAMURA
36. Comparison of ensemble assimilation methods in a hydrological model dedicated to agricultural best management practices E. Rouzies et al.
37. Earth System Modeling 2.0: A Blueprint for Models That Learn From Observations and Targeted High‐Resolution Simulations T. Schneider et al.
38. Parameter and state estimation with ensemble Kalman filter based algorithms for convective‐scale applications Y. Ruckstuhl & T. Janjić
39. Efficient derivative-free Bayesian inference for large-scale inverse problems D. Huang et al.
40. Revising the stochastic iterative ensemble smoother P. Raanes et al.
41. Hybrid Levenberg–Marquardt and weak-constraint ensemble Kalman smoother method J. Mandel et al.
42. 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
43. Ensemble-based seismic inversion for a stratified medium M. Gineste et al.
44. A Regional multi-Air Pollutant Assimilation System (RAPAS v1.0) for emission estimates: system development and application S. Feng et al.
45. A model-independent iterative ensemble smoother for efficient history-matching and uncertainty quantification in very high dimensions J. White
46. Coupled Stratospheric Chemistry–Meteorology Data Assimilation. Part II: Weak and Strong Coupling R. Ménard et al.
47. A New Approach to Solving the Problem of Atmospheric Air Pollution in the Industrial City Z. Oralbekova et al.
48. Φ-DVAE: Physics-informed dynamical variational autoencoders for unstructured data assimilation A. Glyn-Davies et al.
49. Training Physics‐Based Machine‐Learning Parameterizations With Gradient‐Free Ensemble Kalman Methods I. Lopez‐Gomez et al.
50. Data assimilation for Bayesian updating of predicted embankment response using monitoring data A. Amavasai et al.
51. Characterizing viscoelastic materials via ensemble-based data assimilation of bubble collapse observations J. Spratt et al.
52. 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.
53. Four-dimensional ensemble variational data assimilation and the unstable subspace M. Bocquet & A. Carrassi
54. Surrogate modeling for the climate sciences dynamics with machine learning and data assimilation M. Bocquet
55. From points to waves: Fast ocean wave spatial–temporal fields estimation using ensemble transform Kalman filter with optical measurement F. Wang et al.
56. Combining ensemble Kalman filter and reservoir computing to predict spatiotemporal chaotic systems from imperfect observations and models F. Tomizawa & Y. Sawada
57. Comparison of simulation-based algorithms for parameter estimation and state reconstruction in nonlinear state-space models T. Chau et al.
58. Hypothesis-driven modeling of the human lung–ventilator system: A characterization tool for Acute Respiratory Distress Syndrome research J. Stroh et al.
59. An iterative ensemble Kalman filter in the presence of additive model error P. Sakov et al.