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.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Probabilistic Multimodel Ensemble Wake-Vortex Prediction Employing Bayesian Model Averaging


    Beteiligte:
    Körner, Stephan (Autor:in) / Holzäpfel, Frank (Autor:in) / Sölch, Ingo (Autor:in)

    Erschienen in:

    Journal of Aircraft ; 56 , 2 ; 695-706


    Erscheinungsdatum :

    2019-01-03


    Format / Umfang :

    12 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Probabilistic Multimodel Ensemble Wake-Vortex Prediction employing Bayesian Model Averaging

    Körner, Stephan / Holzäpfel, Frank / Sölch, Ingo | Deutsches Zentrum für Luft- und Raumfahrt (DLR) | 2019

    Freier Zugriff

    Multimodel Ensemble Methods for Prediction of Wake-Vortex Transport and Decay

    Körner, Stephan / Ahmad, Nash’at N. / Holzäpfel, Frank et al. | AIAA | 2017



    Multimodel Ensemble Methods for Prediction of Wake-Vortex Transport and Decay Originating NASA

    Korner, Stephan / Ahmad, Nashat N. / Holzapfel, Frank et al. | NTRS | 2017


    Multi-Model Ensemble Wake Vortex Prediction

    Koerner, Stephan / Ahmad, Nash'at N. / Holzaepfel, Frank et al. | NTRS | 2015