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Article: An ensemble Kalman filter for atmospheric data assimilation: Application to wind tunnel data

TitleAn ensemble Kalman filter for atmospheric data assimilation: Application to wind tunnel data
Authors
KeywordsData assimilation
Dispersion model
Ensemble Kalman filter
Wind tunnel
Issue Date2010
PublisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/atmosenv
Citation
Atmospheric Environment, 2010, v. 44 n. 13, p. 1699-1705 How to Cite?
AbstractIn the previous work (Zheng et al., 2007, 2009), a data assimilation method, based on ensemble Kalman filter, has been applied to a Monte Carlo Dispersion Model (MCDM). The results were encouraging when the method was tested by the twin experiment and a short-range field experiment. In this technical note, the measured data collected in a wind tunnel experiment have been assimilated into the Monte Carlo dispersion model. The uncertain parameters in the dispersion model, including source term, release height, turbulence intensity and wind direction have been considered. The 3D parameters, i.e. the turbulence intensity and wind direction, have been perturbed by 3D random fields. In order to find the factors which may influence the assimilation results, eight tests with different specifications were carried out. Two strategies of constructing the 3D perturbation field of wind direction were proposed, and the result shows that the two level strategy performs better than the one level strategy. It is also found that proper standard deviation and the correlation radius of the perturbation field play an important role for the data assimilation results. © 2010 Elsevier Ltd. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/125287
ISSN
2015 Impact Factor: 3.459
2015 SCImago Journal Rankings: 1.999
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorZheng, DQen_HK
dc.contributor.authorLeung, JKCen_HK
dc.contributor.authorLee, BYen_HK
dc.date.accessioned2010-10-31T11:22:22Z-
dc.date.available2010-10-31T11:22:22Z-
dc.date.issued2010en_HK
dc.identifier.citationAtmospheric Environment, 2010, v. 44 n. 13, p. 1699-1705en_HK
dc.identifier.issn1352-2310en_HK
dc.identifier.urihttp://hdl.handle.net/10722/125287-
dc.description.abstractIn the previous work (Zheng et al., 2007, 2009), a data assimilation method, based on ensemble Kalman filter, has been applied to a Monte Carlo Dispersion Model (MCDM). The results were encouraging when the method was tested by the twin experiment and a short-range field experiment. In this technical note, the measured data collected in a wind tunnel experiment have been assimilated into the Monte Carlo dispersion model. The uncertain parameters in the dispersion model, including source term, release height, turbulence intensity and wind direction have been considered. The 3D parameters, i.e. the turbulence intensity and wind direction, have been perturbed by 3D random fields. In order to find the factors which may influence the assimilation results, eight tests with different specifications were carried out. Two strategies of constructing the 3D perturbation field of wind direction were proposed, and the result shows that the two level strategy performs better than the one level strategy. It is also found that proper standard deviation and the correlation radius of the perturbation field play an important role for the data assimilation results. © 2010 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.subjectData assimilationen_HK
dc.subjectDispersion modelen_HK
dc.subjectEnsemble Kalman filteren_HK
dc.subjectWind tunnelen_HK
dc.titleAn ensemble Kalman filter for atmospheric data assimilation: Application to wind tunnel dataen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1352-2310&volume=44&issue=13&spage=1699&epage=1705&date=2010&atitle=An+ensemble+Kalman+filter+for+atmospheric+data+assimilation:+application+to+wind+tunnel+data-
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.2010.01.020en_HK
dc.identifier.scopuseid_2-s2.0-77949571681en_HK
dc.identifier.hkuros175098en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-77949571681&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume44en_HK
dc.identifier.issue13en_HK
dc.identifier.spage1699en_HK
dc.identifier.epage1705en_HK
dc.identifier.eissn1873-2844-
dc.identifier.isiWOS:000277162200013-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridZheng, DQ=15849854400en_HK
dc.identifier.scopusauthoridLeung, JKC=24080627200en_HK
dc.identifier.scopusauthoridLee, BY=15848940000en_HK
dc.identifier.citeulike6868620-

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