Articles | Volume 23, issue 6
https://doi.org/10.5194/npg-23-391-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/npg-23-391-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
04 Nov 2016
Research article |
| 04 Nov 2016
A local particle filter for high-dimensional geophysical systems
Stephen G. Penny
CORRESPONDING AUTHOR
Department of Atmospheric and Oceanic Science, University of Maryland, College Park, MD, USA
National Centers for Environmental Prediction, College Park, MD, USA
RIKEN Advanced Institute for Computational Science, Kobe, Japan
Takemasa Miyoshi
RIKEN Advanced Institute for Computational Science, Kobe, Japan
Department of Atmospheric and Oceanic Science, University of Maryland, College Park, MD, USA
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- 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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- State-of-the-art stochastic data assimilation methods for high-dimensional non-Gaussian problems S. Vetra-Carvalho et al. 10.1080/16000870.2018.1445364
- 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
- A hybrid particle-ensemble Kalman filter for problems with medium nonlinearity I. Grooms et al. 10.1371/journal.pone.0248266
- A new formulation of vector weights in localized particle filters Z. Shen et al. 10.1002/qj.3180
- Progress toward the Application of a Localized Particle Filter for Numerical Weather Prediction J. Poterjoy et al. 10.1175/MWR-D-17-0344.1
- 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
- Reanalysis in Earth System Science: Toward Terrestrial Ecosystem Reanalysis R. Baatz et al. 10.1029/2020RG000715
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- 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
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- Socio-hydrological data assimilation: analyzing human–flood interactions by model–data integration Y. Sawada & R. Hanazaki 10.5194/hess-24-4777-2020
- Ocean satellite data assimilation using the implicit equal-weights variational particle smoother P. Wang et al. 10.1016/j.ocemod.2021.101833
- 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
- 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
- CrocO_v1.0: a particle filter to assimilate snowpack observations in a spatialised framework B. Cluzet et al. 10.5194/gmd-14-1595-2021
- Ensemble Kalman filter based data assimilation for tropical waves in the MJO skeleton model T. Gleiter et al. 10.1002/qj.4245
- Improving Particle Filter Performance by Smoothing Observations G. Robinson et al. 10.1175/MWR-D-17-0349.1
- 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
- A comparison of nonlinear extensions to the ensemble Kalman filter I. Grooms 10.1007/s10596-022-10141-x
- Particle Filters for nonlinear data assimilation in high-dimensional systems P. van Leeuwen 10.5802/afst.1560
- Particle filters for high‐dimensional geoscience applications: A review P. Leeuwen et al. 10.1002/qj.3551
- 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
- Review article: Comparison of local particle filters and new implementations A. Farchi & M. Bocquet 10.5194/npg-25-765-2018
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- Second-order extended particle filter with exponential family observation model X. Zhang & Z. Yan 10.1080/00949655.2020.1767103
- A localized weighted ensemble Kalman filter for high‐dimensional systems Y. Chen et al. 10.1002/qj.3685
- Spatial Partition-Based Particle Filtering for Data Assimilation in Wildfire Spread Simulation Y. Long & X. Hu 10.1145/3099471
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Latest update: 30 Mar 2023
Short summary
Particle filters in their basic form have been shown to be unusable for large geophysical systems because the number of required particles grows exponentially with the size of the system. We have applied the ideas of localized analyses at each model grid point and use ensemble weight smoothing to blend each local analysis with its neighbors. This new local particle filter (LPF) makes large geophysical applications tractable for particle filters and is competitive with a popular EnKF alternative.
Particle filters in their basic form have been shown to be unusable for large geophysical...
