Investigation of Aviation Turbulence in Different Air Traffic Control Zones Across China

National-scale turbulence in China remains poorly understood, largely due to the scarcity of measurements.In this present study, we investigates aviation turbulence across China's air traffic control zones from 2017 to 2023 by leveraging the combination of PIREPS and in-situ in-flight turbulence observations. Our analysis reveals a 35% increase in turbulence incidents, a growth rate that significantly outpaces air traffic throughput. The methods used to diagnose turbulence include single-index, ensemble, and Random Forest machine learning models. The Random Forest model demonstrated 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 the western regions are 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.