Localizing the Ensemble Kalman Particle Filter
Ensemble methods such as the Ensemble Kalman Filter (EnKF) are widely used for data assimilation in large-scale geophysical applications, as for example in numerical weather prediction. There is a growing interest for physical models with higher and higher resolution, which brings new challenges for...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Stockholm University Press
2017-01-01
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| Series: | Tellus: Series A, Dynamic Meteorology and Oceanography |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/16000870.2017.1282016 |
| _version_ | 1818139502865022976 |
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| author | Sylvain Robert Hans R. Künsch |
| author_facet | Sylvain Robert Hans R. Künsch |
| author_sort | Sylvain Robert |
| collection | DOAJ |
| description | Ensemble methods such as the Ensemble Kalman Filter (EnKF) are widely used for data assimilation in large-scale geophysical applications, as for example in numerical weather prediction. There is a growing interest for physical models with higher and higher resolution, which brings new challenges for data assimilation techniques because of the presence of non-linear and non-Gaussian features that are not adequately treated by the EnKF. We propose two new localized algorithms based on the Ensemble Kalman Particle Filter, a hybrid method combining the EnKF and the Particle Filter (PF) in a way that maintains scalability and sample diversity. Localization is a key element of the success of EnKF in practice, but it is much more challenging to apply to PFs. The algorithms that we introduce in the present paper provide a compromise between the EnKF and the PF while avoiding some of the problems of localization for pure PFs. Numerical experiments with a simplified model of cumulus convection based on a modified shallow water equation show that the proposed algorithms perform better than the local EnKF. In particular, the PF nature of the method allows to capture non-Gaussian characteristics of the estimated fields such as the location of wet and dry areas. |
| first_indexed | 2024-12-11T10:29:07Z |
| format | Article |
| id | doaj.art-222f105cd40343869016385db5e6a194 |
| institution | Directory Open Access Journal |
| issn | 1600-0870 |
| language | English |
| last_indexed | 2024-12-11T10:29:07Z |
| publishDate | 2017-01-01 |
| publisher | Stockholm University Press |
| record_format | Article |
| series | Tellus: Series A, Dynamic Meteorology and Oceanography |
| spelling | doaj.art-222f105cd40343869016385db5e6a1942022-12-22T01:11:01ZengStockholm University PressTellus: Series A, Dynamic Meteorology and Oceanography1600-08702017-01-0169110.1080/16000870.2017.12820161282016Localizing the Ensemble Kalman Particle FilterSylvain Robert0Hans R. Künsch1ETH ZürichETH ZürichEnsemble methods such as the Ensemble Kalman Filter (EnKF) are widely used for data assimilation in large-scale geophysical applications, as for example in numerical weather prediction. There is a growing interest for physical models with higher and higher resolution, which brings new challenges for data assimilation techniques because of the presence of non-linear and non-Gaussian features that are not adequately treated by the EnKF. We propose two new localized algorithms based on the Ensemble Kalman Particle Filter, a hybrid method combining the EnKF and the Particle Filter (PF) in a way that maintains scalability and sample diversity. Localization is a key element of the success of EnKF in practice, but it is much more challenging to apply to PFs. The algorithms that we introduce in the present paper provide a compromise between the EnKF and the PF while avoiding some of the problems of localization for pure PFs. Numerical experiments with a simplified model of cumulus convection based on a modified shallow water equation show that the proposed algorithms perform better than the local EnKF. In particular, the PF nature of the method allows to capture non-Gaussian characteristics of the estimated fields such as the location of wet and dry areas.http://dx.doi.org/10.1080/16000870.2017.1282016ensemble Kalman filterparticle filterdata assimilationnon-linear filteringlocalization |
| spellingShingle | Sylvain Robert Hans R. Künsch Localizing the Ensemble Kalman Particle Filter Tellus: Series A, Dynamic Meteorology and Oceanography ensemble Kalman filter particle filter data assimilation non-linear filtering localization |
| title | Localizing the Ensemble Kalman Particle Filter |
| title_full | Localizing the Ensemble Kalman Particle Filter |
| title_fullStr | Localizing the Ensemble Kalman Particle Filter |
| title_full_unstemmed | Localizing the Ensemble Kalman Particle Filter |
| title_short | Localizing the Ensemble Kalman Particle Filter |
| title_sort | localizing the ensemble kalman particle filter |
| topic | ensemble Kalman filter particle filter data assimilation non-linear filtering localization |
| url | http://dx.doi.org/10.1080/16000870.2017.1282016 |
| work_keys_str_mv | AT sylvainrobert localizingtheensemblekalmanparticlefilter AT hansrkunsch localizingtheensemblekalmanparticlefilter |