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.
| Identifer | oai:union.ndltd.org:arizona.edu/oai:arizona.openrepository.com:10150/623125 |
| Date | January 2017 |
| Creators | Morzfeld, Matthias, Hodyss, Daniel, Snyder, Chris |
| Contributors | Univ Arizona, Dept Math |
| Publisher | TAYLOR & FRANCIS LTD |
| Source Sets | University of Arizona |
| Language | English |
| Detected Language | English |
| Type | Article |
| Rights | © 2017 Informa UK Limited, trading as Taylor & Francis Group This is an Open Access article distributed under the terms of the Creative Commons Attribution License. |
| Relation | http://www.tandfonline.com/doi/full/10.1080/16000870.2017.1283809 |
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