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Conference Paper: Comparison of squall line positioning methods using radar data

TitleComparison of squall line positioning methods using radar data
Authors
Issue Date2006
PublisherSpringer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/
Citation
Lecture Notes In Computer Science (Including Subseries Lecture Notes In Artificial Intelligence And Lecture Notes In Bioinformatics), 2006, v. 4253 LNAI - III, p. 269-276 How to Cite?
AbstractSquall lines are strong indicators of potential severe weather. Yet, automated positioning and tracking algorithms are not common. We propose three different ways to model and identify squall lines using radar images. The three methods are ellipse fitting, Hough transform, and the use of a genetic algorithm-based framework. They model a squall line as an ellipse, a straight line, and adjoining segments of arc respectively. We compare the advantages and limitations of each method in terms of speed, flexibility, stability and sensitivity to parameter settings. It is found that ellipse fitting is the most efficient, followed by Hough transform. Both methods lack flexibility and stability. The genetic algorithm-based framework is stable, has flexibility in modelling and analysis, but comes with a cost of efficiency. The proposed methods provide independent and objective information sources to assist weather forecast. © Springer-Verlag Berlin Heidelberg 2006.
Persistent Identifierhttp://hdl.handle.net/10722/93091
ISSN
2005 Impact Factor: 0.402
2015 SCImago Journal Rankings: 0.252
References

 

DC FieldValueLanguage
dc.contributor.authorWong, KYen_HK
dc.contributor.authorYip, CLen_HK
dc.date.accessioned2010-09-25T14:50:37Z-
dc.date.available2010-09-25T14:50:37Z-
dc.date.issued2006en_HK
dc.identifier.citationLecture Notes In Computer Science (Including Subseries Lecture Notes In Artificial Intelligence And Lecture Notes In Bioinformatics), 2006, v. 4253 LNAI - III, p. 269-276en_HK
dc.identifier.issn0302-9743en_HK
dc.identifier.urihttp://hdl.handle.net/10722/93091-
dc.description.abstractSquall lines are strong indicators of potential severe weather. Yet, automated positioning and tracking algorithms are not common. We propose three different ways to model and identify squall lines using radar images. The three methods are ellipse fitting, Hough transform, and the use of a genetic algorithm-based framework. They model a squall line as an ellipse, a straight line, and adjoining segments of arc respectively. We compare the advantages and limitations of each method in terms of speed, flexibility, stability and sensitivity to parameter settings. It is found that ellipse fitting is the most efficient, followed by Hough transform. Both methods lack flexibility and stability. The genetic algorithm-based framework is stable, has flexibility in modelling and analysis, but comes with a cost of efficiency. The proposed methods provide independent and objective information sources to assist weather forecast. © Springer-Verlag Berlin Heidelberg 2006.en_HK
dc.languageengen_HK
dc.publisherSpringer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/en_HK
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)en_HK
dc.titleComparison of squall line positioning methods using radar dataen_HK
dc.typeConference_Paperen_HK
dc.identifier.emailWong, KY:kywong@cs.hku.hken_HK
dc.identifier.emailYip, CL:clyip@cs.hku.hken_HK
dc.identifier.authorityWong, KY=rp00187en_HK
dc.identifier.authorityYip, CL=rp00205en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.scopuseid_2-s2.0-33750719689en_HK
dc.identifier.hkuros120784en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-33750719689&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume4253 LNAI - IIIen_HK
dc.identifier.spage269en_HK
dc.identifier.epage276en_HK
dc.publisher.placeGermanyen_HK
dc.identifier.scopusauthoridWong, KY=7404759205en_HK
dc.identifier.scopusauthoridYip, CL=7101665547en_HK

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