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.

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.

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.

Intercomparison of Local Warming Trends of Shanghai and Hong Kong Based on 120-Year Temperature Observational Data.

Using surface air temperature observations from 1901 to 2020, this study compared the warming trends of Shanghai and Hong Kong over a period of 120 years. The statistical results reveal the following: (1) The average temperatures of the two cities underwent fluctuating increases during the past 120 years, with linear warming rates of 0.23 °C/decade in Shanghai and 0.13 °C/decade in Hong Kong. (2) The fluctuation ranges of maximum temperature in the two cities were considerably higher than those of mean temperature. Moreover, in both cities, the annual mean maximum temperature decreased during a phase of more than a decade. The fluctuation ranges of minimum temperature were smaller, whereas the linear increases were higher than those for the mean temperature. (3) The diurnal temperature ranges (DTRs) of the two cities decreased; a certain phase of the decreases in DTR in the two cities was caused by decreases in the maximum temperature. (4) At a certain stage of urban development, owing to the shading effect of new high-rise buildings, the solar shortwave radiation reaching the Earth's surface decreased, and anthropogenic heat generated by the energy consumption of buildings and urban human activities at that time was not sufficient to make up for the reduced shortwave radiation. This result may have led to the declines in the maximum temperature experienced by both cities. (5) Currently, the number of hot days and extremely hot days in the two cities has increased significantly compared with that a century ago, indicating that climate warming has an adverse impact on human settlements.

Impact of the COVID-19 on the vertical distributions of major pollutants from a tower in the Pearl River Delta.

The outbreak of the 2019 novel coronavirus (COVID-19) had a large impact on human health and socio-economics worldwide. The lockdown implemented in China beginning from January 23, 2020 led to sharp reductions in human activities and associated emissions. The declines in primary pollution provided a unique opportunity to examine the relationship between anthropogenic emissions and air quality. This study reports on air pollutant and meteorological measurements at different heights from a tall tower in the Pearl River Delta. These measurements were used to investigate the vertical scale response of pollutants to understand reductions in human activities. Compared to that in the pre-lockdown period (from December 16, 2019), the concentrations of surface layer nitric oxide (NOx), fine particulate matter (PM2.5), and daily maximum 8 h average ozone (MDA8O3) declined significantly during the lockdown by 76.8%, 49.4%, and 18.6%, respectively. Although the vertical profiles of NOx and O3 changed during the lockdown period, those of PM2.5 remained the same. During the lockdown period, there were statistically significant correlations between PM2.5 and O3 but not between PM2.5 and NOx at four heights, indicating that the main composition of PM2.5 have dramatically changed, during which the impact of NOx on PM2.5 became insignificant. Additionally, O3 concentrations were also insensitive to NOx concentrations during the lockdown, implying that O3 levels were more of a representative of regional background level. In this case, local photochemical formation is no longer a significant ozone source. This evidence suggests that it is possible to mitigation of PM2.5 and O3 levels simultaneously by significant reductions in anthropogenic emissions.

On the Use of Dynamic Calibration to Correct Drop Counter Rain Gauge Measurements.

Dynamic calibration was performed in the laboratory on two catching-type drop counter rain gauges manufactured as high-sensitivity and fast response instruments by Ogawa Seiki Co. Ltd. (Japan) and the Chilbolton Rutherford Appleton Laboratory (UK). Adjustment procedures were developed to meet the recommendations of the World Meteorological Organization (WMO) for rainfall intensity measurements at the one-minute time resolution. A dynamic calibration curve was derived for each instrument to provide the drop volume variation as a function of the measured drop releasing frequency. The trueness of measurements was improved using a post-processing adjustment algorithm and made compatible with the WMO recommended maximum admissible error. The impact of dynamic calibration on the rainfall amount measured in the field at the annual and the event scale was calculated for instruments operating at two experimental sites. The rainfall climatology at the site is found to be crucial in determining the magnitude of the measurement bias, with a predominant overestimation at the low to intermediate rainfall intensity range.

A direct aircraft observation of helical rolls in the tropical cyclone boundary layer.

