Articles | Volume 25, issue 4
https://doi.org/10.5194/npg-25-765-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/npg-25-765-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Review article
| 
12 Nov 2018
Review article |
| 12 Nov 2018
| 12 Nov 2018
Review article: Comparison of local particle filters and new implementations
CEREA, joint laboratory École des Ponts Paris Tech and EDF R&D, Université Paris-Est, Champs-sur-Marne, France
Marc Bocquet
CEREA, joint laboratory École des Ponts Paris Tech and EDF R&D, Université Paris-Est, Champs-sur-Marne, France
Viewed
Total article views: 4,369 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 05 Mar 2018)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 3,049 | 1,215 | 105 | 4,369 | 143 | 110 |
- HTML: 3,049
- PDF: 1,215
- XML: 105
- Total: 4,369
- BibTeX: 143
- EndNote: 110
Total article views: 3,619 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 12 Nov 2018)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 2,601 | 925 | 93 | 3,619 | 125 | 99 |
- HTML: 2,601
- PDF: 925
- XML: 93
- Total: 3,619
- BibTeX: 125
- EndNote: 99
Total article views: 750 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 05 Mar 2018)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 448 | 290 | 12 | 750 | 18 | 11 |
- HTML: 448
- PDF: 290
- XML: 12
- Total: 750
- BibTeX: 18
- EndNote: 11
Viewed (geographical distribution)
Total article views: 4,369 (including HTML, PDF, and XML)
Thereof 3,738 with geography defined
and 631 with unknown origin.
Total article views: 3,619 (including HTML, PDF, and XML)
Thereof 3,021 with geography defined
and 598 with unknown origin.
Total article views: 750 (including HTML, PDF, and XML)
Thereof 717 with geography defined
and 33 with unknown origin.
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
Cited
36 citations as recorded by crossref.
- Data Assimilation and Online Parameter Optimization in Groundwater Modeling Using Nested Particle Filters M. Ramgraber et al. 10.1029/2018WR024408
- Inference on high-dimensional implicit dynamic models using a guided intermediate resampling filter J. Park & E. Ionides 10.1007/s11222-020-09957-3
- State Space Partitioning Based on Constrained Spectral Clustering for Block Particle Filtering R. MIN et al. 10.2139/ssrn.4059157
- 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
- On the Localization in Strongly Coupled Ensemble Data Assimilation Using a Two‐Scale Lorenz Model Z. Shen et al. 10.1029/2020EA001465
- Toward Snow Cover Estimation in Mountainous Areas Using Modern Data Assimilation Methods: A Review C. Largeron et al. 10.3389/feart.2020.00325
- A localized weighted ensemble Kalman filter for high‐dimensional systems Y. Chen et al. 10.1002/qj.3685
- Applying the Sinkhorn Algorithm for Resampling of Local Particle Filter K. Oishi & S. Kotsuki 10.2151/sola.2023-024
- A stochastic covariance shrinkage approach to particle rejuvenation in the ensemble transform particle filter A. Popov et al. 10.5194/npg-29-241-2022
- 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
- Ensemble Transform Algorithms for Nonlinear Smoothing Problems J. de Wiljes et al. 10.1137/19M1239544
- Bayesian Update with Importance Sampling: Required Sample Size D. Sanz-Alonso & Z. Wang 10.3390/e23010022
- Particle filters for high‐dimensional geoscience applications: A review P. van Leeuwen et al. 10.1002/qj.3551
- 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
- An improved framework for the dynamic likelihood filtering approach to data assimilation D. Foster & J. Restrepo 10.1063/5.0083071
- An application of the localized weighted ensemble Kalman filter for ocean data assimilation Y. Chen et al. 10.1002/qj.3824
- CrocO_v1.0: a particle filter to assimilate snowpack observations in a spatialised framework B. Cluzet et al. 10.5194/gmd-14-1595-2021
- 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
- Learning Biological Dynamics From Spatio-Temporal Data by Gaussian Processes L. Han et al. 10.1007/s11538-022-01022-6
- ParticleDA.jl v.1.0: a distributed particle-filtering data assimilation package D. Giles et al. 10.5194/gmd-17-2427-2024
- 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
- Regularization and tempering for a moment‐matching localized particle filter J. Poterjoy 10.1002/qj.4328
- 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
- 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
- Recent advancements for tropical cyclone data assimilation H. Christophersen et al. 10.1111/nyas.14873
- Probing robustness of nonlinear filter stability numerically using Sinkhorn divergence P. Mandal et al. 10.1016/j.physd.2023.133765
- Particle filters for data assimilation based on reduced‐order data models J. Maclean & E. Van Vleck 10.1002/qj.4001
- State space partitioning based on constrained spectral clustering for block particle filtering R. Min et al. 10.1016/j.sigpro.2022.108727
- The Multiple Snow Data Assimilation System (MuSA v1.0) E. Alonso-González et al. 10.5194/gmd-15-9127-2022
- A Machine Learning Augmented Data Assimilation Method for High‐Resolution Observations L. Howard et al. 10.1029/2023MS003774
- Nonlinear Data Assimilation by Deep Learning Embedded in an Ensemble Kalman Filter T. TSUYUKI & R. TAMURA 10.2151/jmsj.2022-027
- 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
- A Local Particle Filter Using Gamma Test Theory for High‐Dimensional State Spaces Z. Wang et al. 10.1029/2020MS002130
- 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
- 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
- Gaussian approximations in filters and smoothers for data assimilation M. Morzfeld & D. Hodyss 10.1080/16000870.2019.1600344
35 citations as recorded by crossref.
