METEOR: An Artificial Intelligence System for Convective Storm Forecasting

Abstract

Abstract An experienced forecaster can use several different types of knowledge in forcing. First, there is his theoretical understanding of meteorology, which is well entrenched in current numerical models. A second type is his “local knowledge,” gained over years of experience, of how weather is likely to form in his forecast area. This kind of local familiarity is not easily captured with traditional numeric techniques, but might provide additional insights for prediction that someone unfamiliar with the area might not have. A third type of knowledge is how to interpret forecast tools already in use. This might include knowledge of the tool's limitations and how it works in a particular locale. Capturing these types of knowledge is important in building computing systems that can serve as intelligent consultants to forecasters. This paper describes a prototype system, called METEOR, that incorporates all these types of knowledge to predict the location, severity, and motion of convective storms in Albe...