53 citations as recorded by crossref.

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2. Application of ensemble transform data assimilation methods for parameter estimation in reservoir modeling S. Ruchi & S. Dubinkina https://doi.org/10.5194/npg-25-731-2018
3. State-of-the-art stochastic data assimilation methods for high-dimensional non-Gaussian problems S. Vetra-Carvalho et al. https://doi.org/10.1080/16000870.2018.1445364
4. A new formulation of vector weights in localized particle filters Z. Shen et al. https://doi.org/10.1002/qj.3180
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6. A localized particle filter data assimilation method coupled with a Huber loss function Y. Bai et al. https://doi.org/10.1007/s11227-026-08626-y
7. Joint state-parameter estimation of a nonlinear stochastic energy balance model from sparse noisy data F. Lu et al. https://doi.org/10.5194/npg-26-227-2019
8. Assimilating satellite SST/SSH and in-situ T/S profiles with the Localized Weighted Ensemble Kalman Filter M. Shen et al. https://doi.org/10.1007/s13131-021-1903-2
9. Regularization and tempering for a moment‐matching localized particle filter J. Poterjoy https://doi.org/10.1002/qj.4328
10. Overview and Prospect of Data Assimilation in Numerical Weather Prediction L. Lei et al. https://doi.org/10.1007/s13351-025-4905-8
11. Socio-hydrological data assimilation: analyzing human–flood interactions by model–data integration Y. Sawada & R. Hanazaki https://doi.org/10.5194/hess-24-4777-2020
12. Comparison of regularized ensemble Kalman filter and tempered ensemble transform particle filter for an elliptic inverse problem with uncertain boundary conditions S. Dubinkina & S. Ruchi https://doi.org/10.1007/s10596-019-09904-w
13. Two Methods for Data Assimilation of Wind Direction I. Grooms https://doi.org/10.16993/tellusa.2005
14. CrocO_v1.0: a particle filter to assimilate snowpack observations in a spatialised framework B. Cluzet et al. https://doi.org/10.5194/gmd-14-1595-2021
15. Ensemble Kalman filter based data assimilation for tropical waves in the MJO skeleton model T. Gleiter et al. https://doi.org/10.1002/qj.4245
16. Evaluation of a localized particle filter with all-sky FY-4 AGRI infrared radiance assimilation for the convection-permitting simulation of an extremely heavy rainfall event H. Chen et al. https://doi.org/10.1016/j.atmosres.2026.108866
17. A deviation correction strategy based on particle filtering and improved model predictive control for vertical drilling D. Zhang et al. https://doi.org/10.1016/j.isatra.2020.11.023
18. Ensemble-based model predictive control using data assimilation techniques K. Kurosawa et al. https://doi.org/10.5194/npg-32-293-2025
19. An improved framework for the dynamic likelihood filtering approach to data assimilation D. Foster & J. Restrepo https://doi.org/10.1063/5.0083071
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21. Particle filters for high‐dimensional geoscience applications: A review P. van Leeuwen et al. https://doi.org/10.1002/qj.3551
22. Particle Filtering and Gaussian Mixtures – On a Localized Mixture Coefficients Particle Filter (LMCPF) for Global NWP A. ROJAHN et al. https://doi.org/10.2151/jmsj.2023-015
23. 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. https://doi.org/10.5194/tc-16-1281-2022
24. Analog ensemble data assimilation in a quasigeostrophic coupled model I. Grooms et al. https://doi.org/10.1002/qj.4446
25. Reply to Comment by Jie Qin and Teng Wu on “A Modified Particle Filter‐Based Data Assimilation Method for a High‐Precision 2‐D Hydrodynamic Model Considering Spatial‐Temporal Variability of Roughness: Simulation of Dam‐Break Flood Inundation” Y. Cao et al. https://doi.org/10.1029/2020WR027315
26. A Hybrid Ensemble Kalman Filter to Mitigate Non-Gaussianity in Nonlinear Data Assimilation T. TSUYUKI https://doi.org/10.2151/jmsj.2024-027
27. A Modified Particle Filter‐Based Data Assimilation Method for a High‐Precision 2‐D Hydrodynamic Model Considering Spatial‐temporal Variability of Roughness: Simulation of Dam‐Break Flood Inundation Y. Cao et al. https://doi.org/10.1029/2018WR023568
