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.

Traditional methods for ADS-B data quality assessment cannot obtain the quality level objectively and accurately. For better application of ADS-B data, an ADS-B data quality evaluation index system is constructed on the basis of analysis of ADS-B data quality requirements in industry applications, transmitting equipment performance, data security, etc. A new data quality evaluation method is proposed based on the ensemble learning Adaptive Boosting (AdaBoost) algorithm. In this method, the best quality grade category is determined by K-means clustering, data labels are determined by combining the Technique for Order Preference by Similarity to Idealsolution (TOPSIS), and the evaluation model is trained and optimized by the AdaBoost algorithm. The data of Tianjin Airport are use for case analysis. The experiment shows that it is the best scheme to divide ADS-B data quality into 5 grades, and the accuracy of the obtained data quality evaluation model is as high as 98.5%. This verifies that the method proposed can effectively avoid the influence of subjective factors and obtain the optimal quality grade classification, improving the stability and accuracy of evaluation results.