Hong Kong International Airport Research Articles

Environmental Impact by Air Traffic: Assessing aircraft noise nearby Hong Kong International Airport

As part of a more global research effort towards a greener aviation, the present study focuses on the noise impact by aircraft operations around a major airport, namely the Hong Kong International Airport (HKIA). To assess a pre-existing aircraft noise prediction platform vis-à-vis the specificities of the local aviation scene, the ground noise impacts of 70+ aircraft flying in and out of HKIA are live-measured in two locations of Hong Kong city. This is achieved through in-situ audio recordings of aircraft during take-off/landing flight phases, along with a real-time tracking of their characteristics (aircraft and engine types, flightpaths, atmospheric conditions, etc.). Once suitably post-processed, the measurements are analyzed to characterize better the noise impacts by these various flights in regard to their specificities (e.g., aircraft types, flight operations). In a second stage, these noise impacts are compared against their digital twins, which are simulated via the noise prediction platform, using the actual flight characteristics (aircraft types, flightpaths, thrust, airspeed, etc.). This comparative assessment is achieved for a total of 60 flights consisting of 37 departures (resp. 23 arrivals) and involving 6 (resp. 5) aircraft types. Overall, the comparison between the field tests and their digital twins proves to be reasonably good, with both measured and predicted noise levels falling within a range of a few decibels across all flights, on average. Finally, specific analyses are conducted to explore the uncertainties weighing on the predictions, possibly explaining some of the mismatches observed between the experimental and computational results. Overall, the study further demonstrates that aircraft noise prediction methods such as the present one constitute a valuable means to assess the environmental impacts of air traffic operations, including when the latter are highly specific — as in Hong Kong city, owing to its complex airspace and meteorological conditions.

AI-supported estimation of safety critical wind shear-induced aircraft go-around events utilizing pilot reports

The occurrence of wind shear and severe thunderstorms during the final approach phase contributes to nearly half of all aviation accidents. Pilots usually employ the go-around procedure in order to lower the likelihood of an unsafe landing. However, multiple factors influence the go-arounds induced by wind shear. In order to predict the wind shear-induced go-around, this study utilized a cutting-edge AI-based Combined Kernel and Tree Boosting (KTBoost) framework with various data augmentation strategies. First, the KTBoost model was trained, tested, and compared to other Machine Learning models using the data extracted from Hong Kong International Airport (HKIA)-based Pilot Reports for the years 2017–2021. The performance evaluation revealed that the KTBoost model with Synthetic Minority Oversampling Technique - Edited Nearest Neighbor (SMOTE-ENN)- augmented data demonstrated superior performance as measured by the F1-Score (94.37%) and G-Mean (94.87%). Subsequently, the SHapley Additive exPlanations (SHAP) approach was employed to elucidate the interpretation of the KTBoost model using data that had been treated with the SMOTE-ENN technique. According to the findings, flight type, wind shear magnitude, and approach runway contributed the most to the wind shear-induced go-around. Compared to international flights, Hong Kong-based airlines endured the highest number of wind shear-induced go-arounds. Shear due to the tailwind contributed more to the go-around than the headwinds. The runways with the most wind shear-induced Go-arounds were 07C and 07R.

Explainable Boosting Machine: A Contemporary Glass-Box Strategy for the Assessment of Wind Shear Severity in the Runway Vicinity Based on the Doppler Light Detection and Ranging Data

