Data Mining Storm Attributes from Spatial Grids

Abstract

A technique to identify storms and capture scalar features within the geographic and temporal extent of the identified storms is described. The identification technique relies on clustering grid points in an observation field to find self-similar and spatially coherent clusters that meet the traditional understanding of what storms are. From these storms, geometric, spatial, and temporal features can be extracted. These scalar features can then be data mined to answer many types of research questions in an objective, data-driven manner. This is illustrated by using the technique to answer questions of forecaster skill and lightning predictability.