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dc.contributor.author
van Leeuwen, Peter Jan
dc.contributor.author
Künsch, Hans R.
dc.contributor.author
Nerger, Lars
dc.contributor.author
Potthast, Roland
dc.contributor.author
Reich, Sebastian
dc.date.accessioned
2020-01-28T11:48:44Z
dc.date.available
2020-01-28T11:48:44Z
dc.date.issued
2019-07
dc.identifier.issn
0035-9009
dc.identifier.issn
1477-870X
dc.identifier.other
10.1002/qj.3551
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/394868
dc.identifier.doi
10.3929/ethz-b-000364084
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
2019-05-21
ethz.journal.title
Quarterly Journal of the Royal Meteorological Society
ethz.journal.volume
145
en_US
ethz.journal.issue
723
en_US
ethz.journal.abbreviated
Q. J. R. Meteorol. Soc.
ethz.pages.start
2335
en_US
ethz.pages.end
2365
en_US
ethz.size
31 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
London
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02000 - Dep. Mathematik / Dep. of Mathematics::02537 - Seminar für Statistik (SfS) / Seminar for Statistics (SfS)
en_US
ethz.date.deposited
2019-09-15T02:31:12Z
ethz.source
WOS
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2020-01-28T11:49:13Z
ethz.rosetta.lastUpdated
2020-02-15T23:53:39Z
ethz.rosetta.versionExported
true
dc.identifier.olduri
http://hdl.handle.net/20.500.11850/367976
dc.identifier.olduri
http://hdl.handle.net/20.500.11850/366509
dc.identifier.olduri
http://hdl.handle.net/20.500.11850/365426
dc.identifier.olduri
http://hdl.handle.net/20.500.11850/367086
dc.identifier.olduri
http://hdl.handle.net/20.500.11850/367673
dc.identifier.olduri
http://hdl.handle.net/20.500.11850/368240
dc.identifier.olduri
http://hdl.handle.net/20.500.11850/366008
dc.identifier.olduri
http://hdl.handle.net/20.500.11850/365010
dc.identifier.olduri
http://hdl.handle.net/20.500.11850/365660
dc.identifier.olduri
http://hdl.handle.net/20.500.11850/364084
ethz.COinS
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