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Prediction of Tropical Cyclone Turning and Acceleration Using Empirical Orthogonal Function Representations

James E. PeakDepartment of Meteorology, Naval Postgraduate School, Monterey, CA 93943

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Russell L. ElsberryDepartment of Meteorology, Naval Postgraduate School, Monterey, CA 93943

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Abstract

Prediction of tropical cyclone motion in terms of cross-track (CT) and along-track (AT) components is proposed as an alternative to geographic (zonal and meridional) components. Since the CT and AT components are defined relative to an extrapolated track based on the current and − 12 h wanting positions, the CT and AT components are representative of the important turning motion and apparent speed changes along the track. A discriminant analysis approach is used to determine which of the persistence-type and predictors and empirical orthogonal functions of the geopotential fields are most relevant. Classification functions are derived to predict the future CT and AT tercile group. The scheme correctly selects 45% of the CT and 50% of the AT classifications versus 33% due to random chance.

Based on the results of the discriminant analysis, the sample of cases is stratified into five subgroups in terms of the past 12 h storm heading and speed. Separate regression equations are derived for the subgroups, which are taken to represent different environmental conditions. Those storms moving to the northwest are best predicted by the scheme with mean 72 h forecast errors of 490 and 506 km for the slow- and fast-moving categories, respectively. The weighted mean of the subgroup 72 h track errors is 566 km, which is smaller than the long-term mean JTWC error of 610 km. The ability to predict storms which deviate from their previous course while maintaining this level of forecast skill is a major advantage of these schemes.

Abstract

Prediction of tropical cyclone motion in terms of cross-track (CT) and along-track (AT) components is proposed as an alternative to geographic (zonal and meridional) components. Since the CT and AT components are defined relative to an extrapolated track based on the current and − 12 h wanting positions, the CT and AT components are representative of the important turning motion and apparent speed changes along the track. A discriminant analysis approach is used to determine which of the persistence-type and predictors and empirical orthogonal functions of the geopotential fields are most relevant. Classification functions are derived to predict the future CT and AT tercile group. The scheme correctly selects 45% of the CT and 50% of the AT classifications versus 33% due to random chance.

Based on the results of the discriminant analysis, the sample of cases is stratified into five subgroups in terms of the past 12 h storm heading and speed. Separate regression equations are derived for the subgroups, which are taken to represent different environmental conditions. Those storms moving to the northwest are best predicted by the scheme with mean 72 h forecast errors of 490 and 506 km for the slow- and fast-moving categories, respectively. The weighted mean of the subgroup 72 h track errors is 566 km, which is smaller than the long-term mean JTWC error of 610 km. The ability to predict storms which deviate from their previous course while maintaining this level of forecast skill is a major advantage of these schemes.

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