Pilots commonly undergo training to effectively manage instances of wind shear (WS) during both the landing and takeoff stages. Nevertheless, in exceptional circumstances, there may be instances of severe wind shear (SWS) surpassing a magnitude of 30 knots, leading to adverse effects on the operation of taking off and landing aircraft. This phenomenon can lead to the execution of aborted landing maneuvers and deviations from the intended glide path. This study utilized the explainable boosting machine (EBM), an advanced machine learning (ML) model known for its transparency, to predict the severity of WS occurrences and analyze the underlying factors. The dataset consisted of 21,392 data points from 2018 to 2022 acquired from two Doppler light detection and ranging (LiDAR) systems installed at Hong Kong International Airport (HKIA). Initially, the Doppler LiDAR data received data treatment in order to address the issue of data imbalance. Subsequently, utilizing the processed data, the hyperparameters of EBM were optimized using the Bayesian optimization technique. The EBM model underwent subsequent training and evaluation, wherein its performance metrics were computed and compared with those of an alternative glass-box model including decision tree (DT) and counterpart black-box models, namely, random forest (RF) and extreme gradient boosting (XGBoost). The EBM model trained on synthetic minority oversampling technique (SMOTE)-treated data demonstrated superior performance in comparison with the alternative models, as indicated by its higher geometric mean (0.77), balanced accuracy (0.78), and Matthews’ correlation coefficient (0.169). Furthermore, the EBM exhibited enhanced predictive performance and facilitated a comprehensive analysis of individual and pairwise factor interactions in the prediction of WS severity. This enabled the assessment of the factors that contributed to the instances of SWS in the proximity of airport runways.

Assessment of Crosswind Speed over the Runway Glide Path Using an Interpretable Local Cascade Ensemble Approach Aided by Wind Tunnel Experiments

The close proximity of crosswinds to airport runways presents great hazards to landing operations. As a result, an aircraft is susceptible to encountering a loss of control. Elevated levels of turbulence are commonly linked with strong crosswind speeds over the runway glide path. Therefore, it is imperative to evaluate the factors that impact crosswind speeds. The susceptibility of the runways at Hong Kong International Airport (HKIA) to severe crosswinds is well established. This study aimed to build a scaled model of HKIA, along with its surrounding terrain/buildings, within a TJ-3 ABL wind tunnel to compute the crosswind speeds under different wind directions over the runway glide path. Subsequently, utilizing the outcomes of the experiment, a cutting-edge local cascade ensemble (LCE) model was employed in conjunction with a tree-structured Parzen estimator (TPE) to evaluate the crosswind speed over the north runway glide path. The comparative analysis of the TPE-LCE model was also conducted with other machine learning models. The TPE-LCE model demonstrated superior predictive capabilities in comparison to alternative models, as assessed by MAE (0.490), MSE (0.381), RMSE (0.617), and R2 (0.855). The SHAP analysis, which utilized TPE-LCE predictions, revealed that two factors, specifically “Effect of Terrain/Buildings” and “Distance from Runway,” exhibiting noteworthy influence over the probability of encountering elevated crosswind speeds over the runway glide path. The optimal conditions for high-crosswind speeds were found to be characterized by the absence of nearby terrain features or structures, a smaller distance from HKIA’s north runway threshold, and with a wind direction ranging from 125 to 180 degrees.

Integrating SBAS-InSAR and LSTM for subsidence monitoring and prediction at Hong Kong international airport

Hong Kong International Airport (HKIA) is one of the busiest airports in the world, and much of its land is reclaimed from the sea, making it prone to uneven subsidence of the ground. Monitoring and predicting the subsidence of its surface are crucial for ensuring the operational safety of the airport. This paper firstly obtained the surface subsidence characteristics of the HKIA through applying the Small Baseline Subset Interferometry Synthetic Aperture Radar (SBAS-InSAR) technology, and then the spatial–temporal evolution was analyzed by using the Standard Deviational Ellipse (SDE) method. Moreover, the Long Short-Term Memory (LSTM) was employed to perform surface trend prediction of HKIA. The results show that the HKIA presents different levels of subsidence and uplift, with a maximum average subsidence rate of −64 mm/year and a maximum cumulative subsidence of −199 mm. The comparison between predicted curves and the actual subsidence revealed by InSAR from 2019 to 2023 is highly consistent, with the average absolute error and root mean square error less than 5 mm, and a coefficient of determination greater than 0.99. The LSTM model utilized in this paper can achieve reliable results in subsidence prediction based on time-series InSAR, and provide alternative means for geohazard prediction.

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

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.

