MIT Lincoln Laboratory has developed the Route Availability Planning Tool (RAPT), which provides automated convective weather guidance to air traffic managers of the NYC metro region. Prior studies of RAPT have shown high-accuracy guidance from forecast weather, but further refinements to prevent forecast misclassification is still desirable. An attribute set of highly correlated predictors for forecast misclassification is identified. Using this attribute set, a variety of prediction models for forecast misclassification are generated and evaluated. Rule-based models, decision trees, multi-layer perceptrons, and Bayesian prediction model techniques are used. Filtering, resampling, and attribute selection methods are applied to refine model generation. Our results show promising accuracy rates for multi-layer perceptrons trained on full attribute sets.