Robust flight planning impact assessment considering convective phenomena. (February 2021)

• Record Type: Journal Article
• Authors: García-Heras, Javier; Soler, Manuel; González-Arribas, Daniel; Eschbacher, Kurt; Rokitansky, Carl-Herbert; Sacher, Daniel; Gelhardt, Ulrike; Lang, Jürgen; Hauf, Thomas; Simarro, Juan; Valenzuela, Alfonso; Franco, Antonio; Rivas, Damián
• Is Part Of: Transportation research. Volume 123(2021)
• Publication Date: 2021-02
• Subjects: Robust flight-planning -- Convective weather -- Simulation; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2021.102968
• Languages: English
• ISSNs: 0968-090X

Abstract

Highlights: Generalization of the robust flight planning method with uncertain weather. Evaluation of the resulting robust trajectories using the simulator environment. Quantification the impact of robust flight planning in safety and efficiency terms. Abstract: Thunderstorms are one of the leading causes of Air Traffic Management delays. In this paper, we assess how incorporating convective information into flight planning algorithms can lead to reductions in reroutings due to storm encounters during the execution of the flight. We use robust open-loop optimal control methodology at the flight planning level and incorporate meteorological uncertainties based on Ensemble Prediction System forecasts. Convective risk areas can be derived from the latter to be included in the objective function. At the execution level, the planned trajectories are included in an air traffic simulator (NAVSIM) under observed weather (wind and storms). In this simulation process, track modifications might be triggered in case of encountering an observed thunderstorm. A tool termed DIVMET based on pathfinding algorithms has been integrated into NAVSIM is considered to that end. Results show that planning robust trajectories (avoiding thus convective areas) reduces the number of storms encounters and increases predictability. This increase in predictability is at a cost in terms of fuel and time, also quantified.