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15 pages, 5617 KiB

Open AccessArticle

Analysis of Spatio-Temporal Characteristics of Visibility in the Yellow and Bohai Seas Based on Observational Data

by Lei Zhang, Mei Xu, Xiaobin Qiu, Dongbin Zhang, Rongwei Liao, Xiaoyi Fang, Bingui Wu and Fanchao Meng

Atmosphere 2023, 14(7), 1101; https://doi.org/10.3390/atmos14071101 - 30 Jun 2023

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Abstract

In the Yellow and Bohai Seas, the detailed characteristics of visibility are analyzed based on automatic hourly observation data of marine visibility between 2019 and 2021. The results show that the annual average visibility in the Yellow and Bohai Seas is 13.346 km. [...] Read more.

In the Yellow and Bohai Seas, the detailed characteristics of visibility are analyzed based on automatic hourly observation data of marine visibility between 2019 and 2021. The results show that the annual average visibility in the Yellow and Bohai Seas is 13.346 km. The average visibility at high latitudes is higher than that at low latitudes in the Yellow and Bohai Seas. The low visibility area is mainly distributed in the southwest of the Yellow Sea. There are obvious seasonal differences in visibility in the Yellow and Bohai Seas. Visibility is high from September to November, with maximum values in October. Visibility is lowest in July when the maximum visibility is low and the minimum visibility is high. The visibility in spring is overall relatively low, and the areas of low visibility appear in the southwest of the Yellow Sea. The visibility in autumn is overall relatively high, and the areas of high visibility occur in the northern part of the Bohai and Yellow Seas. The visibility has significant intraday variation. The visibility around sunset is significantly higher than that around sunrise. The hourly visibility is low between 4:00 and 9:00, with the lowest visibility most likely around 7:00. The hourly visibility is high between 16:00 and 21:00, with the highest visibility most likely around 18:00. Low visibility occurs frequently between November and April, most of all in March. Low visibility most often occurs between 4:00 and 7:00. Low visibility may occur at any time between November and April, and also in mornings between May and August. It occurs less often at other times. Full article

(This article belongs to the Special Issue Advances in Transportation Meteorology)

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16 pages, 5062 KiB

Open AccessArticle

Study on Risk Prediction Model of Expressway Agglomerate Fog-Related Accidents

by Jianyang Song, Hua Tian, Xiaoyu Yuan, Jingjing Gao, Xihui Yin, Zhi Wang, Meichao Qian and Hengtong Zhang

Atmosphere 2023, 14(6), 960; https://doi.org/10.3390/atmos14060960 - 31 May 2023

Cited by 1 | Viewed by 1071

Abstract

Based on meteorological observations, traffic flow data and information of traffic accidents caused by fog or agglomerate fog along the expressways in Jiangsu Province and Anhui Province in China from 2012 to 2021, key impact factors including meteorological conditions, road hidden dangers and [...] Read more.

Based on meteorological observations, traffic flow data and information of traffic accidents caused by fog or agglomerate fog along the expressways in Jiangsu Province and Anhui Province in China from 2012 to 2021, key impact factors including meteorological conditions, road hidden dangers and traffic flow conditions are integrated to establish the prediction model for risk levels of expressway agglomerate fog-related accidents. This model takes the discrimination of the occurrence conditions of agglomerate fog as the starting term, and determines the hazard levels of agglomerate fog-related accidents by introducing the probability prediction value of meteorological conditions for fog-related accident as the disaster-causing factor. On this basis, the hourly road traffic flow and the location of road sections with a hidden danger of agglomerate fog are taken as traffic and road factors to construct the correction scheme for the hazard levels, and the final predicted risk level of agglomerate fog-related accident is obtained. The results show that for the criteria of disaster-causing factor classification threshold, 72.3% of fog-related accidents correspond to a hazard of a medium level or above, and 86.2% of the road traffic flow conditions are consistent with the levels of the traffic factor defined based on parametric indexes. For risk level prediction, six out of the seven agglomerate fog-related accidents correspond to the level of higher risk or above, which can help provide meteorological support for traffic safety under severe weather conditions. Moreover, the model takes into account the impacts of traffic flow and the road environment, which is conducive to further improving the reliability of the risk assessment results. Full article

