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dc.contributor.author
Van Leeuwen, Peter J.
dc.contributor.author
Künsch, Hans R.
dc.contributor.author
Nerger, Lars
dc.contributor.author
Potthast, Roland
dc.contributor.author
Reich, Sebastian
dc.date.accessioned
2019-09-23T12:49:40Z
dc.date.available
2019-09-21T02:29:23Z
dc.date.available
2019-09-23T12:49:40Z
dc.date.issued
2019-07
dc.identifier.other
10.1002/qj.3551
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/365660
dc.identifier.doi
10.3929/ethz-b-000365660
dc.description.abstract
Particle filters contain the promise of fully nonlinear data assimilation. They have been applied in numerous science areas, including the geosciences, but their application to high‐dimensional geoscience systems has been limited due to their inefficiency in high‐dimensional systems in standard settings. However, huge progress has been made, and this limitation is disappearing fast due to recent developments in proposal densities, the use of ideas from (optimal) transportation, the use of localization and intelligent adaptive resampling strategies. Furthermore, powerful hybrids between particle filters and ensemble Kalman filters and variational methods have been developed. We present a state‐of‐the‐art discussion of present efforts of developing particle filters for high‐dimensional nonlinear geoscience state‐estimation problems, with an emphasis on atmospheric and oceanic applications, including many new ideas, derivations and unifications, highlighting hidden connections, including pseudo‐code, and generating a valuable tool and guide for the community. Initial experiments show that particle filters can be competitive with present‐day methods for numerical weather prediction, suggesting that they will become mainstream soon.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Wiley
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.subject
hybrids
en_US
dc.subject
localization
en_US
dc.subject
nonlinear data assimilation
en_US
dc.subject
particle filters
en_US
dc.subject
proposal densities
en_US
dc.title
Particle filters for high-dimensional geoscience applications: A review
en_US
dc.type
Review Article
dc.rights.license
Creative Commons Attribution 4.0 International
dc.date.published
2019-04-22
ethz.journal.title
Quarterly Journal of the Royal Meteorological Society
ethz.journal.volume
145
en_US
ethz.journal.issue
723
en_US
ethz.pages.start
2335
en_US
ethz.pages.end
2365
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.scopus
ethz.publication.place
Oxford
en_US
ethz.publication.status
published
en_US
ethz.date.deposited
2019-09-21T02:29:28Z
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2019-09-23T12:49:57Z
ethz.rosetta.lastUpdated
2019-09-23T12:49:57Z
ethz.rosetta.versionExported
true
ethz.COinS
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