Observation and Warning Algorithms for Low-Level Wind Shear at Airports in Xinjiang, China

Low-level wind shear is a frequent hazardous phenomenon at airports in the Xinjiang region of China, mainly due to complex terrain and highly variable weather conditions. It poses a significant risk to aircraft operations, particularly during take-off and landing. In this study, Doppler wind lidar observations are used to detect and identify low-level wind shear in the vicinity of airports, with a focus on improving the performance of existing identification algorithms under complex terrain conditions. Several commonly used wind shear detection algorithms are implemented, evaluated, and further refined. Based on their complementary strengths, a joint warning algorithm is developed to provide more reliable wind shear alerts. In addition, machine learning methods are explored to directly extract wind shear signals from raw lidar data, aiming to achieve faster detection without relying on full wind field retrieval. The results show that the joint warning algorithm clearly improves warning performance compared to individual algorithms, with fewer false alarms and missed events. The machine learning approach also demonstrates promising capability for rapid wind shear identification. These results suggest that combining multi-algorithm warning strategies with data-driven methods can effectively enhance future airport wind shear warning systems in the Xinjiang region. Further improvements are expected by training machine learning models with expanded libraries of representative wind shear cases.