Assessing wind field characteristics along the airport runway glide slope: an explainable boosting machine-assisted wind tunnel study

Aircraft landings are especially perilous when the wind is gusty near airport runways. For this reason, an aircraft may deviate from its glide slope, miss its approach, or even crash in the worst cases. In the study, we used the state-of-the-art glass-box model, the Explainable Boosting Machine (EBM), to estimate the variation in headwind speed and turbulence intensity along the airport runway glide slope and to interpret the various contributing factors. To begin, the wind field characteristics were examined by developing a scaled-down model of Hong Kong International Airport (HKIA) runway as well as and the surrounding buildings and complex terrain in the TJ-3 atmospheric boundary layer wind tunnel. The placement of probes along the glide slope of the model runway aided in the measurement of wind field characteristics at different locations in the presence and absence of surrounding buildings. Next, the experimental data was used to train the EBM model in conjunction with Bayesian optimization approach. The counterpart black box models (extreme gradient boosting, random forest, extra tree and adaptive boosting) as well as other glass box models (linear regression and decision tree) were compared with the outcomes of the EBM model. Based on the holdout testing data, the EBM model revealed superior performance for both variation in headwind speed and turbulence intensity in terms of mean absolute error, mean squared error, root mean squared error and R-square values. To further evaluate the impact of different factors on the wind field characteristics along the airport runway glide slope, the EBM model allows for a full interpretation of the contribution of individual and pairwise interactions of factors to the prediction results from both a global and a local perspective.

Observations of the Impacts of Hong Kong International Airport on Water Quality from 1986 to 2022 Using Landsat Satellite

Hong Kong International Airport (HKIA) is an important sea airdrome in China. The aim of this study is to evaluate the impacts of this reclamation on the water quality of the Northwestern Bay of Hong Kong (NWBHK). In all, 117 Landsat 5 TM and 44 Landsat 8 OLI images were preprocessed and matched with the marine water data of 18 in situ monitoring points, acquiring 458 and 119 sets of data, respectively. This study adopted BPNN Machine Learning methods to establish the retrieval algorithm. Based on the images, the construction of HKIA was divided into three stages: (1) the construction of the first and second runways from 1992 to 1995; (2) the construction of the Hong Kong Port from 2013 to 2016; and (3) the construction of the third runway from 2017 to 2020. The concentrations of suspended particulate matter, orthophosphate phosphorus, and dissolved inorganic nitrogen from 1986 to 2022 were retrieved. In this paper, it was found that (1) the construction activities led to an increase in SPM, PO4P, and DIN concentrations in adjacent water bodies; (2) the impact of the Tuen Mun River on the NWBHK increased, while the impact of the Tung Chung River on the NWBHK decreased; and (3) the interception impact of HKIA on the transportation of the Pearl River water became stronger.

Estimating turbulence intensity along the glide path using wind tunnel experiments combined with interpretable tree-based machine learning algorithms

Wind fluctuations near airport runways can make aircraft landings risky. Consequently, an aircraft may veer off its glide path, missed approach, or crash. In this study, a scaled-down model of Hong Kong International Airport (HKIA) and the complex terrain in its vicinity were built in a TJ-3 atmospheric boundary layer wind tunnel to investigate wind turbulence intensity. Cobra probes were placed along the glide slope of the models' runway to compute turbulence intensity at various locations under different inflow wind directions, with and without surrounding terrain. Next, advanced tree-based machine learning algorithms, including Random Forest, Extreme Gradient Boosting, Adaptive Boosting, and Light Gradient Boosting Machine optimized via Bayesian Optimization, were used to estimate turbulence intensity along the glide path. The Bayesian optimized-random forest model outperforms all other models in prediction performance, as measured by MAE (0.521), MSE (1.046), RMSE (1.024), and R2 (0.934). Furthermore, according to SHAP analysis, "Terrain", "Distance from Runway", and "Wind Direction" significantly contributed to high turbulence intensity along the glide path. The most optimistic predictions for high turbulence intensity were the presence of complex terrain, a shorter distance from the runway, and an inflow wind direction between 125 and 200°.

