What the collapse of the ensemble Kalman filter tells us about particle filters
Final Published Version
AffiliationUniv Arizona, Dept Math
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PublisherTAYLOR & FRANCIS LTD
CitationMatthias 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, 1283809
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.
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AbstractThe 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.
NoteOpen Access Journal.
VersionFinal published version
SponsorsOffice 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]
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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