dc.contributor.author |
Stordal, AS |
en |
dc.contributor.author |
Karlsen, HA |
en |
dc.contributor.author |
Naevdal, G |
en |
dc.contributor.author |
Skaug, HJ |
en |
dc.contributor.author |
Valles, Brice |
en |
dc.date.accessioned |
2012-06-25T21:51:41Z |
en |
dc.date.issued |
2011-03-01 |
en |
dc.identifier.citation |
COMPUTATIONAL GEOSCIENCES 15(2):293-305 01 Mar 2011 |
en |
dc.identifier.issn |
1420-0597 |
en |
dc.identifier.uri |
http://hdl.handle.net/2292/19131 |
en |
dc.description.abstract |
The nonlinear filtering problem occurs in many scientific areas. Sequential Monte Carlo solutions with the correct asymptotic behavior such as particle filters exist, but they are computationally too expensive when working with high-dimensional systems. The ensemble Kalman filter (EnKF) is a more robust method that has shown promising results with a small sample size, but the samples are not guaranteed to come from the true posterior distribution. By approximating the model error with a Gaussian distribution, one may represent the posterior distribution as a sum of Gaussian kernels. The resulting Gaussian mixture filter has the advantage of both a local Kalman type correction and the weighting/resampling step of a particle filter. The Gaussian mixture approximation relies on a bandwidth parameter which often has to be kept quite large in order to avoid a weight collapse in high dimensions. As a result, the Kalman correction is too large to capture highly non-Gaussian posterior distributions. In this paper, we have extended the Gaussian mixture filter (Hoteit et al., Mon Weather Rev 136:317-334, 2008) and also made the connection to particle filters more transparent. In particular, we introduce a tuning parameter for the importance weights. In the last part of the paper, we have performed a simulation experiment with the Lorenz40 model where our method has been compared to the EnKF and a full implementation of a particle filter. The results clearly indicate that the new method has advantages compared to the standard EnKF. |
en |
dc.language |
English |
en |
dc.publisher |
SPRINGER |
en |
dc.relation.ispartofseries |
Computational Geosciences |
en |
dc.rights |
Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. Details obtained from http://www.sherpa.ac.uk/romeo/issn/1420-0597/ |
en |
dc.rights.uri |
https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm |
en |
dc.subject |
Science & Technology |
en |
dc.subject |
Technology |
en |
dc.subject |
Physical Sciences |
en |
dc.subject |
Computer Science, Interdisciplinary Applications |
en |
dc.subject |
Geosciences, Multidisciplinary |
en |
dc.subject |
Computer Science |
en |
dc.subject |
Geology |
en |
dc.subject |
Nonlinear filtering |
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dc.subject |
Data assimilation |
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dc.subject |
Ensemble Kalman filter |
en |
dc.subject |
Particle filters |
en |
dc.subject |
DATA ASSIMILATION |
en |
dc.subject |
SIMULATION |
en |
dc.subject |
MODEL |
en |
dc.title |
Bridging the ensemble Kalman filter and particle filters: the adaptive Gaussian mixture filter |
en |
dc.type |
Journal Article |
en |
dc.identifier.doi |
10.1007/s10596-010-9207-1 |
en |
pubs.issue |
2 |
en |
pubs.begin-page |
293 |
en |
pubs.volume |
15 |
en |
dc.rights.holder |
Copyright: SPRINGER |
en |
pubs.author-url |
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=000288216900006&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=6e41486220adb198d0efde5a3b153e7d |
en |
pubs.end-page |
305 |
en |
dc.rights.accessrights |
http://purl.org/eprint/accessRights/RestrictedAccess |
en |
pubs.subtype |
Article |
en |
pubs.elements-id |
208493 |
en |
pubs.record-created-at-source-date |
2012-06-26 |
en |