Potential Impact of GNSS Positioning Errors on the Satellite‐Navigation‐Based Air Traffic Management

AbstractThe traditional air traffic management (ATM) is transforming to the Communication, Navigation, Surveillance/ATM, which relies on accurate Global Navigation Satellite System (GNSS) navigation service, particularly during the final approach and landing phases. However, in the event of adverse space weather, there may be a significant increase in total electron contents (TECs) and irregularities in the ionosphere, which may cause considerable GNSS positioning errors. As a result, the aircraft navigation mode has to be switched from satellite navigation to ground navigation, which will reduce the airport acceptance rate and cause an imbalance between flight demands and airport capacity. The ATM authority will have to make tactical measures to remedy the problem, such as flight rescheduling and even cancellations. Hong Kong International Airport (HKIA) is one of the busiest airports in the world and Hong Kong, located in the equatorial ionosphere anomaly region, is prone to impacts of space weather. Thus, we have created a hypothesis scenario in this study by analyzing projected flight data from the HKIA during a simulated geomagnetic storm. Calculation results show that without an ionospheric delay forecast, the potential financial costs related to airlines due to flight delays, cancellations, and diversions could be over 2 million Euros. These costs decrease with an increased lead time of ionospheric delay forecast and the inaccurate ionospheric delay forecast can also result in significant extra costs. We also estimate the time cost of flight delays to passengers can be between 1.7 and 3.0 million Euros.

IRDA: Incremental Reinforcement Learning for Dynamic Resource Allocation

Resource allocation problems often manifest as online decision-making tasks where the proper allocation strategy depends on the understanding of the allocation environment and resource workload. Most existing resource allocation methods are based on meticulously designed heuristics which ignore the patterns of incoming tasks, so the dynamics of incoming tasks cannot be properly handled. To address this problem, we mine the task patterns from the large volume of historical allocation data and propose a reinforcement learning model termed IRDA to learn the allocation strategy in an incremental way. We observe that historical allocation data is usually generated from the daily repeated operations, which is not independent and identically distributed. Training with the partial dataset can make the strategy converged already, thereby wasting the remaining data. To improve the learning efficiency, we partition the whole historical big dataset into multi-batch datasets, which forces the agent continuously to ‘explore’ and learn on the distinct state spaces. We apply the proposed method to handle baggage carousel allocation at Hong Kong International Airport (HKIA). The experimental results show that IRDA improves the baggage carousel resource utilization by around 51.86% compared to the current baggage carousel allocation system at HKIA.

THE SETTLEMENT OF HONG KONG INTERNATIONAL AIRPORT BASED ON THE INTERFEROMETRIC POINT TARGET ANALYSIS

Abstract. Hong Kong International Airport (HKIA) is one of the famous airports built by reclamation of the sea platform. However, the reclaimed area of the airport, which cost about $9 billion, has shown uneven displacement. Over the past two decades, researchers have used different methods to monitor the deformation of HKIA. As an emerging deformation monitoring technology, Interferometric synthetic aperture radar (InSAR), has been widely used to monitoring the deformation of HKIA in the last ten years. In this study, different from conventional interferometry, the target of this interference processing is single look complex (SLC) point data, which was extracted by points in the SLC data stacks (filtered by spectrum and backscatter intensity). We used 40 scenes of COSMO-SkyMed (CSK) SAR images to generate the differential interferogram point data. After multiple phase regression analysis, the SLC point data interferometric processing model can effectively eliminate baseline errors and atmospheric distortions, obtain highly reliable phase unwrapping results.

Assessing Equitable Accessibility Based on Total Travel Cost and Multiple Options: Case Study of Airport Ground Access in Hong Kong

In recent decades, transit equity has become a recurrent topic within the domain of sustainable transportation, where accessibility is a crucial indicator. However, the measurement of accessibility and equity in existing studies is usually based on the route with the shortest travel time, whereas multiple travel routes and corresponding travel costs remain unexamined. Taking the ground access to Hong Kong International Airport (HKIA) as an example, this study aims to fill the previous research gaps by examining the equity of accessibility by public transport simultaneously considering different sets of route options and corresponding total travel costs. These routes have either the shortest travel time or the maximum time-saving benefit–monetary cost ratio. Results reveal spatial variation in the total travel cost, which reflects considerable horizontal inequity. The results, however, indicate that vertical equity exists because the total travel cost is positively correlated with median income level across different locations. In addition, the route that provides a reasonable benefit–cost ratio further improves the equity in accessibility to HKIA. The study illustrates the significance of simultaneous consideration of total travel costs and multiple routes in the assessment of accessibility equity. It also provides transferrable methods for evaluating transit equity.

