English(EN) Predictive Modeling of High-Altitude Clear Air Turbulence in the United States: A Machine Learning Approach

机器学习模型预测高空晴空湍流

作者 PulseAugur 编辑部 · [1 个来源] · 2026-07-15 04:00

研究人员开发了一种机器学习方法来预测美国空域的高空晴空湍流（CAT）。利用飞行员报告、ERA5再分析数据和飞机空气动力学参数，研究发现XGBoost算法的AUC为0.904。地理坐标和湍流指数被发现是预测最重要的特征，冬季是CAT发生率最高的月份。 AI

影响这项研究可以通过提高晴空湍流的可预测性来增强航空安全。

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arXiv cs.LG TIER_1 English(EN) · Kadir Gokdeniz, Irem Ulku · 2026-07-15 04:00

美国高空晴空湍流的预测建模：一种机器学习方法

arXiv:2607.11899v1 Announce Type: cross Abstract: 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. airspa…