Abstract
The utility of route guidance and trajectory prediction tools in air traffic management is directly related to how well such tools anticipate pilot and controller reactions to weather. This paper presents a new method for translating weather data into patterns in aggregated aircrafttrajectories.Techniquesaredescribedthatlimitthehumanandcomputationaleffort required to analyze large sets of data and enable formulation and discovery of mathematical relationships among large numbers of weather- and flight plan-related variables. The method is used to examine the effects of thunderstorms on aggregate aircraft operations near Atlanta in the spring and summer of 2007. Measures of precipitation intensity and storm cell height were related to aircraft positions over a period of 40 days.A mathematical model of the relationship between precipitation intensity, storm cell height, flight level, and airspace occupancy was constructed using multivariate adaptive polynomial spline regression. Explanatory power was lost when aircraft altitude and storm cell height readings were combined into a measure of their difference. Precipitation intensity contributed surprisingly little discriminatory power to the built model.Aircraft sought to avoid airspace within 5km of storm activity, rerouting to airspace 10km to 20km and farther from the storm.
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