Improving Lidar Windshear Detection Efficiency by Removal of “Gentle Ramps”

The Doppler Lidar windshear alerting system at the Hong Kong International Airport (HKIA), the first of its kind in the world, has been in operation since 2006. This paper reports on an enhancement to the automatic windshear detection algorithm at HKIA, which aims at filtering out alerts associated with smoother headwind changes spread over longer distances along the aircraft glide path (called “gentle ramps”) which may nonetheless exceed the well-established alerting threshold. Real-time statistics are examined over a 46-month study period between March 2016 and December 2019, covering a total of 2,017,440 min and over 1500 quality-controlled pilot reports of windshear (PIREP). The “gentle ramp removal” (GRR) function is able to effectively cut down the alert duration over the 5 major runway corridors, inclusive of both landing and take-off, which together account for over 98% of the PIREP received at HKIA during the study period. In all 5 runway corridors this is achieved with a proportionately smaller decrease—even with no changes in 2 cases—in the hit rate, highlighting the efficiency of the GRR function. The difference in statistical behaviour across the runway corridors also echo literature findings about the differences in length scale of wind disturbances at different locations within HKIA. This study serves as a unique documentation of the state-of-the-art in operational Lidar windshear detection and can provide useful reference to airports and aviation meteorologists around the world.

Artificial intelligence prediction of air traffic flow rate at the Hong Kong International Airport

The Hong Kong Flight Information Region (HKFIR) is surrounded by the airspaces of Guangzhou, Sanya, Taipei and Manila. International/regional flight routes to and from the Hong Kong International Airport (HKIA) rank among the busiest in the world. Air traffic flow rate is a complex parameter influenced by a variety of factors such as weather, business operation and regulations, etc. Accurate prediction of flow rate allows airport management to optimise tactical planning and decision making, potentially benefitting airport operations and air traffic efficiency. Leveraging the concept of Big Data, this paper establishes a preliminary algorithm for artificial intelligence prediction of air traffic flow rate at HKIA through supervised machine learning. Results based on 2017 data showed positive short-term prediction skills as well as superior performance over persistence based on the previous day.

Prediction of Aircraft Noise Impact with Application to Hong Kong International Airport

As part of a collective research effort towards greener aviation, the present study focuses on the noise impact of aircraft operations around major airports. To this end, an aircraft noise prediction platform is developed, which relies on state-of-the-art functionalities as well as more specific, innovative features. Originally built upon the Aircraft Noise and Performance (ANP) database and its Noise–Power–Distance (NPD) table, the method is further refined to alleviate most of their inherent limitations (e.g., standardized and simplified aircraft noise scenarios). The resulting aircraft noise prediction platform is validated against benchmark cases of increasing complexity, being then applied to real-life situations involving actual aircraft operations around Hong Kong International Airport (HKIA). Specific comparative analyses are conducted, which allow highlighting the variability of the noise impact by aircraft, depending on their type (A330, B777) and/or operational conditions (power settings, meteorological conditions, routes, banks, etc.). The study delivers insightful outcomes, whether phenomenological (aircraft noise impact) or methodological (aircraft noise prediction). As a by-product, it illustrates how noise prediction methods/platforms such as the present one may help in guiding the further expansion of airport operations and/or infrastructures (as is currently the case with HKIA).

Time-Series Analysis on Persistent Scatter-Interferometric Synthetic Aperture Radar (PS-InSAR) Derived Displacements of the Hong Kong–Zhuhai–Macao Bridge (HZMB) from Sentinel-1A Observations

The synthetic aperture radar interferometry (InSAR) technique has been applied in monitoring the deformation of infrastructures, such as bridges, highways, railways and subways. Persistent scatterer (PS)-InSAR is one of the InSAR techniques, which utilises persistent scatterers to derive long-term displacements. This study applied time-series methods to post-process the PS-InSAR-derived time-series displacements with the use of 86 Sentinel-1A acquisitions spanning from 6 January 2018 to 27 November 2020. Empirical mode decomposition (EMD) and seasonal and trend decomposition using loess (STL) were combined to estimate the seasonal component of the total time-series displacements. Then, a temperature correlation map was generated by correlating the seasonal component with the temperature variation. Results show that the thermal expansion phenomenon is pronounced on the buildings of the Zhuhai–Macao Passenger Terminal as well as the bridge and road connecting to the Hong Kong International Airport (HKIA), while it is less obviously observed at the main Hong Kong-Zhuhai-Macao Bridge (HZMB). In addition, sudden changes between subsidence and uplift can be detected through the p-values derived by applying the augmented Dickey-Fuller (ADF) test to the residual signals after removing the linear and seasonal components from the original ones.

