9C.2 Data Mining Techniques for the Studies of Tropical Cyclone Intensity Changes

Wednesday, 12 May 2010: 10:30 AM
Arizona Ballroom 10-12 (JW MArriott Starr Pass Resort)
Ruixin Yang, George Mason University, Fairfax, VA; and J. Tang

Data mining techniques are applied to the studies of intensity changes of tropical cyclones (TCs). The NHC best track data and SHIPS databases are used for identifying intensifying and weakening condition combinations for stratified TCs. The mining results show that a faster northwards storm motion (meridional component of storm motion or MERY) is favored for intensifying tropical storms than for intensifying hurricanes. Intensifying tropical storms incline to have a higher convergence in the upper atmosphere (200 hPa relative eddy momentum flux convergence or REFC) than weakening tropical storms, while intensifying hurricanes prefer to have lower REFC values. Compared to classical statistical analysis methods, data mining is an exploratory data analysis method and gives a comprehensive and exhaustive association relationship among the given multiple conditions. Data mining techniques, therefore, will not only shed light on the roles of multiple associated physical processes in tropical cyclone development but also help to improve the TC intensity forecasting. An outline is sketched on how to use data mining techniques and how to overcome the low occurrences of mined conditions to improve the TC intensity forecasting skills.
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