(This article belongs to the Special Issue Advances in Transportation Meteorology)

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22 pages, 7151 KiB

Open AccessArticle

Precipitation Nowcasting Based on Deep Learning over Guizhou, China

by Dexuan Kong, Xiefei Zhi, Yan Ji, Chunyan Yang, Yuhong Wang, Yuntao Tian, Gang Li and Xiaotuan Zeng

Atmosphere 2023, 14(5), 807; https://doi.org/10.3390/atmos14050807 - 28 Apr 2023

Cited by 2 | Viewed by 1868

Abstract

Accurate precipitation nowcasting (lead time: 0–2 h), which requires high spatiotemporal resolution data, is of great relevance in many weather-dependent social and operational activities. In this study, we are aiming to construct highly accurate deep learning (DL) models to directly obtain precipitation nowcasting [...] Read more.

Accurate precipitation nowcasting (lead time: 0–2 h), which requires high spatiotemporal resolution data, is of great relevance in many weather-dependent social and operational activities. In this study, we are aiming to construct highly accurate deep learning (DL) models to directly obtain precipitation nowcasting at 6-min intervals for the lead time of 0–2 h. The Convolutional Long Short-Term Memory (ConvLSTM) and Predictive Recurrent Neural Network (PredRNN) models were used as comparative DL models, and the Lucas–Kanade (LK) Optical Flow method was selected as a traditional extrapolation baseline. The models were trained with high-quality datasets (resolution: 1 min) created from precipitation observations recorded by automatic weather stations in Guizhou Province (China). A comprehensive evaluation of the precipitation nowcasting was performed, which included consideration of the root mean square error, equitable threat score (ETS), and probability of detection (POD). The evaluation indicated that the reduction of the number of missing values and data normalization boosted training efficiency and improved the forecasting skill of the DL models. Increasing the time series length of the training set and the number of training samples both improved the POD and ETS of the DL models and enhanced nowcasting stability with time. Training with the Hea-P dataset further improved the forecasting skill of the DL models and sharply increased the ETS for thresholds of 2.5, 8, and 15 mm, especially for the 1-h lead time. The PredRNN model trained with the Hea-P dataset (time series length: 8 years) outperformed the traditional LK Optical Flow method for all thresholds (0.1, 1, 2.5, 8, and 15 mm) and obtained the best performance of all the models considered in this study in terms of ETS. Moreover, the Method for Object-Based Diagnostic Evaluation on a rainstorm case revealed that the PredRNN model, trained well with high-quality observation data, could both capture complex nonlinear characteristics of precipitation more accurately than achievable using the LK Optical Flow method and establish a reasonable mapping network during drastic changes in precipitation. Thus, its results more closely matched the observations, and its forecasting skill for thresholds exceeding 8 mm was improved substantially. Full article

(This article belongs to the Special Issue Advances in Transportation Meteorology)

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19 pages, 3898 KiB

Open AccessArticle

Spatiotemporal Distributions and Vulnerability Assessment of Highway Blockage under Low-Visibility Weather in Eastern China Based on the FAHP and CRITIC Methods

by Tian Jing, Duanyang Liu, Yunxuan Bao, Hongbin Wang, Mingyue Yan and Fan Zu

Atmosphere 2023, 14(4), 756; https://doi.org/10.3390/atmos14040756 - 21 Apr 2023

Cited by 1 | Viewed by 982

Abstract

In this study, the spatiotemporal distributions of highway blockage and the low-visibility weather events in eastern China are studied by taking Jiangsu Province as an example. Based on the record table data of highway-blocking events, a vulnerability evaluation model for the highway network [...] Read more.

