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

Abstract 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.

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Publication Details

Journal
Journal Of Big Data
Published
2024-11-07
DOI
https://doi.org/10.1186/s40537-024-01032-2
Citations
2
Primary Topic
Wind and Air Flow Studies
Type
article
Field-Weighted Citation Impact
0.41
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article

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

Haruka Ohba, Shinya Mizuno, Koji Ito
2 citations
Journal Of Big Data
Wind and Air Flow Studies
0.41
article

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

Haruka Ohba, Shinya Mizuno, Koji Ito
article en
2 citations

Abstract

Abstract 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.

Journal Of Big DataVol. 11(1)
J. F. Oberlin University (JP), Juntendo University (JP)
Affordable and clean energy
Openalex Percentile: Top 41%
Wind and Air Flow Studies
0.41
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Low-level turbulence risk assessment and visualization using temporal rate of change of headwind of an aircraft — Haruka Ohba, Shinya Mizuno, et al. · Journal Of Big Data (2024) | TGRS Research Map | TGRS