By addressing the imbalanced proportions of the data category samples in the velocity structure function of the LiDAR turbulence identification model, we propose a flight turbulence identification model utilizing both a conditional generative adversarial network (CGAN) and extreme gradient boosting (XGBoost). This model can fully learn small- and medium-sized turbulence samples, reduce the false alarm rate, improve robustness, and maintain model stability. Model training involves constructing a balanced dataset by generating samples that conform to the original data distribution via the CGAN. Subsequently, the XGBoost model is iteratively trained on the sample set to obtain the flight turbulence classification level. Experiments show that the turbulence recognition accuracy achieved on the CGAN-generated augmented sample set improves by 15%. Additionally, when incorporating LiDAR-obtained wind field data, the performance of the XGBoost model surpasses that of traditional classification algorithms such as K-nearest neighbours, support vector machines, and random forests by 14%, 8%, and 5%, respectively, affirming the excellence of the model for turbulence classification. Moreover, a comparative analysis conducted on a Zhongchuan Airport flight crew report showed that the model achieved a 78% turbulence identification accuracy, indicating enhanced recognition ability under data-imbalanced conditions. In conclusion, our CGAN/XGBoost model effectively addresses the proportion imbalance issue.

Accurate measurements of visibility are of great importance in many fields. This paper reports a multipoint visibility measurement (MVM) method to measure and calculate the atmospheric transmittance, extinction coefficient, and meteorological optical range (MOR). The relative errors of atmospheric transmittance and MOR measured by the MVM method and traditional transmissometer method are analyzed and compared. Experiments were conducted indoors, and the data were simultaneously processed. The results revealed that the MVM can effectively improve the accuracy under different visibility conditions. The greatest improvement of accuracy was 27%. The MVM can be used to calibrate and evaluate visibility meters.

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.

Considering that existing theories consider runway lights as markers, neglecting their roles as lighting devices that increase sky background luminance and thus affect runway visual range (RVR) measurements, the actual values of sky background luminance observed by pilots during take-off are calculated and compared with the instrumental RVR measurements in this paper. The RVR measurement errors due to the installation positions of these devices are subsequently analysed. Furthermore, the impacts of different runway light types and positions on sky background luminance are explored, leading to the proposal of an optimized runway lighting scheme. The results show that the representation of sky background luminance sensor data in RVR measurement systems for nighttime airport runways is limited. A deviation of over 90% exists between the sky background luminance of the runway entrance and the RVR measurement equipment location, resulting in a relative error in RVR measurements of over 18%. The centreline and entrance lights contribute significantly to the total sky background luminance. Lights closer to the observer contribute more luminance. In the second proposed scheme, modifying the lighting range to 600 m yields the highest RVR improvement, with an optimization magnitude of over 5% across all visibility ranges.