用机器学习提升美国高空晴空湍流预测精度，关键靠气象与飞机数据融合。
Predictive Modeling of High-Altitude Clear Air Turbulence in the United States: A Machine Learning Approach
- 融合飞行员报告、再分析数据和飞机参数，用XGBoost模型预测湍流
- 模型AUC达0.904，地理坐标和湍流指数贡献最大，冬季为高发期
- 加入飞机气动特征后，中到强湍流识别率提升，适合航空气象预报应用
高空晴空湍流（CAT）因难以预测和探测，严重威胁飞行安全。本研究利用机器学习模型，在美国200–350 hPa气压层范围内提升CAT预测能力，数据来源包括飞行员报告（PIREPs）、ERA5再分析数据及BADA数据库中的飞机气动参数。梯度提升算法中，XGBoost表现最优，AUC达0.904，展现出对非线性大气动力学的强捕捉能力。关键发现表明，地理坐标（特征重要性17.5%）和湍流指数如TI3在预测中占主导地位，凸显区域地形与上对流层不稳定性的作用。引入阻力、翼载等气动特征后，中至强感知湍流检测率（POD）从0.845提升至0.866，显著优于传统独立飞机方法。季节分析显示冬季为高峰，与急流活动相关。尽管结果与全球研究一致，但存在地理范围和机型多样性局限。该研究证明了机器学习在实际航空湍流预报中的潜力，建议未来工作聚焦全球数据整合与实时遥测，以应对气候变化带来的湍流趋势变化。
原文摘要 · Abstract (English)
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.
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