Predictive Modeling of High-Altitude Clear Air Turbulence in the United States: A Machine Learning Approach
High-altitude Clear Air Turbulence (CAT) poses significant risks to aviation safety due to its unpredictability and challenges in detection. This study leverages machine learning models to improve CAT prediction within U.S. airspace at 200-350 hPa pressure levels, utilizing Pilot Reports (PIREPs), ERA5 reanalysis data, and aircraft aerodynamic parameters from the BADA database. Gradient boosting algorithms, particularly XGBoost, achieved the highest performance with an AUC of 0.904, demonstrating superior capability in capturing non-linear atmospheric dynamics. Key findings highlight the dominance of geographic coordinates (17.5% feature importance) and turbulence indices like TI3 in pred...
| Authors | Kadir Gokdeniz, Irem Ulku |
|---|---|
| Primary category | physics.ao-ph |
| Published | 16 June 2026 |
| Last updated at source | 16 June 2026 |
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