Search Results (2)

Aircraft-missed approaches pose significant safety challenges, particularly under adverse weather conditions like wind shear. This study examines the critical factors influencing wind-shear-related missed approaches at Hong Kong International Airport (HKIA) using Pilot Report (PIREP) data from 2015 to 2023. A Binary Logistic Model (BLM) with L1 (Lasso) and L2 (Ridge) regularization was applied to both balanced and imbalanced datasets, with the balanced dataset created using the Synthetic Minority Oversampling Technique (SMOTE). The performance of the BLM on the balanced data demonstrated a good model fit, with Hosmer–Lemeshow statistics of 5.91 (L1) and 5.90 (L2). The Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) were slightly lower for L1 regularization, at 1528.77 and 1574.35, respectively, compared to 1528.86 and 1574.66 for L2. Cohen’s Kappa values were 0.266 for L1 and 0.253 for L2, reflecting moderate agreement between observed and predicted outcomes and improved performance compared to the imbalanced data. The analysis identified designated-approach runway, aircraft classification, wind shear source, and vertical proximity of wind shear to runway as the most influential factors. Runways 07R and 07C, gust fronts as wind shear sources, and wind shear occurring within 400 ft of the runway posed the highest risk for missed approaches. Narrow-body aircrafts also demonstrated greater susceptibility to turbulence-induced missed approaches. These findings show the importance of addressing these risk factors and enhancing safety protocols for adverse weather conditions. Full article

Gust fronts (GFs) belong to the boundary layer convergence system. A strong GF can cause serious wind disasters, so its automatic monitoring and identification are very helpful but difficult in daily meteorological operations. By collecting convective weather processes in Hubei, Jiangsu, and other regions of China, 1422 GFs from 106 S-band new-generation weather radar (CINRAD/SA) volume scan data are labeled as positive samples by means of human–computer interaction, and the same number of negative samples are randomly tagged from no GF radar data. A deep learning dataset including 2844 labels with a positive and negative sample ratio of 1:1 is constructed, and 80%, 10%, and 10% of the dataset are separated as training, validation, and test sets, respectively. Then, the training dataset is expanded to 273,120 samples by data augmentation technology. Since the height of a GF is generally less than 1.5 km, three deep-learning-based models are trained for GF automatic recognition according to the distance from the radars. Three models (M1, M2, M3) are trained with the data at a 0.5° elevation angle from 65 to 180 km away from the radars, at 0.5° and 1.5° angles from 40 to 65 km, and at 0.5°, 1.5°, and 2.4° angles within 40 km, respectively. The precision, confusion matrix, and its derived indicators including receiver operating characteristic curve (ROC) and the area under ROC (AUC) are used to evaluate the three models by the test set. The results show that the identification precisions of the models are 97.66% (M1), 90% (M2), and 90.43% (M3), respectively. All the hit rates are over 89%, the false positive rates are less than 11%, and the critical success indexes (CSIs) surpass 82%. In addition, all the optimal critical points on the ROC curves are close to (0, 1), and the AUC values are above 0.93. These results suggest that the three models can effectively achieve the automatic discrimination of GFs. Finally, the models are demonstrated by three GF events detected with Qingpu, Nantong, and Cangzhou radars. Full article