File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Automatic identification of weather systems from numerical weather prediction data using genetic algorithm

TitleAutomatic identification of weather systems from numerical weather prediction data using genetic algorithm
Authors
KeywordsGenetic algorithm
Meteorological computing
Numerical weather prediction
Weather system identification and positioning
Weather system modeling
Issue Date2008
PublisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/eswa
Citation
Expert Systems With Applications, 2008, v. 35 n. 1-2, p. 542-555 How to Cite?
AbstractWeather systems such as tropical cyclones, fronts, troughs and ridges affect our daily lives. Yet, they are often manually located and drawn on weather charts based on forecasters' experience. To identify them, multiple atmospheric elements need to be considered, and the results may vary among forecasters. In this paper, we propose an automatic weather system identification method. A generic model of weather systems is designed, along with a genetic algorithm-based framework for finding them automatically from multidimensional numerical weather prediction data. The framework allows multiple weather elements to be analyzed. It is found that our method not only can locate weather systems with 80-100% precision, but can also discover features that could indicate the genesis or dissipation of such systems that forecasters may overlook. The method provides an independent and objective source of information to assist forecasters in identifying and positioning weather systems. © 2007 Elsevier Ltd. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/127359
ISSN
2015 Impact Factor: 2.981
2015 SCImago Journal Rankings: 1.839
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorWong, KYen_HK
dc.contributor.authorYip, CLen_HK
dc.contributor.authorLi, PWen_HK
dc.date.accessioned2010-10-31T13:20:59Z-
dc.date.available2010-10-31T13:20:59Z-
dc.date.issued2008en_HK
dc.identifier.citationExpert Systems With Applications, 2008, v. 35 n. 1-2, p. 542-555en_HK
dc.identifier.issn0957-4174en_HK
dc.identifier.urihttp://hdl.handle.net/10722/127359-
dc.description.abstractWeather systems such as tropical cyclones, fronts, troughs and ridges affect our daily lives. Yet, they are often manually located and drawn on weather charts based on forecasters' experience. To identify them, multiple atmospheric elements need to be considered, and the results may vary among forecasters. In this paper, we propose an automatic weather system identification method. A generic model of weather systems is designed, along with a genetic algorithm-based framework for finding them automatically from multidimensional numerical weather prediction data. The framework allows multiple weather elements to be analyzed. It is found that our method not only can locate weather systems with 80-100% precision, but can also discover features that could indicate the genesis or dissipation of such systems that forecasters may overlook. The method provides an independent and objective source of information to assist forecasters in identifying and positioning weather systems. © 2007 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/eswaen_HK
dc.relation.ispartofExpert Systems with Applicationsen_HK
dc.subjectGenetic algorithmen_HK
dc.subjectMeteorological computingen_HK
dc.subjectNumerical weather predictionen_HK
dc.subjectWeather system identification and positioningen_HK
dc.subjectWeather system modelingen_HK
dc.titleAutomatic identification of weather systems from numerical weather prediction data using genetic algorithmen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0957-4174&volume=35&spage=542&epage=555&date=2008&atitle=Automatic+Identification+of+Weather+Systems+from+Numerical+Weather+Prediction+Data+Using+Genetic+Algorithmen_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.doi10.1016/j.eswa.2007.07.032en_HK
dc.identifier.scopuseid_2-s2.0-44949250089en_HK
dc.identifier.hkuros181687en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-44949250089&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume35en_HK
dc.identifier.issue1-2en_HK
dc.identifier.spage542en_HK
dc.identifier.epage555en_HK
dc.identifier.isiWOS:000257617100055-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridWong, KY=7404759205en_HK
dc.identifier.scopusauthoridYip, CL=7101665547en_HK
dc.identifier.scopusauthoridLi, PW=27171545800en_HK
dc.identifier.citeulike3468174-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats