Case study of a special event of low‐level windshear and turbulence at the Hong Kong International Airport

Chan, Pak Wai

doi:10.1002/asl.1143

Cited by 6 publications

(4 citation statements)

References 12 publications

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“…HKIA is located on an island called Lantau in a subtropical region off the southeast coast of mainland China (Figure 3). Many observational and modeling investigations have demonstrated that the HKIA's significant land-sea contrast and complex orography are favorable for the formation of low-level turbulence and wind shear [22][23][24].…”

Section: Background and Methodsmentioning

confidence: 99%

Turbulence along the Runway Glide Path: The Invisible Hazard Assessment Based on a Wind Tunnel Study and Interpretable TPE-Optimized KTBoost Approach

et al. 2023

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Aircraft landings can be dangerous near airport runways due to wind variability. As a result, an aircraft could potentially miss an approach or divert off its flight path. In this study, turbulence intensity along the runway glide path was investigated using a scaled-down model of Hong Kong International Airport (HKIA) and the complex terrain nearby built in a TJ-3 atmospheric boundary layer wind tunnel. Different factors, including the effect of terrain, distance from the runway threshold, assigned approach runway, wind direction, and wind speed, were taken into consideration. Next, based on the experimental results, we trained and tested a novel tree-structured Parzen estimator (TPE)-optimized kernel and tree-boosting (KTBoost) model. The results obtained by the TPE-optimized KTBoost model outperformed other advanced machine learning models in terms of MAE (0.83), MSE (1.44), RMSE (1.20), and R2 (0.89). The permutation-based importance analysis using the TPE-optimized KTBoost model also revealed that the top three factors that contributed to the high turbulence intensity were the effect of terrain, distance from the runway threshold, and wind direction. The presence of terrain, the shorter distance from the runway, and the wind direction from 90 degrees to 165 degrees all contributed to high turbulence intensity.

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Section: Background and Methodsmentioning

confidence: 99%

Turbulence along the Runway Glide Path: The Invisible Hazard Assessment Based on a Wind Tunnel Study and Interpretable TPE-Optimized KTBoost Approach

et al. 2023

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“…The NWP model used in the study is the same as that adopted in Chan et al (2021Chan et al ( , 2022Chan et al ( , 2023, namely, Regional Atmospheric Modelling System (RAMS) version 6.3. It is nested from the NCEP global model at a spatial resolution of about 10 km.…”

Section: Numerical Weather Prediction Modelmentioning

confidence: 99%

“…The alerting of low-level windshear/turbulence at HKIA is mainly based on remote-sensing (including Doppler LIght Detection and Ranging (LIDAR) systems, Terminal Doppler Weather Radars (TDWR) and wind profilers) and surface (including automatic weather stations and buoys) observations. The short-term forecasting of such aviation hazards has been studied for selected spring-time cases, as reported in Chan et al (2021Chan et al ( , 2022Chan et al ( , 2023. In the present study, the forecasting of windshear/turbulence for tropical cyclone situations would be studied.…”

Section: Introductionmentioning

confidence: 99%

Performance of windshear/microburst detection algorithms using numerical weather prediction model data for selected tropical cyclone cases

Chan

Lai

Kong

et al. 2023

Atmospheric Science Letters

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Three windshear/microburst detection algorithms are used operationally at the Hong Kong International Airport. Their performance is studied in the present article by assuming the availability of the complete set of meteorological data from numerical weather prediction model output. Only selected tropical cyclone cases are considered. The performance is evaluated using pilot reports of windshear. It turns out that the glide path scan windshear detection algorithm has the best overall performance by considering the area under curve using the relative operating characteristic curves. This may be related to the better geometry in covering the glide paths of the airport for this particular algorithm.

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“…This illustrates that S-LLWS is most likely to occur at low-to-moderate hourly temperatures, while a few high temperatures are more likely to cause the occurrence of NS-LLWS. The reason for this might be a temperature inversion [35][36][37], which is an alteration in the troposphere's typical temperature lapse rate, i.e., the reduction in temperature with altitude. On chilly, clear nights, this phenomenon typically occurs close to the ground, where the air immediately above the ground rapidly cools and becomes much colder than the layer of air higher up.…”

Section: Models Performance Assessment and Comparisonmentioning

confidence: 99%

Prediction of a Pilot’s Invisible Foe: The Severe Low-Level Wind Shear

et al. 2022

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Severe low-level wind shear (S-LLWS) in the vicinity of airport runways (25 knots or more) is a growing concern for the safety of civil aviation. By comprehending the causes of S-LLWS events, aviation safety can be enhanced. S-LLWS is a rare occurrence, but it is hazardous for approaching and departing aircraft. This study introduced the self-paced ensemble (SPE) framework and Shapley additive explanations (SHAP) interpretation system for the classification, prediction, and interpretation of LLWS severity. Doppler LiDAR- and PIREPs-based LLWS data from Hong Kong International Airport were obtained, trained, and evaluated to predict LLWS severity. The SPE framework was also compared to state-of-the-art tree-based models, including light gradient boosting machine, adaptive boosting, and classification and regression tree models. The SPE does not require prior data treatment; however, SMOTE-ENN was utilized to treat highly imbalanced LLWS training data for tree-based models. In terms of prediction performance, the SPE framework outperforms all tree-based models. Using SHAP analysis, the SPE was interpreted. It was determined that “runway 25LD”, “mean hourly temperature”, and “mean wind speed” were the most significant contributors to the occurrence of S-LLWS. The most optimistic projections for the occurrence of S-LLWS events at runway 25LD were during periods of low-to-moderate temperatures and relatively medium-to-high wind speeds. Similarly, the majority of S-LLWS events took place on the runway. Without the need for data augmentation during preprocessing, the SPE framework coupled with the SHAP interpretation system could be utilized effectively for the prediction and interpretation of LLWS severity. This study is an invaluable resource for aviation policymakers and air traffic safety analysts.

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Case study of a special event of low‐level windshear and turbulence at the Hong Kong International Airport

Cited by 6 publications

References 12 publications

Turbulence along the Runway Glide Path: The Invisible Hazard Assessment Based on a Wind Tunnel Study and Interpretable TPE-Optimized KTBoost Approach

Turbulence along the Runway Glide Path: The Invisible Hazard Assessment Based on a Wind Tunnel Study and Interpretable TPE-Optimized KTBoost Approach

Performance of windshear/microburst detection algorithms using numerical weather prediction model data for selected tropical cyclone cases

Prediction of a Pilot’s Invisible Foe: The Severe Low-Level Wind Shear

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