28. A hybrid particle-ensemble Kalman filter for problems with medium nonlinearity I. Grooms et al. https://doi.org/10.1371/journal.pone.0248266
29. Large-scale snow data assimilation using a spatialized particle filter: recovering the spatial structure of the particles J. Odry et al. https://doi.org/10.5194/tc-16-3489-2022
30. A local particle filter and its Gaussian mixture extension implemented with minor modifications to the LETKF S. Kotsuki et al. https://doi.org/10.5194/gmd-15-8325-2022
31. Progress toward the Application of a Localized Particle Filter for Numerical Weather Prediction J. Poterjoy et al. https://doi.org/10.1175/MWR-D-17-0344.1
32. A Reduced-Space Ensemble Kalman Filter Approach for Flow-Dependent Integration of Radar Extrapolation Nowcasts and NWP Precipitation Ensembles D. Nerini et al. https://doi.org/10.1175/MWR-D-18-0258.1
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34. Reanalysis in Earth System Science: Toward Terrestrial Ecosystem Reanalysis R. Baatz et al. https://doi.org/10.1029/2020RG000715
35. Lagrangian Data Assimilation of Surface Drifters in a Double-Gyre Ocean Model Using the Local Ensemble Transform Kalman Filter L. Sun & S. Penny https://doi.org/10.1175/MWR-D-18-0406.1
36. On Two Localized Particle Filter Methods for Lorenz 1963 and 1996 Models N. Schenk et al. https://doi.org/10.3389/fams.2022.920186
37. Imaging the Lower Ionosphere Using Network Measurements of VLF Transmitter Signals and a Particle Filter Algorithm W. Ma et al. https://doi.org/10.1109/JSTARS.2025.3645493
38. A dynamic likelihood approach to filtering J. Restrepo https://doi.org/10.1002/qj.3143
39. Uncertainty in hydrogeophysics: electrical resistivity tomography with variational inference J. Yan et al. https://doi.org/10.1093/gji/ggag137
40. Assimilating SMOS Brightness Temperature for Hydrologic Model Parameters and Soil Moisture Estimation with an Immune Evolutionary Strategy F. Ju et al. https://doi.org/10.3390/rs12101556
41. Ocean satellite data assimilation using the implicit equal-weights variational particle smoother P. Wang et al. https://doi.org/10.1016/j.ocemod.2021.101833
42. Convective-Scale Data Assimilation for the Weather Research and Forecasting Model Using the Local Particle Filter J. Poterjoy et al. https://doi.org/10.1175/MWR-D-16-0298.1
43. Redox mechanism of geobattery and related electrical signals using a novel real-time self-potential monitoring experimental platform J. Xie et al. https://doi.org/10.1007/s11771-024-5769-2
44. Improving Particle Filter Performance by Smoothing Observations G. Robinson et al. https://doi.org/10.1175/MWR-D-17-0349.1
45. Applying the Sinkhorn Algorithm for Resampling of Local Particle Filter K. Oishi & S. Kotsuki https://doi.org/10.2151/sola.2023-024
46. Application of Hybrid Data Assimilation Methods for Mesoscale Eddy Simulation and Prediction in the South China Sea Y. Shan et al. https://doi.org/10.3390/atmos16101193
47. Particle Filters for nonlinear data assimilation in high-dimensional systems P. van Leeuwen https://doi.org/10.5802/afst.1560
48. Impacts of the Lagrangian Data Assimilation of Surface Drifters on Estimating Ocean Circulation during the Gulf of Mexico Grand Lagrangian Deployment L. Sun et al. https://doi.org/10.1175/MWR-D-21-0123.1
49. Improving the Analyses and Forecasts of a Tropical Squall Line Using Upper Tropospheric Infrared Satellite Observations M. Chan & X. Chen https://doi.org/10.1007/s00376-021-0449-8
50. Scalable marginalized particle filter to improve state estimation of one-way coupled PDE systems H. Iqbal & C. Claudel https://doi.org/10.1016/j.apm.2024.115807
51. Review article: Comparison of local particle filters and new implementations A. Farchi & M. Bocquet https://doi.org/10.5194/npg-25-765-2018
52. Localization in the mapping particle filter J. Guerrieri et al. https://doi.org/10.5194/npg-33-33-2026
53. Ensemble variational Bayesian approximation for the inversion and uncertainty quantification of Darcy flows in heterogeneous porous media with random parameters Z. Zhang et al. https://doi.org/10.1016/j.jcp.2024.113052