49 citations as recorded by crossref.

1. 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. 10.1029/2020WR027315
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5. A Hybrid Ensemble Kalman Filter to Mitigate Non-Gaussianity in Nonlinear Data Assimilation T. TSUYUKI 10.2151/jmsj.2024-027
6. 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. 10.1029/2018WR023568
7. A hybrid particle-ensemble Kalman filter for problems with medium nonlinearity I. Grooms et al. 10.1371/journal.pone.0248266
8. 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
9. A new formulation of vector weights in localized particle filters Z. Shen et al. 10.1002/qj.3180
10. 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
11. Progress toward the Application of a Localized Particle Filter for Numerical Weather Prediction J. Poterjoy et al. 10.1175/MWR-D-17-0344.1
12. A Reduced-Space Ensemble Kalman Filter Approach for Flow-Dependent Integration of Radar Extrapolation Nowcasts and NWP Precipitation Ensembles D. Nerini et al. 10.1175/MWR-D-18-0258.1
13. Nonlinear Data Assimilation by Deep Learning Embedded in an Ensemble Kalman Filter T. TSUYUKI & R. TAMURA 10.2151/jmsj.2022-027
14. Reanalysis in Earth System Science: Toward Terrestrial Ecosystem Reanalysis R. Baatz et al. 10.1029/2020RG000715
15. Lagrangian Data Assimilation of Surface Drifters in a Double-Gyre Ocean Model Using the Local Ensemble Transform Kalman Filter L. Sun & S. Penny 10.1175/MWR-D-18-0406.1
16. On Two Localized Particle Filter Methods for Lorenz 1963 and 1996 Models N. Schenk et al. 10.3389/fams.2022.920186
17. A dynamic likelihood approach to filtering J. Restrepo 10.1002/qj.3143
18. 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
19. Assimilating SMOS Brightness Temperature for Hydrologic Model Parameters and Soil Moisture Estimation with an Immune Evolutionary Strategy F. Ju et al. 10.3390/rs12101556
20. 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
21. Regularization and tempering for a moment‐matching localized particle filter J. Poterjoy 10.1002/qj.4328
22. Socio-hydrological data assimilation: analyzing human–flood interactions by model–data integration Y. Sawada & R. Hanazaki 10.5194/hess-24-4777-2020
23. Ocean satellite data assimilation using the implicit equal-weights variational particle smoother P. Wang et al. 10.1016/j.ocemod.2021.101833
24. Convective-Scale Data Assimilation for the Weather Research and Forecasting Model Using the Local Particle Filter J. Poterjoy et al. 10.1175/MWR-D-16-0298.1
25. 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 10.1007/s10596-019-09904-w
26. Two Methods for Data Assimilation of Wind Direction I. Grooms 10.16993/tellusa.2005
27. CrocO_v1.0: a particle filter to assimilate snowpack observations in a spatialised framework B. Cluzet et al. 10.5194/gmd-14-1595-2021
28. Ensemble Kalman filter based data assimilation for tropical waves in the MJO skeleton model T. Gleiter et al. 10.1002/qj.4245
29. Improving Particle Filter Performance by Smoothing Observations G. Robinson et al. 10.1175/MWR-D-17-0349.1
30. A deviation correction strategy based on particle filtering and improved model predictive control for vertical drilling D. Zhang et al. 10.1016/j.isatra.2020.11.023
31. An improved framework for the dynamic likelihood filtering approach to data assimilation D. Foster & J. Restrepo 10.1063/5.0083071
32. Applying the Sinkhorn Algorithm for Resampling of Local Particle Filter K. Oishi & S. Kotsuki 10.2151/sola.2023-024
33. A comparison of nonlinear extensions to the ensemble Kalman filter I. Grooms 10.1007/s10596-022-10141-x
34. Particle Filters for nonlinear data assimilation in high-dimensional systems P. van Leeuwen 10.5802/afst.1560
35. Particle filters for high‐dimensional geoscience applications: A review P. van Leeuwen et al. 10.1002/qj.3551
36. 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. 10.1175/MWR-D-21-0123.1
37. 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
38. Improving the Analyses and Forecasts of a Tropical Squall Line Using Upper Tropospheric Infrared Satellite Observations M. Chan & X. Chen 10.1007/s00376-021-0449-8
39. Review article: Comparison of local particle filters and new implementations A. Farchi & M. Bocquet 10.5194/npg-25-765-2018
40. Ensemble variational Bayesian approximation for the inversion and uncertainty quantification of Darcy flows in heterogeneous porous media with random parameters Z. Zhang et al. 10.1016/j.jcp.2024.113052
41. 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
42. Analog ensemble data assimilation in a quasigeostrophic coupled model I. Grooms et al. 10.1002/qj.4446
43. Gaussian approximations in filters and smoothers for data assimilation M. Morzfeld & D. Hodyss 10.1080/16000870.2019.1600344
44. Second-order extended particle filter with exponential family observation model X. Zhang & Z. Yan 10.1080/00949655.2020.1767103
45. A localized weighted ensemble Kalman filter for high‐dimensional systems Y. Chen et al. 10.1002/qj.3685
46. Spatial Partition-Based Particle Filtering for Data Assimilation in Wildfire Spread Simulation Y. Long & X. Hu 10.1145/3099471
47. Variational particle smoothers and their localization M. Morzfeld et al. 10.1002/qj.3256
48. An application of the localized weighted ensemble Kalman filter for ocean data assimilation Y. Chen et al. 10.1002/qj.3824
49. A Local Particle Filter Using Gamma Test Theory for High‐Dimensional State Spaces Z. Wang et al. 10.1029/2020MS002130