Multi-Model Ensemble Wake Vortex Prediction

Purpose Wake vortices that are generated by an aircraft as a consequence of lift constitute a potential danger to the following aircraft. To predict and avoid dangerous situations, wake vortex transport and decay models have been developed. Being based on different model physics, they can complement each other with their individual strengths. This paper investigates the skill of a Multi-Model Ensemble (MME) approach to improve prediction performance. Therefore, this paper aims to use wake vortex models developed by NASA (APA3.2, APA3.4, TDP2.1) and by DLR (P2P). Furthermore, this paper analyzes the possibility to use the ensemble spread to compute uncertainty envelopes. Design/methodology/approach An MME approach called Reliability Ensemble Averaging (REA) is adapted and used to the wake vortex predictions. To train the ensemble, a set of wake vortex measurements accomplished at the airports of Frankfurt (WakeFRA), Munich (WakeMUC) and at a special airport Oberpfaffenhofen was applied. Findings The REA approach can outperform the best member of the ensemble, on average, regarding the root-mean-square error. Moreover, the ensemble delivers reasonable uncertainty envelopes. Practical implications Reliable wake vortex predictions may be applicable for both tactical optimization of aircraft separation at airports and airborne wake vortex prediction and avoidance. Originality/value Ensemble approaches are widely used in weather forecasting, but they have never been applied to wake vortex predictions. Until today, the uncertainty envelopes for wake vortex forecasts have been computed among others from perturbed initial conditions or perturbed physics as well as from uncertainties from environmental conditions or from safety margins but not from the spread of structurally independent model forecasts.

[1]  Fred H. Proctor,et al.  The NASA-Langley Wake Vortex Modelling Effort in Support of an Operational Aircraft Spacing System , 1998 .

[2]  Grégoire Winckelmans,et al.  Fast-Time Modeling of Ground Effects on Wake Vortex Transport and Decay , 2013 .

[3]  Coleman duP. Donaldson,et al.  Vortex Wakes of Conventional Aircraft , 1975 .

[4]  Frank Holzäpfel,et al.  Probabilistic Two-Phase Wake Vortex Decay and Transport Model , 2003 .

[5]  Thierry Poinsot,et al.  Vortex model to define safe aircraft separation distances , 1996 .

[6]  Meiko Steen,et al.  Aircraft Wake-Vortex Evolution in Ground Proximity: Analysis and Parameterization , 2006 .

[7]  Frank Holzäpfel Effects of Environmental and Aircraft Parameters on Wake Vortex Behavior , 2014 .

[8]  Mark B. Sussman A remark concerning engine-inlet distortion. , 1968 .

[9]  Fred H. Proctor Interaction of Aircraft Wakes from Laterally Spaced Aircraft , 2009 .

[10]  Robert E. Robins,et al.  Algorithm for Prediction of Trailing Vortex Evolution , 2001 .

[11]  Michael Frech,et al.  Skill of an Aircraft Wake-Vortex Model Using Weather Prediction and Observation , 2008 .

[12]  F. Giorgi,et al.  Calculation of average, uncertainty range, and reliability of regional climate changes from AOGCM simulations via the reliability ensemble averaging (REA) method , 2002 .

[13]  Earl R. Booth,et al.  Aircraft Wake Vortex Measurements at Denver International Airport , 2004 .

[14]  R. Heinrichs,et al.  Vortex and meteorological measurements at Dallas/Ft. Worth airport , 1999 .

[15]  Adrian E. Raftery,et al.  Bayesian Model Averaging: A Tutorial , 2016 .

[16]  Turgut Sarpkaya,et al.  Wake-Vortex Eddy-Dissipation Model Predictions Compared with Observations , 2000 .

[17]  Donald P. Delisi,et al.  Assessment of Fast-Time Wake Vortex Prediction Models using Pulsed and Continuous Wave Lidar Observations at Several Different Airports , 2011 .

[18]  Donald P. Delisi,et al.  Comparisons of Crosswind Velocity Profile Estimates Used in Fast-Time Wake Vortex Prediction Models , 2011 .