- Data Assimilation and Online Parameter Optimization in Groundwater Modeling Using Nested Particle Filters M. Ramgraber et al. 10.1029/2018WR024408
- Inference on high-dimensional implicit dynamic models using a guided intermediate resampling filter J. Park & E. Ionides 10.1007/s11222-020-09957-3
- State Space Partitioning Based on Constrained Spectral Clustering for Block Particle Filtering R. MIN et al. 10.2139/ssrn.4059157
- 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
- On the Localization in Strongly Coupled Ensemble Data Assimilation Using a Two‐Scale Lorenz Model Z. Shen et al. 10.1029/2020EA001465
- Toward Snow Cover Estimation in Mountainous Areas Using Modern Data Assimilation Methods: A Review C. Largeron et al. 10.3389/feart.2020.00325
- A localized weighted ensemble Kalman filter for high‐dimensional systems Y. Chen et al. 10.1002/qj.3685
- Applying the Sinkhorn Algorithm for Resampling of Local Particle Filter K. Oishi & S. Kotsuki 10.2151/sola.2023-024
- A stochastic covariance shrinkage approach to particle rejuvenation in the ensemble transform particle filter A. Popov et al. 10.5194/npg-29-241-2022
- 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
- Ensemble Transform Algorithms for Nonlinear Smoothing Problems J. de Wiljes et al. 10.1137/19M1239544
- Bayesian Update with Importance Sampling: Required Sample Size D. Sanz-Alonso & Z. Wang 10.3390/e23010022
- Particle filters for high‐dimensional geoscience applications: A review P. van Leeuwen et al. 10.1002/qj.3551
- 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
- An improved framework for the dynamic likelihood filtering approach to data assimilation D. Foster & J. Restrepo 10.1063/5.0083071
- An application of the localized weighted ensemble Kalman filter for ocean data assimilation Y. Chen et al. 10.1002/qj.3824
- CrocO_v1.0: a particle filter to assimilate snowpack observations in a spatialised framework B. Cluzet et al. 10.5194/gmd-14-1595-2021
- 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
- Learning Biological Dynamics From Spatio-Temporal Data by Gaussian Processes L. Han et al. 10.1007/s11538-022-01022-6
- ParticleDA.jl v.1.0: a distributed particle-filtering data assimilation package D. Giles et al. 10.5194/gmd-17-2427-2024
- 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
- Regularization and tempering for a moment‐matching localized particle filter J. Poterjoy 10.1002/qj.4328
- 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
- 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
- Recent advancements for tropical cyclone data assimilation H. Christophersen et al. 10.1111/nyas.14873
- Probing robustness of nonlinear filter stability numerically using Sinkhorn divergence P. Mandal et al. 10.1016/j.physd.2023.133765
- Particle filters for data assimilation based on reduced‐order data models J. Maclean & E. Van Vleck 10.1002/qj.4001
- State space partitioning based on constrained spectral clustering for block particle filtering R. Min et al. 10.1016/j.sigpro.2022.108727
- The Multiple Snow Data Assimilation System (MuSA v1.0) E. Alonso-González et al. 10.5194/gmd-15-9127-2022
- A Machine Learning Augmented Data Assimilation Method for High‐Resolution Observations L. Howard et al. 10.1029/2023MS003774
- Nonlinear Data Assimilation by Deep Learning Embedded in an Ensemble Kalman Filter T. TSUYUKI & R. TAMURA 10.2151/jmsj.2022-027
- 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
- A Local Particle Filter Using Gamma Test Theory for High‐Dimensional State Spaces Z. Wang et al. 10.1029/2020MS002130
- 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
- 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
1 citations as recorded by crossref.
Latest update: 18 Apr 2024
Short summary
Data assimilation looks for an optimal way to learn from observations of a dynamical system to improve the quality of its predictions. The goal is to filter out the noise (both observation and model noise) to retrieve the true signal. Among all possible methods, particle filters are promising; the method is fast and elegant, and it allows for a Bayesian analysis. In this review paper, we discuss implementation techniques for (local) particle filters in high-dimensional systems.
Data assimilation looks for an optimal way to learn from observations of a dynamical system to...
