Use of Convective Indices to Improve the Prediction of Departure Delays

Severe convective weather disrupts European aviation, causing flight deviations and delays. This study addresses the challenge of improving long-term flight predictability, beyond two hours, focussing on departure delays. It explores the potential of convective indices, derived from atmospheric data, as proxies for departure delays. Despite limitations, these indices are appealing due to their simplicity and widespread availability in medium-range weather forecasts. The research collects historical flight data from Europe and correlates departure delays with convective indices. Deterministic and probabilistic prediction models are developed, evaluating their performance against baseline flight plan predictions. The results reveal that using convective indices significantly enhances the prediction of departure delay, particularly in probabilistic models. Lifted, Boyden, and Bradbury indices show promise. Future work includes multi-index predictors, airport-specific indices, machine learning techniques, and the extension of this approach to other flight deviations.