AI 中文总结
该研究利用机器学习模型，基于飞行员报告、ERA5再分析数据和飞机空气动力学参数，对美国200 - 350百帕压力水平下的高空晴空湍流进行预测建模，梯度提升算法表现最佳，强调了地理坐标等因素作用，凸显机器学习在CAT预测中的潜力。
AI 中文摘要
高空晴空湍流（CAT）因其不可预测性和探测挑战，对航空安全构成重大风险。本研究利用机器学习模型，在200 - 350百帕压力水平下改善美国空域内的CAT预测，使用飞行员报告（PIREPs）、ERA5再分析数据和来自BADA数据库的飞机空气动力学参数。梯度提升算法，特别是XGBoost，表现最佳，AUC为0.904，在捕捉非线性大气动力学方面能力卓越。关键发现突出地理坐标（特征重要性17.5%）和TI3等湍流指数在预测中的主导地位。空气动力学特征的整合提高了对中度至重度感知湍流强度的检测能力（概率检测从0.845提高到0.866）。季节性分析显示冬季是CAT事件高峰期，与急流活动相关。研究强调机器学习在CAT业务预测中的潜力，并对未来工作提出聚焦全球数据整合和实时遥测以应对气候驱动湍流趋势的建议。
英文摘要
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 prediction, emphasizing the role of regional topography and upper-tropospheric instability. The integration of aerodynamic features such as drag force and wing loading improved the detection of moderate-to-severe perceived turbulence intensity (POD improved from 0.845 to 0.866), providing additional value to traditional aircraft-independent methods. Seasonal analysis revealed winter months as peak periods for CAT incidents, correlating with jet stream activity. While results align with global studies, limitations include geographic scope and aircraft-type diversity. This research underscores the potential of machine learning for operational CAT forecasting, with recommendations for future work focusing on global data integration and real-time telemetry to address climate-driven turbulence trends.
