Supported by the Experiment on Typhoon Intensity Change in Coastal Areas, Phase II (EXOTICCA-II), under the auspices of the ESCAP/WMO Typhoon Committee, the Shanghai Typhoon Institute (STI) and the Hong Kong Observatory (HKO) conducted an inaugural manned aircraft-based collaborative field experiment targeting Typhoon Trami (2024) over the South China Sea. This pioneering experiment utilized two modified aircraft platforms: the King Air 350i, equipped with advanced instruments such as a Ka-band cloud radar (KPR) and a Droplet Measurement Technology (DMT) cloud particle measurement system, which focused on studying cloud microphysical processes within the typhoon’s spiral rainbands; and the Challenger 605 aircraft operated by the HKO, with a deployed dropsonde system to gather vertical wind speed, temperature, and humidity profiles. The comprehensive data collected from this experiment provide vital insights into the warm-cloud processes that influence typhoon precipitation and act to validate the use of modified manned aircraft for typhoon observation. This successful joint effort marks a milestone in China’s typhoon observation, establishing a robust technical foundation for future large-scale observational campaigns and international collaborations across the western North Pacific.

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.

A study was conducted on the problem of insufficient turbulence samples and low recognition rate when using Doppler wind lidar for clear-air turbulence recognition in the flight terminal area. An improved Deep Convolutional Generative Adversarial Network (DCGAN) algorithm was proposed. Eddy Dissipation Rate (EDR) images were constructed using the radial wind speed data of nine months from the Doppler wind lidar experimental platform of the Lanzhou Zhongchuan International Airport. Samples with clear-air turbulence were selected to construct a turbulence sample set, the DCGAN structure was improved by expanding the convolutional layer and transposing the convolutional layer, so as to achieve sample expansion. The post-confrontation was then used for recognition. The results show that the recognition accuracy of the post-confrontation discriminator trained with the original sample set and that trained with the augmented sample set are both better than that trained with the Convolutional Neural Network (CNN) and the pre-confrontation discriminator, with improvements of 6.55%, 8.25%, and 0.31%, 1.9%, respectively. A comparison with the measured samples shows that the recognition accuracy was improved by 3.33% and 6.67%, verifying the feasibility of the proposed method.