Historical analysis (2001–2019) of low‐level wind shear at the Hong Kong International Airport

Hon, K. K.; Chan, Pak Wai

doi:10.1002/met.2063

Cited by 16 publications

(6 citation statements)

References 37 publications

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“…In recent years, thanks to advancements in monitoring technology and forecasting capabilities, aviation accidents caused by weather-related factors have seen a significant reduction [37]. However, incidents involving aircraft unable to perform normal takeoffs and landings due to wind shear remain alarmingly common [83][84][85][86][87][88], and one significant cause of wind shear is a microburst. Dry microbursts, in particular, are challenging to detect and pose greater dangers.…”

Section: Discussionmentioning

confidence: 99%

Analysis of the Causes and Wind Field Structure of a Dry Microburst in a Weak Weather Background

Feng,

Zheng,

Pan

et al. 2023

Atmosphere

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Dry microbursts in weak weather backgrounds, due to their small scale and general lack of precipitation, are often difficult to observe using weather radar. On the night of 26 April 2023, a dry microburst occurred at the Yinchuan Airport. Based on conventional meteorological observations, Automated Weather Observing System (AWOS) data, and the Doppler Wind Lidar data, an analysis was conducted on the causes and wind field structure of this microburst. It was found that (1) the sounding data indicated a DCAPE value of 880 J·kg−1, which is important for forecasting the potential for dry microburst events; (2) the foehn from the Helan Mountains contributed to the occurrence of microburst weather at the Yinchuan Airport; (3) the Doppler Wind Lidar wind data showed distinct characteristics of the wind field during this microburst event, including a symmetric horizontal wind field structure, significant vertical downdraft velocities (reaching −5.76 m·s−1), and low-level wind shear over the airport runway and its vicinity; and (4) effective monitoring of such microburst weather events with the Doppler Wind Lidar wind measurements is crucial for ensuring aviation safety.

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Section: Discussionmentioning

confidence: 99%

Analysis of the Causes and Wind Field Structure of a Dry Microburst in a Weak Weather Background

Feng,

Zheng,

Pan

et al. 2023

Atmosphere

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“…It is among the airports that suffer the most from LLWS on a global scale. Perhaps every 400 to 500 flights, a notable LLWS event will happen (Hon & Chan, 2022). The topographical features of the southern region of HKIA, as depicted in Figure 2, enhance the effects of LLWS, thereby causing disturbances in the air currents throughout the entire glide path.…”

Section: Methodsmentioning

confidence: 99%

Interpretable ensemble imbalance learning strategies for the risk assessment of severe‐low‐level wind shear based on LiDAR and PIREPs

Khattak,

Chan,

Chen

et al. 2023

Risk Analysis

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The occurrence of severe low‐level wind shear (S‐LLWS) events in the vicinity of airport runways poses a significant threat to flight safety and exacerbates a burgeoning problem in civil aviation. Identifying the risk factors that contribute to occurrences of S‐LLWS can facilitate the improvement of aviation safety. Despite the significant influence of S‐LLWS on aviation safety, its occurrence is relatively infrequent in comparison to non‐SLLWS incidents. In this study, we develop an S‐LLWS risk prediction model through the utilization of ensemble imbalance learning (EIL) strategies, namely, BalanceCascade, EasyEnsemble, and RUSBoost. The data for this study were obtained from PIREPs and LiDAR at Hong Kong International Airport. The analysis revealed that the BalanceCascade strategy outperforms EasyEnsemble and RUSBoost in terms of prediction performance. Afterward, the SHapley Additive exPlanations (SHAP) interpretation tool was used in conjunction with the BalanceCascade model for the risk assessment of various factors. The four most influential risk factors, according to the SHAP interpretation tool, were hourly temperature, runway 25LD, runway 25LA, and RWY (encounter location of LLWS). S‐LLWS was likely to happen at Runway 25LD and Runway 25LA in temperatures ranging from low to moderate. Similarly, a high proportion of S‐LLWS events occurred near the runway threshold, and a relatively small proportion occurred away from it. The EIL strategies in conjunction with the SHAP interpretation tool may accurately predict the S‐LLWS without the need for data augmentation in the data pre‐processing phase.

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“…Innumerable observation-based and simulation studies demonstrated that HKIA's complex land-sea contrast and intricate orography are favorable conditions for the emergence of low-level wind shear [49]. Approximately every 400-500 fights, a signifcant low-level wind shear event takes place [50]. From 1998, when HKIA frst opened, through 2015, 97% of reports indicated level-level wind shear between 15 and 25 knots [51].…”

Section: Study Locationmentioning

confidence: 99%

Missed Approach, a Safety-Critical Go-Around Procedure in Aviation: Prediction Based on Machine Learning-Ensemble Imbalance Learning

Khattak

Chan

Chen

et al. 2023

Advances in Meteorology

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The final approach phase of an aircraft accounts for nearly half of all aviation incidents worldwide due to low-level wind shear, heavy downpours, runway excursions, and unsteady approaches. Adopting the missed approach (MAP) procedures may prevent a risky landing, which is usually executed in those situations, but it is safety-critical and a rare occurrence. This study employed machine learning-ensemble imbalance learning to predict MAPs under low-level wind shear conditions based on environmental and situational parameters. The models were developed using the 2017–2021 Hong Kong International Airport (HKIA) Pilot Reports (PIREPs). Initially, imbalance data were applied to machine learning models such as the random forest (RF), light gradient boosting machine (LGBM), and extreme gradient boosting (XGBoost), but these were unable to accurately predict the occurrence of MAPs. Then, these models were used as base estimators for ensemble imbalance learning methods, including the self-paced ensemble (SPE) framework, the balance cascade model, and the easy ensemble model. The SPE framework utilizing XGboost as the base estimator performed better than other frameworks in terms of recall, F1-score, balanced accuracy, and geometric mean. Afterwards, SHAP was utilized to interpret the SPE framework with XGboost as the base estimator. Results showed that low-level wind shear magnitude, runway orientation, and vertical location of low-level wind shear contributed most to MAPs. Runways 07C and 07R had the most MAPs. Most MAPs were initiated when low-level wind shear was within 500 feet of the ground. Strong tailwind triggered MAPs more than headwind. For aviation safety researchers and airport authorities, the framework proposed in this study is a valuable tool.

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Historical analysis (2001–2019) of low‐level wind shear at the Hong Kong International Airport

Cited by 16 publications

References 37 publications

Analysis of the Causes and Wind Field Structure of a Dry Microburst in a Weak Weather Background

Analysis of the Causes and Wind Field Structure of a Dry Microburst in a Weak Weather Background

Interpretable ensemble imbalance learning strategies for the risk assessment of severe‐low‐level wind shear based on LiDAR and PIREPs

Missed Approach, a Safety-Critical Go-Around Procedure in Aviation: Prediction Based on Machine Learning-Ensemble Imbalance Learning

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