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
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Cited
42 citations as recorded by crossref.
- Localization in the mapping particle filter J. Guerrieri et al.
- Inference on high-dimensional implicit dynamic models using a guided intermediate resampling filter J. Park & E. Ionides
- State Space Partitioning Based on Constrained Spectral Clustering for Block Particle Filtering R. MIN et al.
- On the Localization in Strongly Coupled Ensemble Data Assimilation Using a Two‐Scale Lorenz Model Z. Shen et al.
- Toward Snow Cover Estimation in Mountainous Areas Using Modern Data Assimilation Methods: A Review C. Largeron et al.
- A localized weighted ensemble Kalman filter for high‐dimensional systems Y. Chen et al.
- A stochastic covariance shrinkage approach to particle rejuvenation in the ensemble transform particle filter A. Popov et al.
- Real-time estimation and prediction of unsteady flows using reduced-order models coupled with few measurements V. Resseguier et al.
- An improved framework for the dynamic likelihood filtering approach to data assimilation D. Foster & J. Restrepo
- CrocO_v1.0: a particle filter to assimilate snowpack observations in a spatialised framework B. Cluzet et al.
- 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.
- Using global Bayesian optimization in ensemble data assimilation: parameter estimation, tuning localization and inflation, or all of the above S. Lunderman et al.
- Particle filter data assimilation for ubiquitous unstable trajectories of two-dimensional three-state cellular automata K. Furukawa et al.
- Scalable marginalized particle filter to improve state estimation of one-way coupled PDE systems H. Iqbal & C. Claudel
- Recent advancements for tropical cyclone data assimilation H. Christophersen et al.
- Probing robustness of nonlinear filter stability numerically using Sinkhorn divergence P. Mandal et al.
- State space partitioning based on constrained spectral clustering for block particle filtering R. Min et al.
- The Multiple Snow Data Assimilation System (MuSA v1.0) E. Alonso-González et al.
- Nonlinear Data Assimilation by Deep Learning Embedded in an Ensemble Kalman Filter T. TSUYUKI & R. TAMURA
- Large-scale snow data assimilation using a spatialized particle filter: recovering the spatial structure of the particles J. Odry et al.
- A Local Particle Filter Using Gamma Test Theory for High‐Dimensional State Spaces Z. Wang et al.
- A local particle filter and its Gaussian mixture extension implemented with minor modifications to the LETKF S. Kotsuki et al.
- Spatio-temporal information propagation using sparse observations in hyper-resolution ensemble-based snow data assimilation E. Alonso-González et al.
- Data Assimilation and Online Parameter Optimization in Groundwater Modeling Using Nested Particle Filters M. Ramgraber et al.
- Assimilating satellite SST/SSH and in-situ T/S profiles with the Localized Weighted Ensemble Kalman Filter M. Shen et al.
- Applying the Sinkhorn Algorithm for Resampling of Local Particle Filter K. Oishi & S. Kotsuki
- Particle Filtering and Gaussian Mixtures – On a Localized Mixture Coefficients Particle Filter (LMCPF) for Global NWP A. ROJAHN et al.
- Ensemble Transform Algorithms for Nonlinear Smoothing Problems J. de Wiljes et al.
- Sequential model identification with reversible jump ensemble data assimilation method Y. Huan & H. Lin
- Remaining Useful Life Prediction of Lithium Batteries Based on Transfer Learning and Particle Filter Fusion L. Chen et al.
- Particle filters for high‐dimensional geoscience applications: A review P. van Leeuwen et al.
- An application of the localized weighted ensemble Kalman filter for ocean data assimilation Y. Chen et al.
- Learning Biological Dynamics From Spatio-Temporal Data by Gaussian Processes L. Han et al.
- ParticleDA.jl v.1.0: a distributed particle-filtering data assimilation package D. Giles et al.
- 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.
- Regularization and tempering for a moment‐matching localized particle filter J. Poterjoy
- Joint state-parameter estimation of a nonlinear stochastic energy balance model from sparse noisy data F. Lu et al.
- Bridging classical data assimilation and optimal transport: the 3D-Var case M. Bocquet et al.
- A Hybrid Ensemble Kalman Filter to Mitigate Non-Gaussianity in Nonlinear Data Assimilation T. TSUYUKI
- Particle filters for data assimilation based on reduced‐order data models J. Maclean & E. Van Vleck
- A Machine Learning Augmented Data Assimilation Method for High‐Resolution Observations L. Howard et al.
- Ensemble transport filter via optimized maximum mean discrepancy D. Zeng & L. Jiang
42 citations as recorded by crossref.
