Severe Wind Shear Research Articles

Windshear analysis over six airports in Saudi Arabia

The windshear (WS) or wind gradient is a short-lived microscale or meso-gamma weather phenomenon and is potentially very dangerous for aviation everywhere. This research presents the calculation of WS frequency and intensity from radiosonde data at six airport stations namely Abha, Jeddah, Madinah, Tabuk, Riyadh, and Dammam airport in Saudi Arabia for the period 1991–2020. The frequency and intensity of WS indicate that the number of severe WS increased suddenly from 2015 below 30 m level at all stations except for Riyadh there is no sudden increase. At all other levels, the number of light WS increased from bottom to up to ~ 900 m and then gradually decreased at 1200 m irrespective of stations. Overall, the highest WS observed in the autumn season for all stations except it is in summer for Jeddah. The occurrence of WS is higher in the daytime as compared to the nighttime round the year for Dammam, Madinah, Riyad, and Tabuk while the pattern is opposite for Jeddah. In Abha, it depends on the season. The sudden increase of severe WS from 2015 can be linked to the increase of building areas surrounded the airport. However, further investigation is required to understand the large number of severe WS and its relation to climate change in the region along with teleconnection to the large-scale circulations.

Preconvective environments of severe convective winds over North China and South China

This study aimed to identify the environmental discrepancy between severe convective wind (SCW) and climate background over North China and South China and between SCW occurring in these two regions, which were prone to SCW in China. 17 parameters from 11,727 (13130) climate background soundings and 4404 (2513) SCW soundings in North (South) China were quantitatively investigated. These parameters were grouped as significant important parameters (SIP), moderate important parameters (MIP), and weak important parameters (WIP). The skill in discriminating the environments between SCW and climate background steadily decreased from SIP to MIP and then to WIP. In North China, SCW typically occurred under moderate to severe deep wind shear and low convective available potential energy (CAPE) conditions. However, in South China, SCW tended to occur at low CAPE and moderate deep wind shear, as well as moderate CAPE and moderate to severe deep wind shear. Compared to the background climate, the SCW environment was associated with considerable instability, high shear, and dry and cold air in the middle layer over both North and South China. A substantial variation in wind shear was identified between the SCW and background in North China, whereas a considerable difference in comprehensive conditions involving CAPE and wind shear was detected in South China. The SCW environment is characterized by a steeper lapse rate in the middle layer as well as a higher Total Totals index in North China than in South China, while the opposite was true for CAPE between two regions. In general, the thermal parameter discrimination was larger than the shear metric discrimination when comparing the soundings for the two regions.

Assessment of Wind Shear Severity in Airport Runway Vicinity using Interpretable TabNet approach and Doppler LiDAR Data

ABSTRACT Severe wind shear events near airport runways pose serious safety risks and are a growing concern in civil aviation. Identification of severe wind shear risk factors may enhance aviation safety. Although rare, severe wind shear impacts safety by affecting the airspeed, lift, and maneuverability of aircraft. This study presents TabNet, a novel deep learning technique coupled with Bayesian optimization (BO) to predict wind shear severity in the runway vicinity using Doppler LiDAR data from Hong Kong International Airport. To address imbalanced wind shear data, it was first processed by resampling techniques and then used as input to TabNet. The analysis demonstrated that Bayesian-tuned TabNet (BO-TabNet) with SVM-SMOTE-processed data led to better performance compared to other strategies. The TabNet architecture employs the attention mechanism to enable model-specific interpretability. Analysis showed that the most important contributing factor was the summer season, followed by the wind shear encounter location (1 nautical miles from the runway at the departure end). Additionally, a more comprehensive model-agnostic LIME method was used to elucidate the model from a local perspective. By predicting severe wind shear and assessing contributing factors, aviation stakeholders can proactively manage and mitigate the associated risks, leading to safer and more efficient operations.

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.

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

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.

Numerical Simulation of Flow over an Airport During A Low-level Wind Shear Event

In this paper we present the flow features of a severe low-level wind shear event near an airport simulated using a high-resolution numerical weather prediction model. The open-source model Weather Research and Forecasting (WRF) was used for the numerical simulations. Our initial analysis shows the ability of the model in capturing the possibility of the wind shear event.

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

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.

