Probabilistic Multimodel Ensemble Wake-Vortex Prediction Employing Bayesian Model Averaging

Abstract

This paper investigates the capability of the multimodel ensemble approach to improve the deterministic forecast of wake-vortex behavior and to produce reliable probabilistic vortex habitation areas. Therefore, the deterministic two-phase wake vortex model D2P, the aircraft vortex spacing system prediction algorithm (APA) 3.2, APA 3.4, APA 3.8, and terminal area simulation system driven algorithms for wake prediction (TDP 2.1) wake-vortex models are exchanged within the framework of a NASA/DLR cooperation. These models are fused by the Bayesian model averaging approach, which is extended by temporally increasing uncertainties. In addition, combined confidence areas for the vertical and lateral vortex positions are derived from bivariate probability density distributions that are delivered by the ensemble and allow the computation of well-defined probability levels. For ensemble training and evaluation data collected at wake-vortex campaigns accomplished by NASA (at Memphis, Dallas, and Denver airports) and DLR (at Frankfurt, Munich, and Oberpfaffenhofen airports) are employed. Various training strategies are considered to obtain optimal prediction skill. The results demonstrate that a thoughtfully trained ensemble improves the deterministic prediction skill by up to 4.3% and is capable of predicting vortex habitation areas featuring reliable probability levels.