Pasque 255

Tornadoes are one of the nature’s most violent windstorms that can occur all over the world except Antarctica. Previous scientific efforts were spent on studying this nature hazard from facets such as: genesis, dynamics, detection, forecasting, warning, measuring, and assessing. While we want to model the tornado datasets by using modern sophisticated statistical and computational techniques. The goal of the paper is developing novel finite mixture models and performing clustering analysis on the spatial-temporal and intensity patterns of the tornadoes. To analyze the tornado dataset, we firstly try a Gaussian distribution with the mean vector and variance-covariance matrix represented as exponential functions of intensity and time. Then, a Gaussian mixture model is employed, with mean vector and variance-covariance represented as exponential functions of intensity and time. Thirdly, manly transform parameters are added to the Gaussian mixture model to take care of the potential skewness in the tornado dataset. Results are obtained by computer algorithms. we provide a summary of insights about tornado forecasting and assessing.