@inproceedings{9cab63d0275141ed87168d58391b2b1c,
title = "Stochastic Wind Speed Modeling and Prediction using Historical Wind Data for Aircraft Applications",
abstract = "Information about the local wind speed is critical for aircraft applications. Though various methods have been developed for estimating the local wind speed experienced by an aircraft, these methods often require a stochastic model of the wind behavior in order to accurately predict the variations in the wind. While existing work has explored the application of these wind models, information regarding how to determine the parameters of stochastic wind models is limited. This work explores the connections between wind distribution modeling and stochastic wind modeling to bridge the gap between these differing frameworks. Specifically, a method for using historical wind data to calculate the parameters of a stochastic wind model is proposed and validated with respect to real measurement data. The proposed approach is shown to accurately model the measured wind speed using a combination of multiple random variables.",
author = "Matthew Rhudy and Mark Longenberger",
note = "Publisher Copyright: {\textcopyright} 2024, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.; AIAA Aviation Forum and ASCEND, 2024 ; Conference date: 29-07-2024 Through 02-08-2024",
year = "2024",
doi = "10.2514/6.2024-3849",
language = "English (US)",
isbn = "9781624107160",
series = "AIAA Aviation Forum and ASCEND, 2024",
publisher = "American Institute of Aeronautics and Astronautics Inc, AIAA",
booktitle = "AIAA Aviation Forum and ASCEND, 2024",
}