- Localization in the mapping particle filter J. Guerrieri et al.
- Inference on high-dimensional implicit dynamic models using a guided intermediate resampling filter J. Park & E. Ionides
- State Space Partitioning Based on Constrained Spectral Clustering for Block Particle Filtering R. MIN et al.
- On the Localization in Strongly Coupled Ensemble Data Assimilation Using a Two‐Scale Lorenz Model Z. Shen et al.
- Toward Snow Cover Estimation in Mountainous Areas Using Modern Data Assimilation Methods: A Review C. Largeron et al.
- A localized weighted ensemble Kalman filter for high‐dimensional systems Y. Chen et al.
- A stochastic covariance shrinkage approach to particle rejuvenation in the ensemble transform particle filter A. Popov et al.
- Real-time estimation and prediction of unsteady flows using reduced-order models coupled with few measurements V. Resseguier et al.
- An improved framework for the dynamic likelihood filtering approach to data assimilation D. Foster & J. Restrepo
- CrocO_v1.0: a particle filter to assimilate snowpack observations in a spatialised framework B. Cluzet et al.
- 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.
- Using global Bayesian optimization in ensemble data assimilation: parameter estimation, tuning localization and inflation, or all of the above S. Lunderman et al.
- Particle filter data assimilation for ubiquitous unstable trajectories of two-dimensional three-state cellular automata K. Furukawa et al.
- Scalable marginalized particle filter to improve state estimation of one-way coupled PDE systems H. Iqbal & C. Claudel
- Recent advancements for tropical cyclone data assimilation H. Christophersen et al.
- Probing robustness of nonlinear filter stability numerically using Sinkhorn divergence P. Mandal et al.
- State space partitioning based on constrained spectral clustering for block particle filtering R. Min et al.
- The Multiple Snow Data Assimilation System (MuSA v1.0) E. Alonso-González et al.
- Nonlinear Data Assimilation by Deep Learning Embedded in an Ensemble Kalman Filter T. TSUYUKI & R. TAMURA
- Large-scale snow data assimilation using a spatialized particle filter: recovering the spatial structure of the particles J. Odry et al.
- A Local Particle Filter Using Gamma Test Theory for High‐Dimensional State Spaces Z. Wang et al.
- A local particle filter and its Gaussian mixture extension implemented with minor modifications to the LETKF S. Kotsuki et al.
- Spatio-temporal information propagation using sparse observations in hyper-resolution ensemble-based snow data assimilation E. Alonso-González et al.
- Data Assimilation and Online Parameter Optimization in Groundwater Modeling Using Nested Particle Filters M. Ramgraber et al.
- Assimilating satellite SST/SSH and in-situ T/S profiles with the Localized Weighted Ensemble Kalman Filter M. Shen et al.
- Applying the Sinkhorn Algorithm for Resampling of Local Particle Filter K. Oishi & S. Kotsuki
- Particle Filtering and Gaussian Mixtures – On a Localized Mixture Coefficients Particle Filter (LMCPF) for Global NWP A. ROJAHN et al.
- Ensemble Transform Algorithms for Nonlinear Smoothing Problems J. de Wiljes et al.
- Sequential model identification with reversible jump ensemble data assimilation method Y. Huan & H. Lin
- Remaining Useful Life Prediction of Lithium Batteries Based on Transfer Learning and Particle Filter Fusion L. Chen et al.
- Particle filters for high‐dimensional geoscience applications: A review P. van Leeuwen et al.
- An application of the localized weighted ensemble Kalman filter for ocean data assimilation Y. Chen et al.
- Learning Biological Dynamics From Spatio-Temporal Data by Gaussian Processes L. Han et al.
- ParticleDA.jl v.1.0: a distributed particle-filtering data assimilation package D. Giles et al.
- 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.
- Regularization and tempering for a moment‐matching localized particle filter J. Poterjoy
- Joint state-parameter estimation of a nonlinear stochastic energy balance model from sparse noisy data F. Lu et al.
- Bridging classical data assimilation and optimal transport: the 3D-Var case M. Bocquet et al.
- A Hybrid Ensemble Kalman Filter to Mitigate Non-Gaussianity in Nonlinear Data Assimilation T. TSUYUKI
- Particle filters for data assimilation based on reduced‐order data models J. Maclean & E. Van Vleck
- A Machine Learning Augmented Data Assimilation Method for High‐Resolution Observations L. Howard et al.
- Ensemble transport filter via optimized maximum mean discrepancy D. Zeng & L. Jiang
Saved (final revised paper)
Latest update: 26 May 2026
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...