The impact of heterogeneous arrival and departure rates of flights on runway configuration optimization

ABSTRACT The rapid growth of the airline industry has caused an enormous demand in the context of air transport and air traffic congestion in several hub airports. In order to alleviate this situation and resolve the imbalance between the arrival and departure rate, efficient runway usage in airport capacity management is an immediate and feasible solution as compared to airport expansion and runway construction. Air Traffic Control (ATC) operators could optimize their runway capacity by operating dynamic runway configuration in switch-mode runways based on the air and airport traffic conditions. A semi-mixed mode runway is considered in this paper, wherein some runways are configured for either landing or take-off operations, while others are operated in switch mode. The demand for arrival and departure is subject to the passenger's demand, flight availability, and timings, preferred flight schedule, and frequency of flight schedule service, and usually vary in different hours (peak and nonpeak hours). Given this feature, ATC can reconfigure the runway mode responding to the current demand for arrival and departure and further seize the runway capacity via a systematic approach. Under the semi-mixed mode situation, formulating the coordination of dynamic runway configuration planning and the Aircraft Sequencing and Scheduling Problem is proposed. The air traffic pattern in Hong Kong International Airport (HKIA) is used as a test case to evaluate the performance of this proposed model. Based on the test results, it was found that this dynamic runway configuration planning and semi-mixed runway design can utilize runway capacity more efficiently. In the numerical study, the dynamic runway configuration planning achieved 71.6%% and 37.08% reduction of flight tardiness than the two segregated runway systems (two landings and one take-off runways and one landing and two take-off runways) in HKIA.

Assessing the risk of windshear occurrence at HKIA using rare‐event logistic regression

AbstractLow‐level windshear poses serious hazards to the flight safety of civil aircraft. The objective of the study was to assess the risk of windshear occurrence within 1 hr at runway corridors at Hong Kong International Airport (HKIA). Windshear report records from HKIA and weather data around HKIA were obtained from Hong Kong Observatory (HKO) between January 2016 and December 2018. The rare‐event logistic regression was employed to evaluate windshear risk considering the imbalanced nature of the data. Several explanatory variables were found to be significantly correlated with the risk of the hourly occurrence of the windshear. Results indicated that at HKIA, March, April, July and August are associated with a higher likelihood of windshear events, while November suffered a significantly lower windshear risk. Besides, winds from the east, southeast, south and southwest are more likely to cause windshear events at HKIA. However, the study found that it was hard to predict windshear occurrence precisely only with the hourly weather variables due to the transient and sporadic nature of windshear. Therefore, more refined data are needed to improve the prediction performance of regression models for windshear occurrence. The present study is the first to analyse the windshear occurrence at airports using the rare‐event logistic regression method. Findings from the study can provide some references and valuable information for related researchers to understand low‐level windshear.

Terminal Wind Hazard Analyses Based on Assimilated Weather Data and Lagrangian Coherent Structures

AbstractThe operational light detection and ranging (lidar) data from the Hong Kong International Airport (HKIA) in China are assimilated in the six-nest, high-resolution Weather Research and Forecasting (WRF) Model. The existing radar data assimilation schemes in the WRF data assimilation (WRFDA) package have been adapted to accommodate the high temporal frequency and spatial resolution of the lidar observations. The weather data are then used to produce Lagrangian coherent structures to detect atmospheric hazards for flights. The coherent structures obtained from the various datasets are contrasted against flight data measured on aircraft. It is found that both WRF and WRFDA produce coherent structures that are more distinguishable than those obtained from two-dimensional retrieval, which may improve the detection of true wind shear hazards.