File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Online update of model state and parameters of a Monte Carlo atmospheric dispersion model by using ensemble Kalman filter

TitleOnline update of model state and parameters of a Monte Carlo atmospheric dispersion model by using ensemble Kalman filter
Authors
KeywordsAtmospheric dispersion model
Data assimilation
Ensemble Kalman Filter
Parameter estimation
Issue Date2009
PublisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/atmosenv
Citation
Atmospheric Environment, 2009, v. 43 n. 12, p. 2005-2011 How to Cite?
AbstractFor an atmospheric dispersion model designed for the assessment of nuclear accident consequences, some uncertain model parameters, such as source term and weather conditions, may influence the reliability of model predictions. In this respect, good estimations of both model state and uncertain parameters are required. In this paper, an ensemble Kalman filter (EnKF) based method for simultaneous state and parameter estimation, using off-site radiation monitoring data, is presented. This method is based on a stochastic state space model, which resembles the parameter errors with stochastic quantities. Three imperfect parameters, including the source release rate, wind direction and turbulence intensity were perturbed simultaneously, and multiple parameter estimation were performed. Having been tested against both simulated and real radiation monitoring data, the method was found to be able to realistically reconstruct the real scene of dispersion, as well as the uncertain parameters. The estimated parameters given by EnKF nicely converge to the true values, and the method also tracks the temporal variation of those parameters. © 2009 Elsevier Ltd. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/59599
ISSN
2015 Impact Factor: 3.459
2015 SCImago Journal Rankings: 1.999
ISI Accession Number ID
Funding AgencyGrant Number
Computer Center of The University of Hong Kong
Funding Information:

The authors would like to thank Dr. B. Lauritzen of Riso National Laboratory, Denmark, for providing the valuable data set of the 41Ar atmospheric dispersion experiment carried out at the BR1 research reactor in Mol, Belgium. The data made it possible to test out data assimilation method. We are grateful to Dr H.Y.Lam of the Hong Kong Observatory for his beneficial suggestion. We would also like to thank Mr. W.K.Kwan, the Computer Center of The University of Hong Kong, for the support on the parallel computation facilities.

References

 

DC FieldValueLanguage
dc.contributor.authorZheng, DQen_HK
dc.contributor.authorLeung, JKCen_HK
dc.contributor.authorLee, BYen_HK
dc.date.accessioned2010-05-31T03:53:33Z-
dc.date.available2010-05-31T03:53:33Z-
dc.date.issued2009en_HK
dc.identifier.citationAtmospheric Environment, 2009, v. 43 n. 12, p. 2005-2011en_HK
dc.identifier.issn1352-2310en_HK
dc.identifier.urihttp://hdl.handle.net/10722/59599-
dc.description.abstractFor an atmospheric dispersion model designed for the assessment of nuclear accident consequences, some uncertain model parameters, such as source term and weather conditions, may influence the reliability of model predictions. In this respect, good estimations of both model state and uncertain parameters are required. In this paper, an ensemble Kalman filter (EnKF) based method for simultaneous state and parameter estimation, using off-site radiation monitoring data, is presented. This method is based on a stochastic state space model, which resembles the parameter errors with stochastic quantities. Three imperfect parameters, including the source release rate, wind direction and turbulence intensity were perturbed simultaneously, and multiple parameter estimation were performed. Having been tested against both simulated and real radiation monitoring data, the method was found to be able to realistically reconstruct the real scene of dispersion, as well as the uncertain parameters. The estimated parameters given by EnKF nicely converge to the true values, and the method also tracks the temporal variation of those parameters. © 2009 Elsevier Ltd. All rights reserved.en_HK
dc.languageengen_HK
dc.publisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/atmosenven_HK
dc.relation.ispartofAtmospheric Environmenten_HK
dc.subjectAtmospheric dispersion modelen_HK
dc.subjectData assimilationen_HK
dc.subjectEnsemble Kalman Filteren_HK
dc.subjectParameter estimationen_HK
dc.titleOnline update of model state and parameters of a Monte Carlo atmospheric dispersion model by using ensemble Kalman filteren_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1352-2310&volume=43&spage=2005&epage=2011&date=2009&atitle=Online+update+of+model+state+and+parameters+of+a+Monte+Carlo+atmospheric+dispersion+model+by+using+ensemble+Kalman+filteren_HK
dc.identifier.emailLeung, JKC: jkcleung@hku.hken_HK
dc.identifier.authorityLeung, JKC=rp00732en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.atmosenv.2009.01.014en_HK
dc.identifier.scopuseid_2-s2.0-61649091769en_HK
dc.identifier.hkuros162263en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-61649091769&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume43en_HK
dc.identifier.issue12en_HK
dc.identifier.spage2005en_HK
dc.identifier.epage2011en_HK
dc.identifier.eissn1873-2844-
dc.identifier.isiWOS:000264999900008-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridZheng, DQ=15849854400en_HK
dc.identifier.scopusauthoridLeung, JKC=24080627200en_HK
dc.identifier.scopusauthoridLee, BY=15848940000en_HK

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats