40 citations as recorded by crossref.

1. Data Assimilation and Online Parameter Optimization in Groundwater Modeling Using Nested Particle Filters M. Ramgraber et al. 10.1029/2018WR024408
2. Inference on high-dimensional implicit dynamic models using a guided intermediate resampling filter J. Park & E. Ionides 10.1007/s11222-020-09957-3
3. State Space Partitioning Based on Constrained Spectral Clustering for Block Particle Filtering R. MIN et al. 10.2139/ssrn.4059157
4. Assimilating satellite SST/SSH and in-situ T/S profiles with the Localized Weighted Ensemble Kalman Filter M. Shen et al. 10.1007/s13131-021-1903-2
5. On the Localization in Strongly Coupled Ensemble Data Assimilation Using a Two‐Scale Lorenz Model Z. Shen et al. 10.1029/2020EA001465
6. Toward Snow Cover Estimation in Mountainous Areas Using Modern Data Assimilation Methods: A Review C. Largeron et al. 10.3389/feart.2020.00325
7. A localized weighted ensemble Kalman filter for high‐dimensional systems Y. Chen et al. 10.1002/qj.3685
8. Applying the Sinkhorn Algorithm for Resampling of Local Particle Filter K. Oishi & S. Kotsuki 10.2151/sola.2023-024
9. A stochastic covariance shrinkage approach to particle rejuvenation in the ensemble transform particle filter A. Popov et al. 10.5194/npg-29-241-2022
10. Particle Filtering and Gaussian Mixtures – On a Localized Mixture Coefficients Particle Filter (LMCPF) for Global NWP A. ROJAHN et al. 10.2151/jmsj.2023-015
11. Ensemble Transform Algorithms for Nonlinear Smoothing Problems J. de Wiljes et al. 10.1137/19M1239544
12. Sequential model identification with reversible jump ensemble data assimilation method Y. Huan & H. Lin 10.1007/s11222-024-10499-1
13. Particle filters for high‐dimensional geoscience applications: A review P. van Leeuwen et al. 10.1002/qj.3551
14. Real-time estimation and prediction of unsteady flows using reduced-order models coupled with few measurements V. Resseguier et al. 10.1016/j.jcp.2022.111631
15. An improved framework for the dynamic likelihood filtering approach to data assimilation D. Foster & J. Restrepo 10.1063/5.0083071
16. An application of the localized weighted ensemble Kalman filter for ocean data assimilation Y. Chen et al. 10.1002/qj.3824
17. CrocO_v1.0: a particle filter to assimilate snowpack observations in a spatialised framework B. Cluzet et al. 10.5194/gmd-14-1595-2021
18. Model and data reduction for data assimilation: Particle filters employing projected forecasts and data with application to a shallow water model A. Albarakati et al. 10.1016/j.camwa.2021.05.026
19. Learning Biological Dynamics From Spatio-Temporal Data by Gaussian Processes L. Han et al. 10.1007/s11538-022-01022-6
20. ParticleDA.jl v.1.0: a distributed particle-filtering data assimilation package D. Giles et al. 10.5194/gmd-17-2427-2024
21. Propagating information from snow observations with CrocO ensemble data assimilation system: a 10-years case study over a snow depth observation network B. Cluzet et al. 10.5194/tc-16-1281-2022
22. Regularization and tempering for a moment‐matching localized particle filter J. Poterjoy 10.1002/qj.4328
23. Joint state-parameter estimation of a nonlinear stochastic energy balance model from sparse noisy data F. Lu et al. 10.5194/npg-26-227-2019
24. 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
25. Bridging classical data assimilation and optimal transport: the 3D-Var case M. Bocquet et al. 10.5194/npg-31-335-2024
26. Particle filter data assimilation for ubiquitous unstable trajectories of two-dimensional three-state cellular automata K. Furukawa et al. 10.1007/s11071-024-09803-5
27. A Hybrid Ensemble Kalman Filter to Mitigate Non-Gaussianity in Nonlinear Data Assimilation T. TSUYUKI 10.2151/jmsj.2024-027
28. Recent advancements for tropical cyclone data assimilation H. Christophersen et al. 10.1111/nyas.14873
29. Probing robustness of nonlinear filter stability numerically using Sinkhorn divergence P. Mandal et al. 10.1016/j.physd.2023.133765
30. Particle filters for data assimilation based on reduced‐order data models J. Maclean & E. Van Vleck 10.1002/qj.4001
31. State space partitioning based on constrained spectral clustering for block particle filtering R. Min et al. 10.1016/j.sigpro.2022.108727
32. The Multiple Snow Data Assimilation System (MuSA v1.0) E. Alonso-González et al. 10.5194/gmd-15-9127-2022
33. A Machine Learning Augmented Data Assimilation Method for High‐Resolution Observations L. Howard et al. 10.1029/2023MS003774
34. Nonlinear Data Assimilation by Deep Learning Embedded in an Ensemble Kalman Filter T. TSUYUKI & R. TAMURA 10.2151/jmsj.2022-027
35. Large-scale snow data assimilation using a spatialized particle filter: recovering the spatial structure of the particles J. Odry et al. 10.5194/tc-16-3489-2022
36. A Local Particle Filter Using Gamma Test Theory for High‐Dimensional State Spaces Z. Wang et al. 10.1029/2020MS002130
37. A local particle filter and its Gaussian mixture extension implemented with minor modifications to the LETKF S. Kotsuki et al. 10.5194/gmd-15-8325-2022
38. Spatio-temporal information propagation using sparse observations in hyper-resolution ensemble-based snow data assimilation E. Alonso-González et al. 10.5194/hess-27-4637-2023
39. Bayesian Update with Importance Sampling: Required Sample Size D. Sanz-Alonso & Z. Wang 10.3390/e23010022
40. Gaussian approximations in filters and smoothers for data assimilation M. Morzfeld & D. Hodyss 10.1080/16000870.2019.1600344