Helical rolls are known to play a significant role in modulating both the mean and turbulence structure of the atmospheric boundary layer in tropical cyclones. However, in-situ measurements of these rolls have been limited due to safety restrictions. This study presents analyses of data collected by an aircraft operated by the Hong Kong Observatory in Typhoon Kalmaegi (1415) and Typhoon Nida (1604). Examination of the flight-level data at ~ 600 m altitude confirmed the existence of sub-kilometer-scale rolls. These rolls were mostly observed in the outer-core region. Turbulent momentum fluxes were computed using the eddy correlation method. The averaged momentum flux of flight legs with rolls was found to be ~ 2.5 times that of legs without rolls at a similar wind speed range. This result suggests that rolls could significantly modulate turbulent transfer in the tropical cyclone boundary layer. This roll effect on turbulent fluxes should be considered in the planetary boundary layer parameterization schemes of numerical models simulating and forecasting tropical cyclones.

Quantitative effects of atmospheric diffusion on surface aerosol extinction in the Pearl River Delta region.

Aerosol pollution is closely related to meteorological conditions. In order to accurately evaluate the effectiveness of emission reduction policies, it is very important to separate meteorological effects from emissions in long-term PM changes. In this study, we used surface extinction coefficient (SEC) of aerosol particles as an indicator to estimate the long-term trend of PM pollutant conditions, and proposed a parameter surface ventilation index (SVI) to describe the atmospheric diffusion. A novel method was introduced to quantify the effect of atmospheric diffusion and emissions on SEC from an inter-annual scale, and its applicability and limitations was discussed. The results showed that annual mean SEC exhibited an increasing trend with a rate of 15.6 Mm-1∙year-1 from 2000 to 2007 and a decreasing trend with a rate of 14.4 Mm-1∙year-1 from 2008 to 2018. The annual frequency distribution of SVI obeyed a lognormal distribution. The SVI value mainly concentrated between 300 and 3600 m2.s-1, SEC was negatively correlated with SVI in this range. In most cases, SEC variations were mainly dominated by emissions, and SEC variations at clean sites were more influenced by atmospheric diffusion than that at polluted sites. Emissions remained high during 2003-2007, then gradually decreased during 2008-2014 and finally accelerated decrease during 2015-2018, indicating that control policies have effectively mitigated PM pollution since 2008. In generally, this method can provide valuable information for policy makers to evaluate the emission control measures for air pollution mitigation and prevention.

Influence of aerosol hygroscopicity and mixing state on aerosol optical properties in the Pearl River Delta region, China.

Both the effects of aerosol hygroscopicity and mixing state on aerosol optical properties were analyzed using ground-based measurements and a Mie model in this study. The sized-resolved particle hygroscopic growth factor at RH = 90% (Gf(90%)) and the enhancement factor for the scattering coefficients (f(RH)sp) were measured by a self-constructed Hygroscopic Tandem Differential Mobility Analyzer (H-TDMA) and two nephelometers in parallel (PNEPs) respectively from 22nd February to 18th March 2014 in the Pearl River Delta, China. In addition, the particle number size distribution (PNSD) and BC mass concentration (MBC) were measured simultaneously. During the observation period, the f(RH)sp increased sharply along with increasing RH (40%-85%) and the value of f(80%)sp was 1.77 ±â€¯0.18. The mean Gf(90%) for all particles are 1.44 (80 nm), 1.48 (110 nm), 1.52 (150 nm) and 1.55 (200 nm), and the mean Gf(90%) for more-hygroscopic particles are 1.58 (80 nm), 1.63 (110 nm), 1.66 (150 nm) and 1.67 (200 nm) respectively. Based on Gf, PNSD and MBC, the enhancement factor of the aerosol optical properties (extinction (f(RH)ep), scattering (f(RH)sp), backscattering (f(RH)hbsp), absorption (f(RH)absp), and hemispheric backscatter fraction (f(RH)hbsp)) were calculated under three aerosol mixing state assumptions. The results show that the calculated f(80%)sp values agreed well with the ones measured by PNEPs, illustrating that the Gf size distribution fittings are reasonable. The f(RH)ep, f(RH)sp and f(RH)hbsp increased along with increasing RH for three mixtures, while f(RH)HBF decreased. The f(RH)absp increased for the homogenously internal mixture, but remained stable for the external mixture. For the core-shell mixture, the f(RH)absp increased from RH = 0 to 75% and then decreased, due to a decrease of light entering the BC core. The enhancement factor of aerosol direct radiative forcing (f(RH)Fr) increased sharply as the RH elevated for the external mixing state. However, f(RH)Fr increased or decreased along with the elevated RH for the homogenously internal mixture and the core-shell mixture depending on initial value of the aerosol direct radiative forcing (∆Fr) in a dry condition.

Understanding heat patterns produced by vehicular flows in urban areas.

