RUNWAY CONFIGURATION ANALYSIS BASED ON WIND DATA: A CASE STUDY OF WARSAW CHOPIN AIRPORT
Abstract
Runway configuration at airports is determined based on the prevailing wind direction to ensure the safety of flight operations. In this process, wind direction, crosswind component, and wind coverage are of critical importance. In this study, Warsaw Chopin Airport, which has intersecting runways, is examined. Using 12 years of METAR data for the airport, wind speed, wind direction, and wind coverage were analyzed. Finally, monthly wind contour models were generated for the airport, and the relationship between wind and runway configuration was evaluated seasonally.
Based on the wind data from Warsaw Chopin Airport, it was found that the prevailing wind directions are west, northwest, and east-southeast; the average wind direction is 250°, while the maximum and minimum wind directions are 270° and 360°, respectively. Additionally, the wind coverage was calculated as 99.77% with the combined use of the existing intersecting runways, indicating that the current runway configurations are operationally highly sufficient.
Keywords
Aviation, Airport, Meteorology, Wind, Runway Configuration, Transportation
References
- [1] Bellasio R. Analysis of wind data for airport runway design. J. Airl. Airport Manag. 2014; 4(2): 1–15.
- [2] Maslovara A, Mirkovic B. Impact of tailwind on airport capacity and delay at Zurich Airport. Transp. Res. Procedia 2021; 59: 117–126.
- [3] Hernández-Romero E, Valenzuela A, Rivas D. Probabilistic multi-aircraft conflict detection and resolution considering wind forecast uncertainty. Aerosp. Sci. Technol. 2020; 105: 105973.
- [4] Dönmez K, Cetek C, Kaya O. Air traffic management in parallel-point merge systems under wind uncertainties. J. Air Transp. Manag. 2022; 104: 102268.
- [5] Gu Y, Rhudy MB. Stochastic wind modeling and estimation for unmanned aircraft systems. In: AIAA Aviation Forum; 2019.
- [6] Rodionova O, Sridhar B, Ng HK. Conflict resolution for wind-optimal aircraft trajectories in North Atlantic Oceanic Airspace with wind uncertainties. In: IEEE/AIAA Digital Avionics Systems Conference; 2016. pp. 1–10.
- [7] Vela AE, Salaun E, Solak S, Feron E. A two-stage stochastic optimization model for air traffic conflict resolution under wind uncertainty. In: IEEE/AIAA Digital Avionics Systems Conference; 2009. pp. 1–10.
- [8] Saeed A, Li C, Gan Z, Xie Y, Liu F. A simple approach for short-term wind speed interval prediction based on independently recurrent neural networks and error probability distribution. Energy 2022; 238: 122012.
- [9] Chen G, Tang B, Zeng X, Zhou P, Kang P, Long H. et al. Short-term wind speed forecasting based on long short-term memory and improved BP neural network. Int. J. Electr. Power Energy Syst. 2022; 134: 107365.
- [10] Wang Y, Zhang N, Wu L, Wang Y. Wind speed forecasting based on the hybrid ensemble empirical mode decomposition and GA-BP neural network method. Renew. Energy 2016; 94: 629–636.