Dynamic prediction of terminal-area severe convective weather penetration

Abstract

Despite groundbreaking technology and revised operating procedures designed to improve the safety of air travel, numerous aviation accidents still occur every year. According to a recent report by the FAA's Aviation Weather Research Program, over 23% of these accidents are weather-related, typically taking place during the takeoff and landing phases. When pilots fly through severe convective weather, regardless of whether or not an accident occurs, they cause damage to the aircraft, increasing maintenance cost for airlines. These concerns, coupled with the growing demand for air transportation, put an enormous amount of pressure on the existing air traffic control system. Moreover, the degree to which weather impacts airspace capacity, defined as the number of aircraft that can simultaneously y within the terminal area, is not well understood. Understanding how weather impacts terminal area air traffic flows will be important for quantifying the effect that uncertainty in weather forecasting has on flows, and developing an optimal strategy to mitigate this effect. In this thesis, we formulate semi-dynamic models and employ Multinomial Logistic Regression, Classification and Regression Trees (CART), and Random Forests to accurately predict the severity of convective weather penetration by flights in several U.S. airport terminal areas. Our models perform consistently well when re-trained on each individual airport rather than using common models across airports. Random Forests achieve the lowest prediction error with accuracies as high as 99%, false negative rates as low as 1%, and false positive rates as low as 3%. CART is the least sensitive to differences across airports, exhibiting very steady performance. We also identify weather-based features, particularly those describing the presence of fast-moving, severe convective weather within the projected trajectory of the flight, as the best predictors of future penetration.