Evolution and Structure of a Dry Microburst Line Observed by Multiple Remote Sensors in a Plateau Airport

The civilian airplane is a common transportation mode for the local people in the Qinghai-Tibet Plateau (QTP). Due to the profound dynamic and thermal effects, the QTP can trigger strong windstorms during the warm season, during which downbursts can cause severe low-level wind shear and threaten aviation safety. However, the study of downbursts over QTP has not been given much attention. This study analyzes and interprets a typical traveling dry microburst line that happened at the Xining Caojiapu International Airport (ZLXN) on 14 May 2020, intending to show a better understanding of the dry downbursts over QTP and explore the synergetic usage of different remote sensing technologies for downburst detection and warning in plateau airports. Specifically, the characteristics of synoptic conditions, the convective system formation process, and the structure and evolution of downbursts and relevant low-level winds are comprehensively investigated. The results show that, under the control of an upstream shallow trough, features of the local atmosphere state, including a dry-adiabatic stratification, a shallow temperature inversion, increases in solar radiation heating, and strong vertical shears of horizontal winds, can be favorable atmospheric prerequisites for the formation and development of dry storms and downbursts. Low-reflectivity storm cells of the Mesoscale Convective System (MCS) organize to form narrow bow echoes, and downbursts show features of radial wind convergences and rapid descending reflectivity cores with hanging virga as observed by a Doppler weather radar. Moreover, details of gales, gust fronts, convergences, turbulences, wind collisions, and outflow interactions triggered by the downburst line are also detected and interpreted by a scanning Doppler wind lidar from different perspectives. In addition, the findings in this work have been compared with the results observed in Denver, U.S., and some simulation studies. Finally, a few conceptual models of low-level wind evolutions influenced by the dry downburst line are given.

Autonomous flight cycles and extreme landings of airliners beyond the current limits and capabilities using artificial neural networks

We describe the Intelligent Autopilot System (IAS), a fully autonomous autopilot capable of piloting large jets such as airliners by learning from experienced human pilots using Artificial Neural Networks. The IAS is capable of autonomously executing the required piloting tasks and handling the different flight phases to fly an aircraft from one airport to another including takeoff, climb, cruise, navigate, descent, approach, and land in simulation. In addition, the IAS is capable of autonomously landing large jets in the presence of extreme weather conditions including severe crosswind, gust, wind shear, and turbulence. The IAS is a potential solution to the limitations and robustness problems of modern autopilots such as the inability to execute complete flights, the inability to handle extreme weather conditions especially during approach and landing where the aircraft’s speed is relatively low, and the uncertainty factor is high, and the pilots shortage problem compared to the increasing aircraft demand. In this paper, we present the work done by collaborating with the aviation industry to provide training data for the IAS to learn from. The training data is used by Artificial Neural Networks to generate control models automatically. The control models imitate the skills of the human pilot when executing all the piloting tasks required to pilot an aircraft between two airports. In addition, we introduce new ANNs trained to control the aircraft’s elevators, elevators’ trim, throttle, flaps, and new ailerons and rudder ANNs to counter the effects of extreme weather conditions and land safely. Experiments show that small datasets containing single demonstrations are sufficient to train the IAS and achieve excellent performance by using clearly separable and traceable neural network modules which eliminate the black-box problem of large Artificial Intelligence methods such as Deep Learning. In addition, experiments show that the IAS can handle landing in extreme weather conditions beyond the capabilities of modern autopilots and even experienced human pilots. The proposed IAS is a novel approach towards achieving full control autonomy of large jets using ANN models that match the skills and abilities of experienced human pilots and beyond.

Hybrid Method to Identify Second-trip Echoes Using Phase Modulation and Polarimetric Technology

For pulse Doppler radars, the widely used method for identifying second-trip echoes (STs) in the signal processing level yields significant misidentification in regions of high turbulence and severe wind shear. In the data processing level, although the novel algorithm for ST identification does not yield significant misidentification in specific regions, its overall identification performance is not ideal. Therefore, this paper proposes a hybrid method for the identification of STs using phase modulation (signal processing) and polarimetric technology (data processing). Through this approach, most of the STs are removed, whereas most of the first-trip echoes (FTs) remain untouched. Compared with the existing method using a signal quality index filter with an optimized threshold, the hybrid method exhibits superior performance (Heidke skill scores of 0.98 versus 0.88) on independent test datasets, especially in high-turbulence and severe-wind-shear regions, for which misidentification is significantly reduced.

