What the collapse of the ensemble Kalman filter tells us about particle filters
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TAYLOR & FRANCIS LTDCitation
Matthias Morzfeld, Daniel Hodyss & Chris Snyder (2017) What the collapse of the ensemble Kalman filter tells us about particle filters, Tellus A: Dynamic Meteorology and Oceanography, 69:1, 1283809Rights
© 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.Collection Information
This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.Abstract
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.Note
Open Access Journal.ISSN
1600-0870Version
Final published versionSponsors
Office of Naval Research [N00173-17-2-C003, PE-0601153N]; Alfred P. Sloan Research Fellowship; National Science Foundation [DMS-1619630, DMS-1419044]; US Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, Applied Mathematics program [DE-AC02005CH11231]Additional Links
http://www.tandfonline.com/doi/full/10.1080/16000870.2017.1283809Scopus Count
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Except where otherwise noted, this item's license is described as © 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.
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