Performance of an Adaptive Trajectory Prediction Algorithm for Climbing Aircraft

Abstract

Aircraft climb trajectories are difficult to predict, and large errors in these predictions reduce the potential operational benefits of some advanced concepts in the Next Generation Air Transportation System. An algorithm that dynamically adjusts modeled aircraft weights based on observed track data and predictions of atmospheric conditions improved trajectory prediction accuracy for climbing flights across the National Airspace System. Overall, the algorithm reduced both altitude and top-of-climb time prediction root mean square errors by about 20 percent. Although the algorithm improved climb trajectory prediction accuracy in all Centers, results indicate that additional gains may be possible by tuning the algorithm’s parameters on a per-Center basis. Miami, Fort Worth, and Houston Centers were investigated more thoroughly because they represented the lower, middle, and upper end of the algorithm’s performance range. The degree of improvement at the Center level was dependent on the aircraft types.