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The Causes And Prediction Models Of Anomalous Tropical Cyclone Activity Affecting Fujian Province

Posted on:2006-01-02Degree:MasterType:Thesis
Country:ChinaCandidate:J Y GaoFull Text:PDF
GTID:2120360152996456Subject:Journal of Atmospheric Sciences
Abstract/Summary:PDF Full Text Request
Temporal variation of tropical cyclone (TC) affecting Fujian for the period 1884-2003 and the corresponding atmosphere circulation and sea surface temperature of Pacific Ocean are investigated by various statistics methods. Prediction models based on periodic function and a new exploring datum analysis method- projection pursuit are applied to forecast year- and season- frequency of TC in Fujian .The major conclusions of this study are summarized as:In recent century, TC affecting Fujian experienced three less-TC periods and two more-TC periods. 1902,1931,1955 and 1971 are change points of TC year-frequency, and 1971 is an abrupt point. There are quasi-13-year medium period vibration and quasi-4 and quasi-2.5-year short period vibration in TC frequency time serials, among which quasi-4 year period is the most significance.Features of atmospheric circulation and sea surface temperature for anomalous more (little) TC in Fujian are as follow: at 500hPa, westerly trough is northward (southward) with north edge of subtropical high northward(southward) and its ridge line northward(southward);in sea surface temperature field, there is a zonal negative(positive) anomaly in equatorial central and eastern Pacific and positive(negative) one in original region of the North West Pacific Ocean TC and the northwest Pacific Ocean.The Mean Generation Function (MGF) is selected by orthogonalization scheme, and the MGF model (MGFM) can fit and forecast Fujian TC year-frequency very well. Generally, the model based on projection pursuit regression (PPRM) is much better than stepwise regression model (SRM) in fitting and forecasting, especially for very anomalous years. This is due to the...
Keywords/Search Tags:Fujian province, tropical cyclone activity, mechanism, prediction model
PDF Full Text Request
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