Accurate characterization of aircraft turbulence is vital for aviation safety and efficiency. This study leverages 2021 data from nationwide Pilot Reports (PIREPs) and China Eastern Airlines’ in situ Eddy Dissipation Rate (EDR) measurements to systematically compare these two primary turbulence monitoring sources. We quantify their consistencies and discrepancies in capturing turbulence intensity and spatiotemporal patterns to assess their respective value and limitations. The findings indicate that while the diurnal and monthly variation trends of turbulence distributions are generally consistent between the two datasets, significant differences exist in intensity distribution, vertical profiles, and spatial patterns. By examining 242 turbulence events concurrently recorded by both China Eastern Airlines’ EDR and pilot reports, the study identifies a spatial discrepancy within 40 km and an average reporting delay of approximately two minutes in PIREPs, with the delay becoming more pronounced as turbulence intensity increases. Furthermore, pilot-reported “severe” turbulence corresponds to EDR values notably lower than the ICAO standard, revealing a systematic overestimation bias.

This study analyzes the spatiotemporal distribution of turbulence in China from 2020 to 2022 using pilot reports. Results reveal a higher frequency of moderate-to-severe turbulence during spring and winter, particularly in January. Spatially, the primary regions of turbulence occurrence are eastern China, Xinjiang Province, Sichuan Province, and the Qiongzhou Strait, with a focus on altitudes at or above 6000 m. Machine learning models, especially random forest and extreme gradient boosting (XGBoost), demonstrate high accuracy in turbulence prediction, notably for high-altitude events. The random forest model shows optimal performance in winter, achieving an area under the curve of 0.92. The study highlights the importance of thermally related diagnostics, indicating a significant presence of convectively induced turbulence in high-altitude turbulence events. This research not only deepens the understanding of turbulence dynamics in the China region but also underscores the potential of machine learning in enhancing turbulence forecasting.

Turbulence is a significant operational aviation safety hazard during all phases of flight. There is an urgent need for a method of airline turbulence identification in aviation systems to avoid turbulence hazards to aircraft during flight. Integrating flight data and machine learning significantly enhances the efficacy of turbulence identification. Nevertheless, present studies encounter issues including unstable model performance, challenges in data feature extraction, and parameter optimization. Hence, it is imperative to propose a superior approach to enhance the accuracy of turbulence identification along airline. The paper presents a combined swarm intelligence and machine learning model based on data mining for identifying airline turbulence. Based on the theory of swarm-intelligence-based optimization algorithm, the optimal parameters of Categorical Boosting (CatBoost) are obtained by introducing the whale optimization algorithm (WOA), and the corresponding WOA-CatBoost fusion model is established. Then, the Recursive Feature Elimination algorithm (RFE) is used to eliminate the data with lower feature weights, extract the effective features of the data, and the combination with the WOA brings robust optimization effects, whereby the accuracy of CatBoost increased by 11%. The WOA-CatBoost model can perform accurate turbulence identification from QAR data, comparable to that with established EDR approaches and outperforms traditional machine learning models. This discovery highlights the effectiveness of combining swarm intelligence and machine learning algorithms in turbulence monitoring systems to improve aviation safety.

This study analyzes the spatiotemporal distribution of turbulence in China from 2020 to 2022 using pilot reports. Results reveal a higher frequency of moderate-to-severe turbulence during spring and winter, particularly in January. Spatially, the primary regions of turbulence occurrence are eastern China, Xinjiang Province, Sichuan Province, and the Qiongzhou Strait, with a focus on altitudes at or above 6000 m. Machine learning models, especially random forest and extreme gradient boosting (XGBoost), demonstrate high accuracy in turbulence prediction, notably for high-altitude events. The random forest model shows optimal performance in winter, achieving an area under the curve of 0.92. The study highlights the importance of thermally related diagnostics, indicating a significant presence of convectively induced turbulence in high-altitude turbulence events. This research not only deepens the understanding of turbulence dynamics in the China region but also underscores the potential of machine learning in enhancing turbulence forecasting.