Toward understanding tornado formation through spatiotemporal data mining

Abstract

Tornadoes, which are one of the most feared natural phenomena, present a significant challenge to forecasters who strive to provide adequate warnings of the imminent danger. Forecasters recognize the general environmental conditions within which a tornadic thunderstorm, called a supercell thunderstorm, will form. They also recognize a supercell thunderstorm with its rotating updraft, or mesocyclone, when it appears on radar. However, only a minority of supercell storms produce tornadoes. Although most tornadoes are warned in advance, the majority of the tornado warnings are false alarms. In this chapter, we discuss the development of novel spatiotemporal data mining techniques for discriminating between supercell storms that produce tornadoes and those that do not. To test the novel techniques, we initially applied them to numerical models having coarse 500 meter horizontal grid spacing that did not resolve tornadoes but that did resolve the parent mesocyclones.