What the collapse of the ensemble Kalman filter tells us about particle filters
The ensemble Kalman filter (EnKF) is a reliable data assimilation tool for high-dimensional meteorological problems. On the other hand, the EnKF can be interpreted as a particle filter, and particle filters (PF) collapse in high-dimensional problems. We explain that these seemingly contradictory sta...
| 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/10.1080/16000870.2017.1283809 |
| _version_ | 1817980194018820096 |
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| author | Matthias Morzfeld Daniel Hodyss Chris Snyder |
| author_facet | Matthias Morzfeld Daniel Hodyss Chris Snyder |
| author_sort | Matthias Morzfeld |
| collection | DOAJ |
| description | The ensemble Kalman filter (EnKF) is a reliable data assimilation tool for high-dimensional meteorological problems. On the other hand, the EnKF can be interpreted as a particle filter, and particle filters (PF) collapse in high-dimensional problems. We explain that these seemingly contradictory statements offer insights about how PF function in certain high-dimensional problems, and in particular support recent efforts in meteorology to ‘localize’ particle filters, i.e. to restrict the influence of an observation to its neighbourhood. |
| first_indexed | 2024-04-13T22:51:20Z |
| format | Article |
| id | doaj.art-a091d72fd295456ca5ff0e4349ed252e |
| institution | Directory Open Access Journal |
| issn | 1600-0870 |
| language | English |
| last_indexed | 2024-04-13T22:51:20Z |
| publishDate | 2017-01-01 |
| publisher | Stockholm University Press |
| record_format | Article |
| series | Tellus: Series A, Dynamic Meteorology and Oceanography |
| spelling | doaj.art-a091d72fd295456ca5ff0e4349ed252e2022-12-22T02:26:10ZengStockholm University PressTellus: Series A, Dynamic Meteorology and Oceanography1600-08702017-01-0169110.1080/16000870.2017.12838091283809What the collapse of the ensemble Kalman filter tells us about particle filtersMatthias Morzfeld0Daniel Hodyss1Chris Snyder2University of ArizonaNaval Research LaboratoryNational Center for Atmospheric ResearchThe ensemble Kalman filter (EnKF) is a reliable data assimilation tool for high-dimensional meteorological problems. On the other hand, the EnKF can be interpreted as a particle filter, and particle filters (PF) collapse in high-dimensional problems. We explain that these seemingly contradictory statements offer insights about how PF function in certain high-dimensional problems, and in particular support recent efforts in meteorology to ‘localize’ particle filters, i.e. to restrict the influence of an observation to its neighbourhood.http://dx.doi.org/10.1080/16000870.2017.1283809ensemble Kalman filterparticle filtercollapse of particle filters |
| spellingShingle | Matthias Morzfeld Daniel Hodyss Chris Snyder What the collapse of the ensemble Kalman filter tells us about particle filters Tellus: Series A, Dynamic Meteorology and Oceanography ensemble Kalman filter particle filter collapse of particle filters |
| title | What the collapse of the ensemble Kalman filter tells us about particle filters |
| title_full | What the collapse of the ensemble Kalman filter tells us about particle filters |
| title_fullStr | What the collapse of the ensemble Kalman filter tells us about particle filters |
| title_full_unstemmed | What the collapse of the ensemble Kalman filter tells us about particle filters |
| title_short | What the collapse of the ensemble Kalman filter tells us about particle filters |
| title_sort | what the collapse of the ensemble kalman filter tells us about particle filters |
| topic | ensemble Kalman filter particle filter collapse of particle filters |
| url | http://dx.doi.org/10.1080/16000870.2017.1283809 |
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