Forecasting Severe Weather with Random Forests

AbstractUsing nine years of historical forecasts spanning April 2003–April 2012 from NOAA’s Second Generation Global Ensemble Forecast System Reforecast (GEFS/R) ensemble, random forest (RF) models are trained to make probabilistic predictions of severe weather across the contiguous United States (CONUS) at Days 1–3, with separate models for tornado, hail, and severe wind prediction at Day 1 in an analogous fashion to the Storm Prediction Center’s (SPC’s) convective outlooks. Separate models are also trained for the western, central, and eastern CONUS. Input predictors include fields associated with severe weather prediction, including CAPE, CIN, wind shear, and numerous other variables. Predictor inputs incorporate the simulated spatiotemporal evolution of these atmospheric fields throughout the forecast period in the vicinity of the forecast point. These trained RF models are applied to unseen inputs from April 2012 to December 2016, and their forecasts are evaluated alongside the equivalent SPC outlooks. The RFs objectively make statistical deductions about the relationships between various simulated atmospheric fields and observations of different severe weather phenomena that accord with the community’s physical understandings about severe weather forecasting. Using these quantified flow-dependent relationships, the RF outlooks are found to produce calibrated probabilistic forecasts that slightly underperform SPC outlooks at Day 1, but significantly outperform their outlooks at Days 2 and 3. In all cases, a blend of the SPC and RF outlooks significantly outperforms the SPC outlooks alone, suggesting that use of RFs can improve operational severe weather forecasting throughout the Day 1–3 period.

Analysis of Mountain Wave Effects on a Hard Landing Incident in Pico Aerodrome Using the AROME Model and Airborne Observations

A hard landing incident in Pico Aerodrome (LPPI) involving an Airbus A320-200 aircraft is investigated using airborne observations and forecasts of the AROME (Applications of Research to Operations at Mesoscale) model. A second flight is also analyzed. The severity of the wind shear during both flights is quantified using the intensity factor “I” that is based on aerial data and recommended by ICAO (International Civil Aviation Organization). During Flight 1, 36% of the landing phase (below 2100 ft) occurred under “severe” wind shear conditions and 16% occurred under “strong” conditions. Upstream characteristics included southwest winds, stable stratification and a Froude number close to 1. According to the AROME model, these circumstances triggered the development of vertically propagating mountain waves, with maximum vertical velocities above 400 ft/min and exceeding 200 ft/min in the flight path. These conditions, together with the severe wind shear, may have caused the incident. During the second flight, a wake with lee vortices and reversed flow developed in the region of the flight path, which is consistent with a low upstream Froude number and/or with the flow regime diagram of previous studies. During the approach phase of this flight, “severe” wind shear conditions were absent, with “strong” ones occurring 4% of the time. It predominantly displayed “light” conditions during 68% of this phase. As a result of the comparison between “I” and the AROME turbulence indicators, preliminary thresholds are proposed for these indexes. Lastly, this study provides an objective verification of AROME wind forecasts, showing a good agreement with airborne observations for wind speeds above 10 kt, but a poor skill for weaker winds.

Severe wind shear at Hong Kong International Airport: climatology and case studies

ABSTRACTSevere wind shear (headwind change of 30 kn or more) at Hong Kong International Airport is documented in this study. Its climatology, including the number of reports from pilots each year, captured by the wind shear alerting service, its cause and its magnitude are presented. The purpose of the study is to document the characteristics of severe wind shear at an operating airport. Some typical cases of severe wind shear are also examined, such as observation by remote‐sensing instruments and flight data analysis. It is found that the shear magnitudes in pilot reports of severe wind shear, as with pilot reports of wind shear in general, are subjective in nature. They may not be consistent with headwind changes as determined from the flight data. Sometimes, the pilot reports of wind shear may be based on wind trend information available onboard for certain fleets of aircraft. The wind shear can be transient in nature, for which an alert beforehand is difficult.

Modeling and Simulation of China C Series Large Aircraft with Microburst

To simulate and analyze a large aircraft with severe wind shear, nonlinear equations for a rigid aircraft under various wind conditions were designed. In this paper, a double-vortex-ring method is adopted to generate a three-dimensional microburst wind field. This model incorporates a flight dynamics module and three flight simulations based on various forms of nonlinear equations. In addition, four primary wind gradients are considered to compute the aerodynamic coefficients. In simulation experiments with a China C series large aircraft, equations using air-relative velocities and inertial velocities are compared and analyzed. Although the nonlinear equations derived using the air-relative velocities should be identical to those using the inertial velocities, the results show that the latter are more accurate in practice because the former bring error into the simulation when estimating the acceleration induced by the wind field.

