National-scale aviation turbulence in China remains poorly understood,largely due to the scarcity of measurements.In the present study,we investigate aviation turbulence across China's air traffic control zones fr...National-scale aviation turbulence in China remains poorly understood,largely due to the scarcity of measurements.In the present study,we investigate aviation turbulence across China's air traffic control zones from 2017 to 2023 by leveraging the combination of pilot reports(PIREPS)and in-situ in-flight turbulence observations.Our analysis reveals a 35%increase in turbulence incidents in the study period,a growth rate that significantly outpaces air traffic throughput.The methods used to diagnose turbulence include single-index,ensemble,and Random Forest(RF)machine learning models.The RF model demonstrates superior diagnostic accuracy,achieving a nationwide area under the curve(AUC)of 0.87,significantly outperforming traditional ensemble and single-index methods.Regionally,the model's performance was particularly effective in challenging western regions like Northwest China and Xinjiang.Furthermore,notable regional variation of turbulence is revealed.Turbulence in the eastern China is predominantly driven by dynamic factors,while that in the western regions is primarily influenced by thermodynamic processes and complex topography.These findings underscore the potential of machine learning to advance turbulence forecasting and enhance aviation weather services in China.展开更多
基金Supported by the Natural Science Foundation of Tianjin(24JCQNJC00190)China Meteorological Service Association(CMSA2024MD012)Research Start-Up Fund Project of Civil Aviation University of China(2020KYQD120)。
文摘National-scale aviation turbulence in China remains poorly understood,largely due to the scarcity of measurements.In the present study,we investigate aviation turbulence across China's air traffic control zones from 2017 to 2023 by leveraging the combination of pilot reports(PIREPS)and in-situ in-flight turbulence observations.Our analysis reveals a 35%increase in turbulence incidents in the study period,a growth rate that significantly outpaces air traffic throughput.The methods used to diagnose turbulence include single-index,ensemble,and Random Forest(RF)machine learning models.The RF model demonstrates superior diagnostic accuracy,achieving a nationwide area under the curve(AUC)of 0.87,significantly outperforming traditional ensemble and single-index methods.Regionally,the model's performance was particularly effective in challenging western regions like Northwest China and Xinjiang.Furthermore,notable regional variation of turbulence is revealed.Turbulence in the eastern China is predominantly driven by dynamic factors,while that in the western regions is primarily influenced by thermodynamic processes and complex topography.These findings underscore the potential of machine learning to advance turbulence forecasting and enhance aviation weather services in China.
