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dc.contributor.author
Robert, Sylvain
dc.contributor.author
Künsch, Hans R.
dc.date.accessioned
2019-10-30T15:29:28Z
dc.date.available
2017-06-12T20:25:33Z
dc.date.available
2017-09-06T13:49:11Z
dc.date.available
2017-08-31T12:55:09Z
dc.date.available
2017-09-06T13:44:48Z
dc.date.available
2019-10-30T15:29:28Z
dc.date.issued
2017-03-08
dc.identifier.issn
0280-6495
dc.identifier.issn
1600-0870
dc.identifier.other
10.1080/16000870.2017.1282016
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/183527
dc.identifier.doi
10.3929/ethz-b-000183527
dc.description.abstract
Ensemble methods such as the Ensemble Kalman Filter (EnKF) are widely used for data assimilation in large-scale geophysical applications, as for example in numerical weather prediction. There is a growing interest for physical models with higher and higher resolution, which brings new challenges for data assimilation techniques because of the presence of non-linear and non-Gaussian features that are not adequately treated by the EnKF. We propose two new localized algorithms based on the Ensemble Kalman Particle Filter, a hybrid method combining the EnKF and the Particle Filter (PF) in a way that maintains scalability and sample diversity. Localization is a key element of the success of EnKF in practice, but it is much more challenging to apply to PFs. The algorithms that we introduce in the present paper provide a compromise between the EnKF and the PF while avoiding some of the problems of localization for pure PFs. Numerical experiments with a simplified model of cumulus convection based on a modified shallow water equation show that the proposed algorithms perform better than the local EnKF. In particular, the PF nature of the method allows to capture non-Gaussian characteristics of the estimated fields such as the location of wet and dry areas.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Taylor & Francis
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.subject
Data assimilation
en_US
dc.subject
Ensemble kalman filter
en_US
dc.subject
Localization
en_US
dc.subject
Non-linear filtering
en_US
dc.subject
Particle filter
en_US
dc.title
Localizing the Ensemble Kalman Particle Filter
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
dc.date.published
2017-03-08
ethz.journal.title
Tellus A: Dynamic Meteorology and Oceanography
ethz.journal.volume
69
en_US
ethz.journal.issue
1
en_US
ethz.journal.abbreviated
Tellus, Ser. A, Dyn. meteorol. oceanogr.
ethz.pages.start
1282016
en_US
ethz.size
14 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.identifier.nebis
010839219
ethz.publication.place
Abingdon
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.relation.isCitedBy
10.3929/ethz-b-000184084
ethz.date.deposited
2017-06-12T20:26:34Z
ethz.source
ECIT
ethz.source
FORM
ethz.identifier.importid
imp593655592b65543474
ethz.ecitpid
pub:192643
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2017-09-07T09:53:50Z
ethz.rosetta.lastUpdated
2021-02-15T06:28:44Z
ethz.rosetta.versionExported
true
dc.identifier.olduri
http://hdl.handle.net/20.500.11850/182361
dc.identifier.olduri
http://hdl.handle.net/20.500.11850/129659
ethz.COinS
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