A Method of Building Vertical Wind Shear Feature and Convective Storms Recognition Based on This Feature

There is a very close relation between severe convective storms and vertical wind shear. Therefore, it is particularly important to extract the vertical wind shear feature used to recognize storms and distinguish the type of storms such as rainstorm, hail with small or large scale. In meteorology, Doppler radar echo images are used to detect storms, which are reflectivity images(RI) and radial velocity images(RVI). This paper delineates probable storms firstly in RI with low elevation angles, then raises gradually the elevation angle to calculate radial wind shear in RVI point by point and generate a feature called the Vertical Shear Factor(VSF). A lot of testing show that the VSF's difference between rainstorm and large hail is very marked and only by this feature the radio of distinguishing rainstorms from large hails can be increased closely 5∼10 times than reflectivity strength usually used by weathermen.

Hardware Implementation of Aircraft Landing Controller by Evolutionary Computation and DSP

This paper presents a hybrid control scheme for the aircraft automatic landing system. PID control law is adopted in the controller design. Disturbance adaptive capability is demonstrated through hardware in the loop simulations. The control scheme uses PID controller with evolutionary computation technique. Control gains are selected by real-valued genetic algorithms. Different crossover methods are utilized to improve the performance of conventional automatic landing system. Hardware implementation of this intelligent controller is performed by a DSP with VisSim platform. The proposed intelligent controllers can successfully expand the controllable environment in severe wind shear conditions.

Managing “Real” Anesthesia Emergencies on Human Simulators

Managing “Real” Anesthesia Emergencies on Human Simulators

Target pitch angle for the microburst escape maneuver

Recovery performance of a commuter-type aircraft in a microburst encounter is studied using a constant-pitch-attitude strategy and flight path optimization. Results obtained indicate that the pitch attitude which maximized climb rate in a wind shear condition is strongly dependent on whether the aircraft is subjected to a horizontal shear or a downdraft. The pitch attitude which maximizes ground clearance depends on the altitude of the encounter, the strength of the microburst, and the initial position of the aircraft with respect to the downburst core. Best results are obtained at relatively low target pitch angles, in severe wind shear encounters at very low altitudes. A technique for maximizing ground clearance involves maintaining a low pitch attitude early in the encounter, followed by a gradual pitch-up that ceases when the wind shear has been excited.

Robust Kalman filter design for predictive wind shear detection

Severe low-altitude wind shear is a threat to aviation safety. Newly developed airborne sensors measure the radial component of wind along a line directly in front of an aircraft. The authors use optimal estimation theory to define a detection algorithm to warn of hazardous wind shear from these sensors. To achieve robustness, a wind shear detection algorithm must distinguish threatening wind shear from less hazardous gustiness, despite variations in wind shear structure. Statistical analysis methods to refine wind shear detection algorithm robustness are presented. Computational methods predict the ability to warn of severe wind shear and avoid false warning. Comparative capability of the detection algorithm as a function of its design parameters is determined, identifying designs that provide robust detection of severe wind shear.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Thrust laws for microburst wind shear penetration

Two thrust guidance strategies have been developed and analyzed for the ability that they give aircraft to safely penetrate microburst wind shear during landing approach. These are used in conjunction with a pitch steering strategy that has already been shown to give improved performance by maintaining nominal glide path in the presence of microburst winds. The strategies provide valuable understanding of good thrust policy in a microburst. One of the two strategies is to control the airspeed to its nominal value, and the other strategy is to control the airspeed or the inertial speed, which ever is smaller, to the nominal value. To evaluate these strategies, a simplified 1st-order aircraft model has been used. Headwind/tailwind and downdraft microburst models have been considered separately. The safe-performance limits with respect to ability to track glide path have been plotted and compared with those for optimal trajectories. The thrust laws' performance limits are not as good as those for optimal trajectories, as expected; the thrust guidance laws cannot use the global knowledge of the wind field that the optimal trajectories use. Nevertheless, the ability of the practical strategies to safely penetrate severe microburst wind shear has been demonstrated. The best performing guidance law was that which controlled the minimum of airspeed and inertial speed to a nominal value. This demonstrates the importance of using thrust to keep the inertial speed at or above the nominal value. NOMENCLATURE