Previous studies of model performance at varying resolutions have focused on winter storms or isolated convective events. Little attention has been given to the static high pressure situations that may lead to severe wildfire outbreaks. This study focuses on such an event so as to evaluate the value of increased model resolution for prediction of fire danger. The results are intended to lay the groundwork for using the fifth-generation Pennsylvania State University-National Center for Atmospheric Research Mesoscale Model (MM5) as input to the National Fire Danger Rating System to provide gridded predictions of fire danger indices. Predicted weather parameters were derived from MM5 and evaluated at three different resolutions (36, 12, and 4 km). Model output was compared with observations during the 2000 fire season in western Montana and northern Idaho to help to determine the model's skill in predicting fire danger. For application in fire danger rating, little significant improvement was found in skill with increased model resolution using standard forecast verification techniques. Diurnal bias of modeled temperature and relative humidity resulted in errors larger than the differences between resolutions. Significant timing and magnitude errors at all resolutions could jeopardize accurate prediction of fire danger.

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Weather predictions from the MM5 mesoscale model were used to compute gridded predictions of National Fire Danger Rating System (NFDRS) indexes. The model output was applied to a case study of the 2000 fire season in Northern Idaho and Western Montana to simulate an extreme event. To determine the preferred resolution for automating NFDRS predictions, model performance was evaluated at 36, 12, and 4 km. For those indexes evaluated, the best results were consistently obtained for the 4-km domain, whereas the 36-km domain had the largest mean absolute errors. Although model predictions of fire danger indexes are consistently lower than observed, analysis of time series results indicates that the model does well in capturing trends and extreme changes in NFDRS indexes.