Vehicular traffic has strong implication in the severity and degree of Urban Heat Island (UHI) effect in a city. It is crucial to map and monitor the spatio-temporal heat patterns from vehicular traffic in a city. Data observed from traffic counting stations are readily available for mapping the traffic-related heat across the stations. However, macroscopic models utilizing traffic counting data to estimate dynamic directional vehicular flows are rarely established. Our work proposes a simple and robust cell-transmission-model to simulate all the possible cell-based origin-destination trajectories of vehicular flows over time, based on the traffic counting stations. Result shows that the heat patterns have notable daily and weekly periodical circulation/pattern, and volumes of heat vary significantly in different grid cells. The findings suggest that vehicular flows in some places are the dominating influential factor that make the UHI phenomenon more remarkable.

Source-receptor relationships for PM2.5 during typical pollution episodes in the Pearl River Delta city cluster, China.

Located in the Southern China monsoon region, pollution days in Pearl River Delta (PRD) were classified into "Western type", "Central type" or "Eastern type", with a relative percentage of 67%, 24% and 9%, respectively. Using this classification system, three typical pollution events were selected for numerical simulations using the WRF-Chem model. The source sensitivity method for anthropogenic emissions of PM2.5 and its precursors was applied to identify the source-receptor relationships for PM2.5 among 9 cities in PRD. For "Western type" case, the PRD region was under control of a high-pressure system with easterly prevailing winds. The PM2.5 concentrations in the western PRD region were higher than those in the eastern region, with emissions from cities in the eastern PRD region having higher contributions. Within the PRD's urban cluster, PM2.5 in Huizhou, Dongguan and Shenzhen was mainly derived from local emissions, whereas the PM2.5 in the other cities was primarily derived from external transport. For "Eastern type" case, the PRD was influenced by Typhoon Soulik with westerly prevailing winds. Emissions from cities in the western PRD region had the highest impacts on the overall PM2.5 concentration. PM2.5 in Jiangmen and Foshan was primarily derived from local emissions. Regarding "Central type" case, the PRD region was under control of a uniform pressure field with low wind speed. PM2.5 concentrations of each city were primarily caused by local emissions. Overall, wind flows played a significant role in the transport and spatial distribution of PM2.5 across the PRD region. Ideally, local governments would be wise to establish joint prevention and control measures to reduce regional atmospheric pollution, especially for "Western type" pollution.

Process analysis of a regional air pollution episode over Pearl River Delta region, China, using the MM5-CMAQ model.

UNLABELLED: This study focuses on the influences of a warm high-pressure meteorological system on aerosol pollutants, employing the simulations by the Models-3/CMAQ system and the observations collected during October 10-12, 2004, over the Pearl River Delta (PRD) region. The results show that the spatial distributions of air pollutants are generally circular near Guangzhou and Foshan, which are cities with high emissions rates. The primary pollutant is particulate matter (PM) over the PRD. MM5 shows reasonable performance for major meteorological variables (i.e., temperature, relative humidity, wind direction) with normalized mean biases (NMB) of 4.5-38.8% and for their time series. CMAQ can capture one peak of all air pollutant concentrations on October 11, but misses other peaks. The CMAQ model systematically underpredicts the mass concentrations of all air pollutants. Compared with chemical observations, SO2 and O3 are predicted well with a correlation coefficient of 0.70 and 0.65. PM2.5 and NO are significantly underpredicted with an NMB of 43% and 90%, respectively. The process analysis results show that the emission, dry deposition, horizontal transport, and vertical transport are four main processes affecting air pollutants. The contributions of each physical process are different for the various pollutants. The most important process for PM10 is dry deposition, and for NO(x) it is transport. The contributions of horizontal and vertical transport processes vary during the period, but these two processes mostly contribute to the removal of air pollutants at Guangzhou city, whose emissions are high. For this high-pressure case, the contributions of the various processes show high correlations in cities with the similar geographical attributes. According to the statistical results, cities in the PRD region are divided into four groups with different features. The contributions from local and nonlocal emission sources are discussed in different groups. IMPLICATIONS: The characteristics of aerosol pollution episodes are intensively studied in this work using the high-resolution modeling system MM5/SMOKE/CMAQ, with special efforts on examining the contributions of different physical and chemical processes to air concentrations for each city over the PRD region by a process analysis method, so as to provide a scientific basis for understanding the formation mechanism of regional aerosol pollution under the high-pressure system over PRD.