Wind shear severity regimes and multi-step forecasting at Hong Kong International Airport: evidence from the central runway 07C/25C

Wind shear is a critical meteorological hazard in aviation, especially during takeoff and landing, where rapid changes in wind speed and direction can affect lift and airspeed, compromise aircraft performance, and threaten safety. Existing studies predominantly focus on single-horizon wind shear prediction, which limits the characterization of wind shear evolution across multiple future horizons. This limitation restricts early assessment and timely intervention; thus, multi-step forecast frameworks are necessary. This study presents a comprehensive framework for wind shear severity regime analysis and short-term multi-step time series forecasts at Hong Kong International Airport, with a focus on the central runway (RWY 07C/25C). The approach employs unsupervised methods, K-Means and Gaussian mixture models (GMM), for data-driven classification of wind shear severity, alongside a hybrid forecast framework that combines Salp Swarm Algorithm-optimized variational mode decomposition (SSA-VMD) with deep learning models, which include GRU, LSTM, and their bidirectional (BiGRU, BiLSTM) and residual (ResGRU, ResLSTM) variants. Doppler LiDAR data from July 2023 to August 2025 provide the basis for model development and evaluation. The severity classification identifies four distinct levels, with K-Means providing clearer cluster separation than GMM, with silhouette scores of 0.322 for 07C and 0.329 for 25C and reveals clear differences in event frequency and duration between the two RWY directions. The forecast results show high accuracy at short horizons, followed by a gradual decline as the forecast horizon extends. For 07C, the VMD-based models achieve R 2 values of 0.995–0.997 at 1 step and 0.728–0.868 at 9 steps, while for 25C, R 2 ranges from 0.995 to 0.998 at 1 step to 0.735–0.819 at 9 steps. Model performance varies across forecast horizons and RWY directions, with no single architecture providing the best performance across all cases. The ablation study confirms the contribution of VMD, with RMSE reductions of 57.88–94.03% for 07C and 61.25–96.15% for 25C across the evaluated forecast horizons. The results highlight the value of multi-scale wind shear decomposition combined with optimized deep learning models and severity-based classification for reliable short-term wind shear forecasts and proactive operational decisions in aviation.