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Importance of Weather Conditions in a Flight Corridor

Author

Listed:
  • Gong Chen

    (Chair of Econometrics and Statistics esp. Transportation, Dresden University of Technology, 01187 Dresden, Germany)

  • Hartmut Fricke

    (Chair of Air Transport Technology and Logistics, Institute of Logistics and Aviation, Dresden University of Technology, 01062 Dresden, Germany)

  • Ostap Okhrin

    (Chair of Econometrics and Statistics esp. Transportation, Dresden University of Technology, 01187 Dresden, Germany)

  • Judith Rosenow

    (Chair of Air Transport Technology and Logistics, Institute of Logistics and Aviation, Dresden University of Technology, 01062 Dresden, Germany)

Abstract

Current research initiatives, such as the Single European Sky Air Traffic Management Research Program, call for an air traffic system with improved safety and efficiency records and environmental compatibility. The resulting multi-criteria system optimization and individual flight trajectories require, in particular, reliable three-dimensional meteorological information. The Global (Weather) Forecast System only provides data at a resolution of around 100 km. We postulate a reliable interpolation at high resolution to compute these trajectories accurately and in due time to comply with operational requirements. We investigate different interpolation methods for aerodynamic crucial weather variables such as temperature, wind speed, and wind direction. These methods, including Ordinary Kriging, the radial basis function method, neural networks, and decision trees, are compared concerning cross-validation interpolation errors. We show that using the interpolated data in a flight performance model emphasizes the effect of weather data accuracy on trajectory optimization. Considering a trajectory from Prague to Tunis, a Monte Carlo simulation is applied to examine the effect of errors on input (GFS data) and output (i.e., Ordinary Kriging) on the optimized trajectory.

Suggested Citation

  • Gong Chen & Hartmut Fricke & Ostap Okhrin & Judith Rosenow, 2022. "Importance of Weather Conditions in a Flight Corridor," Stats, MDPI, vol. 5(1), pages 1-27, March.
  • Handle: RePEc:gam:jstats:v:5:y:2022:i:1:p:18-338:d:767735
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    References listed on IDEAS

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    1. W. Breymann & A. Dias & P. Embrechts, 2003. "Dependence structures for multivariate high-frequency data in finance," Quantitative Finance, Taylor & Francis Journals, vol. 3(1), pages 1-14.
    2. Wanling Huang & Artem Prokhorov, 2014. "A Goodness-of-fit Test for Copulas," Econometric Reviews, Taylor & Francis Journals, vol. 33(7), pages 751-771, October.
    3. Judith Rosenow & Martin Lindner & Joachim Scheiderer, 2021. "Advanced Flight Planning and the Benefit of In-Flight Aircraft Trajectory Optimization," Sustainability, MDPI, vol. 13(3), pages 1-19, January.
    4. Christian Genest & Jean‐François Quessy & Bruno Rémillard, 2006. "Goodness‐of‐fit Procedures for Copula Models Based on the Probability Integral Transformation," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 33(2), pages 337-366, June.
    5. Zhang, Shulin & Okhrin, Ostap & Zhou, Qian M. & Song, Peter X.-K., 2016. "Goodness-of-fit test for specification of semiparametric copula dependence models," Journal of Econometrics, Elsevier, vol. 193(1), pages 215-233.
    6. Scaillet, Olivier, 2007. "Kernel-based goodness-of-fit tests for copulas with fixed smoothing parameters," Journal of Multivariate Analysis, Elsevier, vol. 98(3), pages 533-543, March.
    7. Genest, Christian & Rémillard, Bruno & Beaudoin, David, 2009. "Goodness-of-fit tests for copulas: A review and a power study," Insurance: Mathematics and Economics, Elsevier, vol. 44(2), pages 199-213, April.
    8. J Emmanuel Johnson & Valero Laparra & Adrián Pérez-Suay & Miguel D Mahecha & Gustau Camps-Valls, 2020. "Kernel methods and their derivatives: Concept and perspectives for the earth system sciences," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-30, October.
    9. Nash, John C. & Varadhan, Ravi, 2011. "Unifying Optimization Algorithms to Aid Software System Users: optimx for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 43(i09).
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