Low-level turbulence risk assessment and visualization using temporal rate of change of headwind of an aircraft

• Published in: Journal of big data Vol. 11; no. 1; pp. 158 - 17
• Main Authors: Ito, Koji, Ohba, Haruka, Mizuno, Shinya
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 07-11-2024; Springer Nature B.V; SpringerOpen
• Subjects: Aircraft accidents & safety; Airline; Airlines; Airports; Aviation meteorology; Big Data; Civil aviation; Communications Engineering; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Database Management; Doppler radar; Energy consumption; Flight; Information Storage and Retrieval; Laplace distribution; Low level turbulence; Low-level wind shear; Mathematical Applications in Computer Science; Networks; Parameters; Pilots; QAR; Risk assessment; Safety; Time of use; Visualization; Weather; Wind shear; Wind speed; Laplace distribution; QAR; Low-level wind shear; Safety; Airline
• ISSN: 2196-1115; 2196-1115
• DOI: 10.1186/s40537-024-01032-2

In this study, we focused on the temporal rate of change of headwind, which is one of the recorded parameters in the aircraft’s in-flight quick access recorder data. We selected the Laplace distribution and utilized the scaling parameter to construct a low-level turbulence risk assessment model. Using this model, we calculated the risk of low-level turbulence occurrence at five airports in Japan based on the month, time of day, and wind speed. We visualized how the geographical conditions at each airport influenced risk in relation to airport wind speeds. We developed a low-level turbulence visualization site linked to weather conditions using these results to enable pilots to easily verify low-level turbulence risk and incorporate this information into their flight routines. These findings are anticipated to significantly enhance aircraft safety.