41 citations as recorded by crossref.

1. Controlling balance in an ensemble Kalman filter G. Gottwald https://doi.org/10.5194/npg-21-417-2014
2. Low-rank statistical finite elements for scalable model-data synthesis C. Duffin et al. https://doi.org/10.1016/j.jcp.2022.111261
3. Cubature Ensemble Kalman Filter for Highly Dimensional Strongly Nonlinear Systems Q. Meng et al. https://doi.org/10.1109/ACCESS.2020.3014850
4. Derivation of ensemble Kalman–Bucy filters with unbounded nonlinear coefficients T. Lange https://doi.org/10.1088/1361-6544/ac4337
5. A shadowing-based inflation scheme for ensemble data assimilation T. Bellsky & L. Mitchell https://doi.org/10.1016/j.physd.2018.05.002
6. On the importance of stochasticity in closures of turbulence (a) A. Freitas et al. https://doi.org/10.1209/0295-5075/ae5a56
7. State estimation in spatiotemporal chaos via low-rank StatFEM D. Claassen et al. https://doi.org/10.1063/5.0336433
8. On the mathematical theory of ensemble (linear-Gaussian) Kalman–Bucy filtering A. Bishop & P. Del Moral https://doi.org/10.1007/s00498-023-00357-2
9. Divergence of the Ensemble Transform Kalman Filter (LETKF) by Nonlocal Observations A. Hutt https://doi.org/10.3389/fams.2020.00042
10. On the stability and the uniform propagation of chaos properties of Ensemble Kalman–Bucy filters P. Del Moral & J. Tugaut https://doi.org/10.1214/17-AAP1317
11. Probing robustness of nonlinear filter stability numerically using Sinkhorn divergence P. Mandal et al. https://doi.org/10.1016/j.physd.2023.133765
12. Multiscale Methods for Data Assimilation in Turbulent Systems Y. Lee & A. Majda https://doi.org/10.1137/140978326
13. Sequential assimilation of geomagnetic observations: perspectives for the reconstruction and prediction of core dynamics S. Sanchez et al. https://doi.org/10.1093/gji/ggz090
14. Preventing Catastrophic Filter Divergence Using Adaptive Additive Inflation for Baroclinic Turbulence Y. Lee et al. https://doi.org/10.1175/MWR-D-16-0121.1
15. An Approach for Filter Divergence Suppression in a Sequential Data Assimilation System and Its Application in Short-Term Traffic Flow Forecasting X. Tong et al. https://doi.org/10.3390/ijgi9060340
16. Estimating Time-Varying Applied Current in the Hodgkin-Huxley Model K. Campbell et al. https://doi.org/10.3390/app10020550
17. Ensemble Kalman filters for dynamical systems with unresolved turbulence I. Grooms et al. https://doi.org/10.1016/j.jcp.2014.05.037
18. Multiscale data assimilation in turbulent models F. Fossella et al. https://doi.org/10.1103/c1wn-n1j6
19. Correcting Biased Observation Model Error in Data Assimilation T. Berry & J. Harlim https://doi.org/10.1175/MWR-D-16-0428.1
20. Modular Data Assimilation for Flow Prediction A. Çıbık et al. https://doi.org/10.1002/num.70066
21. Concrete ensemble Kalman filters with rigorous catastrophic filter divergence D. Kelly et al. https://doi.org/10.1073/pnas.1511063112
22. A Martingale-Free Introduction to Conditional Gaussian Nonlinear Systems M. Andreou & N. Chen https://doi.org/10.3390/e27010002
23. Well-posedness and accuracy of the ensemble Kalman filter in discrete and continuous time D. Kelly et al. https://doi.org/10.1088/0951-7715/27/10/2579
24. Full field reconstruction of turbulent systems from sparse observations V. Gupta et al. https://doi.org/10.1017/jfm.2026.11611
25. An Adaptive Online Smoother with Closed-Form Solutions and Information-Theoretic Lag Selection for Conditional Gaussian Nonlinear Systems M. Andreou et al. https://doi.org/10.1007/s00332-026-10271-x
26. Conditional Gaussian nonlinear system: A fast preconditioner and a cheap surrogate model for complex nonlinear systems N. Chen et al. https://doi.org/10.1063/5.0081668
27. Continuous data assimilation of large eddy simulation by lattice Boltzmann method and local ensemble transform Kalman filter (LBM-LETKF) Y. Hasegawa et al. https://doi.org/10.1088/1873-7005/ad06bd
28. A unified framework for the analysis of accuracy and stability of a class of approximate Gaussian filters for the Navier–Stokes equations A. Biswas & M. Branicki https://doi.org/10.1088/1361-6544/ad805b
29. The Mean-Field Ensemble Kalman Filter: Near-Gaussian Setting J. Carrillo et al. https://doi.org/10.1137/24M1628207
30. Infinite-dimensional Bayesian inversion theory and algorithms J. Junxiong et al. https://doi.org/10.1360/SSM-2024-0100
31. 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
32. Ensemble data assimilation for earthquake sequences: probabilistic estimation and forecasting of fault stresses Y. van Dinther et al. https://doi.org/10.1093/gji/ggz063
33. $l_p$ Regularization for Ensemble Kalman Inversion Y. Lee https://doi.org/10.1137/20M1365168
34. On the Stability and the Uniform Propagation of Chaos of a Class of Extended Ensemble Kalman--Bucy Filters P. Del Moral et al. https://doi.org/10.1137/16M1087497
35. Data assimilation models for computing probability distributions of complex multiscale systems D. Qi & J. Liu https://doi.org/10.1016/j.jcp.2025.114465
36. Coupled stochastic-statistical equations for filtering multiscale turbulent systems D. Qi & J. Liu https://doi.org/10.1016/j.physd.2025.135013
37. Kernel-based prediction of non-Markovian time series F. Gilani et al. https://doi.org/10.1016/j.physd.2020.132829
38. An approach to periodic, time-varying parameter estimation using nonlinear filtering A. Arnold & A. Lloyd https://doi.org/10.1088/1361-6420/aad3e0
39. Non-asymptotic analysis of ensemble Kalman updates: effective dimension and localization O. Al-Ghattas & D. Sanz-Alonso https://doi.org/10.1093/imaiai/iaad043
40. Predictions of the geomagnetic secular variation based on the ensemble sequential assimilation of geomagnetic field models by dynamo simulations S. Sanchez et al. https://doi.org/10.1186/s40623-020-01279-y
41. Multivariate localization functions for strongly coupled data assimilation in the bivariate Lorenz 96 system Z. Stanley et al. https://doi.org/10.5194/npg-28-565-2021