Non-empirical extraction of typhoons by geometric data mining

Abstract

Toward developing efficient algorithms of geometric data mining, we extract extreme phenomena with strong vortex of wind such as tropical cyclone (TC) from a meteorological database. The database consists of observational values of some attributes, including wind vectors, at altitudes of certain atmospheric pressure (17 levels) over circa 10,000 grid points on the earth. Conventional method regards a grid point which fulfills some empirical conditions defined for a part of the pressure levels as the center of a typhoon. So, it cannot detect extreme phenomenon with strong wind in another level than the ones for which the empirical conditions are defined. Moreover, because the conventional method checks all grid points, its computation costs enormously. In this study, we propose finding first centers of vortex by moving from random initial positions along streamlines. Next, we calculate intensity and both horizontal and vertical ranges of influence around each center of vortex, then, rank them in order of risk. Comparison experiments showed that the proposed method found and ranked all typhoons extracted by the conventional method as at high risk. Also, the proposed method detected some risky and possibly risky vortices which the conventional method could not find.