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Tropical cyclone track forecasting techniques: A review
Linköping University, Department of Computer and Information Science. Linköping University, The Institute of Technology.ORCID iD: 0000-0002-3997-1088
Linköping University, Department of Computer and Information Science, MDALAB - Human Computer Interfaces. Linköping University, The Institute of Technology.ORCID iD: 0000-0003-2801-7050
2012 (English)In: Atmospheric research, ISSN 0169-8095, Vol. 104-105, 40-69 p.Article, review/survey (Refereed) Published
Abstract [en]

Delivering accurate cyclone forecasts in time is of key importance when it comes to saving human lives and reducing economic loss. Difficulties arise because the geographical and climatological characteristics of the various cyclone formation basins are not similar, which entail that a single forecasting technique cannot yield reliable performance in all ocean basins. For this reason, global forecasting techniques need to be applied together with basin-specific techniques to increase the forecast accuracy. As cyclone track is governed by a range of factors variations in weather conditions, wind pressure, sea surface temperature, air temperature, ocean currents, and the earths rotational force-the coriolis force, it is a formidable task to combine these parameters and produce reliable and accurate forecasts. In recent years, the availability of suitable data has increased and more advanced forecasting techniques have been developed, in addition to old techniques having been modified. In particular, artificial neural network based techniques are now being considered at meteorological offices. This new technique uses freely available satellite images as input, can be run on standard PCs, and can produce forecasts with good accuracy. For these reasons, artificial neural network based techniques seem especially suited for developing countries which have limited capacity to forecast cyclones and where human casualties are the highest. © 2011 Elsevier B.V.

Place, publisher, year, edition, pages
Elsevier , 2012. Vol. 104-105, 40-69 p.
Keyword [en]
Artificial neural networks; Cyclone forecasting models; Cyclone forecasting techniques; Cyclone track forecasting; Hurricane; Typhoon
National Category
Engineering and Technology
URN: urn:nbn:se:liu:diva-74115DOI: 10.1016/j.atmosres.2011.09.012OAI: diva2:480546
Available from: 2012-01-19 Created: 2012-01-19 Last updated: 2014-12-01Bibliographically approved

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Roy, ChandanKovordanyi, Rita
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