Atmospheric turbulence poses a significant hazard to aviation, with severe encounters costing airlines millions of dollars per year in compensation, aircraft damage, and delays due to required post-event inspections and repairs. Moreover, attempts to avoid turbulent airspace cause flight delays and en route deviations that increase air traffic controller workload, disrupt schedules of air crews and passengers and use extra fuel. For these reasons, the Federal Aviation Administration and the National Aeronautics and Space Administration have funded the development of automated turbulence detection, diagnosis and forecasting products. This paper describes a methodology for fusing data from diverse sources and producing a real-time diagnosis of turbulence associated with thunderstorms, a significant cause of weather delays and turbulence encounters that is not well-addressed by current turbulence forecasts. The data fusion algorithm is trained using a retrospective dataset that includes objective turbulence reports from commercial aircraft and collocated predictor data. It is evaluated on an independent test set using several performance metrics including receiver operating characteristic curves, which are used for FAA turbulence product evaluations prior to their deployment. A prototype implementation fuses data from Doppler radar, geostationary satellites, a lightning detection network and a numerical weather prediction model to produce deterministic and probabilistic turbulence assessments suitable for use by air traffic managers, dispatchers and pilots. The algorithm is scheduled to be operationally implemented at the National Weather Service’s Aviation Weather Center in 2014.

中文翻译：

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使用随机森林诊断航空湍流

大气湍流对航空业构成重大危害，严重的事故每年给航空公司造成数百万美元的赔偿、飞机损坏以及由于所需的事后检查和维修造成的延误。此外，试图避开湍流空域会导致航班延误和航路偏差，从而增加空中交通管制员的工作量，扰乱机组人员和乘客的时间表，并使用额外的燃料。出于这些原因，美国联邦航空管理局和美国国家航空航天局资助了自动湍流检测、诊断和预测产品的开发。本文描述了一种融合来自不同来源的数据并实时诊断与雷暴相关的湍流的方法，当前的湍流预报没有很好地解决天气延迟和遇到湍流的一个重要原因。数据融合算法使用回顾性数据集进行训练，该数据集包括来自商用飞机的客观湍流报告和并置的预测数据。它使用多个性能指标在独立测试集上进行评估，包括接收器操作特性曲线，这些指标用于在部署之前进行 FAA 湍流产品评估。原型实施融合了来自多普勒雷达、地球同步卫星、闪电探测网络和数值天气预报模型的数据，以产生适合空中交通管理人员、调度员和飞行员使用的确定性和概率湍流评估。