A Statistical Investigation of the Dependence of Tropical Cyclone Intensity Change on the Surrounding Environment
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A Statistical Investigation of the Dependence of Tropical Cyclone Intensity Change on the Surrounding Environment

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  • Journal Title:
    Monthly Weather Review
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    A progression of advanced statistical methods is applied to investigate the dependence of the 6-h tropical cyclone (TC) intensity change on various environmental variables, including the recently developed ventilation index (VI). The North Atlantic (NA) and western North Pacific (WNP) observations from 1979 to 2014 are used. As a first step, a model of the intensity change is developed as a linear function of 13 variables used in operational models, obtaining statistical R2 values of 0.26 for NA and 0.3 for WNP. Statistical variable selection techniques are then applied to significantly reduce the number of predictors (to 5–11), while keeping similar R2 values with linear or nonlinear models. Further reduction of the number of predictors (to 5–7) and significant improvement of R2 (0.41–0.53) are obtained with mixture modeling, indicating that the dependence of TC intensification on the environment is nonhomogeneous. Applying VI as the environmental predictor in the mixture modeling gives R2 results (0.41–0.74) similar to or better than those with more environmental variables, confirming that VI is a dominant environmental variable, although its effect on TC intensification is quite heterogeneous. However, the overall predictive R2 results of the mixture models are relatively low (<0.3), as the considered environmental variables have limited predictability for the occurrence of extreme/rapid intensification. Finally, nonparametric regression with six predictors [current intensity, previous intensity change, the three components of VI (maximum potential intensity, shear, and entropy deficit), and 200-hPa zonal wind] performs relatively well with predictive R2 values of 0.37 for NA and 0.36 for WNP. The predictability of these statistical models may be further improved by adding oceanic and inner-core process predictors.
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    Monthly Weather Review, 145(7), 2813-2831
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