Abstract
An experimental weather classification, analysis, and nowcasting system, based upon a combination of artificial intelligence techniques and conventional numerical modeling, and designed for use as a real-time range/field forecasting aid, is described. In particular, a computer-based prototype coupled knowledge-based system, called PROCANS (Prototype Coupled Analysis and Nowcasting System), tailored for applications at the U.S. Army field testing range at Fort Hunter Liggett, California, is used as an example to demonstrate and evaluate the overall concept. The components of the system are: 1) a rule-based meteorological scenario evaluator for analysis and classification of weather scenarios, 2) a nowcaster that uses four analogical and rule-based expert subsystems for nowcasting radiation fog, wind gustiness, thunderstorms, and precipitation, 3) a numerical transport and diffusion module based upon either Gaussian or Monte Carlo particle-trajectory models to simulate airflow and diffusion patterns, and 4) a master database for storing information for possible retrieval, comparing current weather scenarios with past scenarios for possible matching and for analogical and conceptual reasoning to aid future predictions. A preliminary evaluation of PROCANS shows that coupled knowledge-based systems have potential as an integrated local analysis and prediction tool or forecaster's aid for field operations such as smoke screening.