[19]  R. E. Robins,et al.  NWRA AVOSS Wake Vortex Prediction Algorithm. 3.1.1 , 2002 .

[20]  Mark A. Liniger,et al.  Can multi‐model combination really enhance the prediction skill of probabilistic ensemble forecasts? , 2007 .

[21]  Laurent Bricteux,et al.  The WAKE4D simulation platform for predicting aircraft wake vortex transport and decay : Description and examples of application , 2010 .

[22]  Donald P. Delisi,et al.  Comparison of Ensemble Predictions of a New Probabilistic Fast-Time Wake Vortex Model and Lidar Observed Vortex Circulation Intensities and Trajectories , 2011 .

[23]  David A. Hinton,et al.  NASA Wake Vortex Research for Aircraft Spacing , 1997 .

[24]  Laurent Bricteux,et al.  Aircraft wake vortices in stably stratified and weakly turbulent atmospheres: simulation and modeling , 2013 .

[25]  Fred H. Proctor,et al.  An Estimation of Turbulent Kinetic Energy and Energy Dissipation Rate Based on Atmospheric Boundary Layer Similarity Theory , 2000 .

[26]  R. E. Robins,et al.  NWRA AVOSS Wake Vortex Prediction Algorithm , 2013 .

[27]  George F. Switzer,et al.  TASS Driven Algorithms for Wake Prediction , 2006 .

[28]  Fred H. Proctor,et al.  Numerical study of wake vortex decay and descent in homogeneous atmospheric turbulence , 2000 .

[29]  Frank Holzäpfel,et al.  Impact of Wind and Obstacles on Wake Vortex Evolution in Ground Proximity , 2014 .

[30]  Turgut Sarpkaya,et al.  New Model for Vortex Decay in the Atmosphere , 2000 .

[31]  Adrian E. Raftery,et al.  Bayesian model averaging: a tutorial (with comments by M. Clyde, David Draper and E. I. George, and a rejoinder by the authors , 1999 .

[32]  G. C. Greene,et al.  An approximate model of vortex decay in the atmosphere , 1985 .

[33]  Frank Holzäpfel,et al.  Probabilistic Two-Phase Aircraft Wake-Vortex Model: Further Development and Assessment , 2006 .

[34]  F. H. Proctor,et al.  The terminal area simulation system. Volume 1: Theoretical formulation , 1987 .

[35]  Renate Hagedorn,et al.  The rationale behind the success of multi-model ensembles in seasonal forecasting — I. Basic concept , 2005 .

[36]  Frank Holzäpfel,et al.  Multi-Model Ensemble Wake Vortex Prediction - Further Development and Probabilistic Assessment , 2016 .

[37]  Fred H. Proctor,et al.  Evaluation of Fast-Time Wake Vortex Prediction Models , 2009 .

[38]  Donald D. Delisi,et al.  Correlation of the Temporal Variability in the Crosswind and the Observation Lifetime of Vortices Measured with a Pulsed Lidar , 2011 .

[39]  Dennis Vechtel,et al.  Performance of Onboard Wake–Vortex Prediction Systems Employing Various Meteorological Data Sources , 2016 .

[40]  Fred H. Proctor,et al.  Wake Vortex Transport and Decay in Ground Effect: Vortex Linking with the Ground , 2000 .

[41]  Rodney E. Cole,et al.  Aircraft Vortex Spacing System (AVOSS) Initial 1997 System Deployment at Dallas/Ft. Worth (DFW) Airport , 1998 .

[42]  A. Raftery,et al.  Using Bayesian Model Averaging to Calibrate Forecast Ensembles , 2005 .

[43]  Matthew J. Pruis,et al.  NASA AVOSS Fast-Time Wake Prediction Models: User's Guide , 2014 .

[44]  Frank Holzäpfel,et al.  Large-eddy simulation of aircraft wake vortex deformation and topology , 2011 .

[45]  George F. Switzer,et al.  Numerical Study of Wake Vortex Behavior in Turbulent Domains with Ambient Stratification , 2000 .

[46]  Michael Harris,et al.  Comparison of Wake-Vortex Parameters Measured by Pulsed and Continuous-Wave Lidars , 2005 .