Wind and Temperature Networking Applied to Aircraft Trajectory Prediction

International audienceTrajectory prediction estimates the future position of aircraft along their planned trajectories in order to detect potential conflicts and to optimize air space occupancy. This prediction is a critical task in the Air Traffic Control (ATC) process and has been studied for many years. For the future automation processes developed in the SESAR [19], NextGen [15] and CARATS [3] projects, such trajectory prediction will be even more critical. As there is always a deviation between the predicted wind (from the weather forecasts) and the encountered wind, the main longitudinal (along-track) error source between the predicted and the actual trajectory is linked to wind estimation. Even if the main longitudinal (along-track) error source between the predicted and the actual trajectory is linked to wind estimation, temperature wrong estimation may also lead to ETE1 prediction errors. In a previous paper [11] we measured the potential benefit produced by sharing wind measures between aircraft. In the present paper we will try to improve the trajectory prediction by sharing the wind and the temperature information between aircraft. Addressing the temperature came when we noticed that at least the cruising phase of many flight was performed at constant Mach number. Maintaining a given Mach number under changing temperatures equals changing the true air speed. Based on the current performances of Air Traffic Control systems , controllers are able to efficiently detect conflict 20 minutes in advance ; for a larger time horizon (look-ahead time), the induced trajectory prediction uncertainty strongly reduces the reliability of the conflict detection. The goal of this work is to measure the potential benefit produced by sharing wind/temperature measures between aircraft (this concept will be called Wind/Temp Networking (WTN)). To reach this goal, aircraft measure (temperature and pressure) and calculate (wind and density) their local atmospheric data and broadcast them to the other aircraft. Having such distributed weather information, each aircraft is able to compute an enhanced local wind/temperature map as a function of location (3D) and time. These updated wind/temp fields could be shared with other aircraft and/or with ground systems. Using this enhanced weather information, each aircraft is able to improve drastically its own trajectory prediction. This concept has been simulated in the French airspace with 8 000 flights. Comparisons have been investigated on trajectory prediction performances with and without wind/temp networking. Statistics have been conducted in order to measure the benefit of such concept in both time and space dimensions showing higher improvement in high traffic areas, as expected