In this study, the spatiotemporal distributions of highway blockage and the low-visibility weather events in eastern China are studied by taking Jiangsu Province as an example. Based on the record table data of highway-blocking events, a vulnerability evaluation model for the highway network in Jiangsu Province is established using the weight assignment methods of the fuzzy analytic hierarchy process (FAHP) and criteria importance though intercriteria correlation (CRITIC). By using the geographic information system, the vulnerability evaluation map of road network in low-visibility weather in Jiangsu Province is finally drawn. The results show that the monthly blockage events on Jiangsu highways are more frequent in the north than in the south and are more frequent along the coast than inland, with the highest occurrence number in winter and a second peak in May. There are basically no blockage events from July to October. Traffic blockage on Jiangsu highways mainly occurs between 22:00 and 08:00 Beijing time. In the afternoon, there are almost no highway-blocking events caused by low-visibility weather. The vulnerability of highway blockage in Jiangsu Province is high in the north and low in the south and high in coastal areas and relatively low in inland. The section K6-K99 of the G30 Lianhuo Highway is the most sensitive. Full article

(This article belongs to the Special Issue Advances in Transportation Meteorology)

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20 pages, 6033 KiB

Open AccessArticle

Variation Characteristics of Temperature and Rainfall and Their Relationship with Geographical Factors in the Qinling Mountains

by Yangna Lei, Rongwei Liao, Yumeng Su, Xia Zhang, Duanyang Liu and Lei Zhang

Atmosphere 2023, 14(4), 696; https://doi.org/10.3390/atmos14040696 - 07 Apr 2023

Cited by 1 | Viewed by 1672

Abstract

The Qinling Mountains (QMs) are considered to be the division in geology, geochemistry, and physical geography between northern China and southern China. They have crucial effects on regional climate, especially on rainfall and temperature, and have shown great scientific relevance to climate change [...] Read more.

The Qinling Mountains (QMs) are considered to be the division in geology, geochemistry, and physical geography between northern China and southern China. They have crucial effects on regional climate, especially on rainfall and temperature, and have shown great scientific relevance to climate change research in China. Using the observational daily and monthly rainfall and temperature data derived from meteorological and regional automatic stations—as well as the methods of correlation analysis, climate trend analysis, and Mann–Kendal and t tests—we revealed the spatiotemporal change characteristics of temperature and rainfall and their correlation with elevation, longitude, and latitude. The results show that the annual mean temperature (AMT) underwent a significant increasing trend in the QMs. The maximum AMT increase occurred in spring, and the minimum occurred in summer. Positive anomalies of annual mean rainfall amount (AMRA) occurred in the 1960s, 1980s, and 2010s, and negative anomalies occurred in the 1970s, 1990s, and 2000s. In the QMs, the amount of moderate rainfall (MR) occupied the maximum proportion and accounted for 27.9% of the AMRA, whereas the torrential rainfall (TR) occupied the minimum proportion and accounted for 12.8%. The AMRA amount significantly decreased by 130.1 mm from the 1980s to the 1990s and accounted for 13.5% of the measure in the 1980s. The AMT and AMRA showed consistent change trends with increases in elevation and latitude and showed the opposite trend as the longitude increased. The results offer a further understanding of the meteorological background of the QMs, helping us in further investigating the potential physical mechanisms that influence the spatiotemporal distribution characteristics of temperature and rainfall in the QMs. This study will provide a scientific basis for rainfall and temperature forecasts, with relevance to local ecosystems, agriculture, soil erosion, and the prevention and mitigation of floods in the future. Full article

(This article belongs to the Special Issue Advances in Transportation Meteorology)

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9 pages, 2929 KiB

Open AccessCommunication

Association between the Rail Breakage Frequency in Beijing–Tianjin–Hebei High-Speed Railway and the Eurasian Atmospheric Circulation Anomaly

by Liwei Huo, Linman Xiao, Ji Wang, Dachao Jin, Yinglong Shi and Qian Zhang

Atmosphere 2023, 14(3), 561; https://doi.org/10.3390/atmos14030561 - 15 Mar 2023

Viewed by 1060

Abstract

The spatiotemporal variations in the frequency of rail breakage (FRB) in the high-speed railway of the Beijing–Tianjin–Hebei (BTH) region and its relationship with atmospheric circulation anomalies and surface temperature are analyzed in this study, based on the monthly FRB data of BTH region [...] Read more.

The spatiotemporal variations in the frequency of rail breakage (FRB) in the high-speed railway of the Beijing–Tianjin–Hebei (BTH) region and its relationship with atmospheric circulation anomalies and surface temperature are analyzed in this study, based on the monthly FRB data of BTH region and the ERA5 reanalysis data from 2010 to 2020. The frequency of rail breaking in the BTH region varies significantly depending on the season, with winter having the highest incidence. In fact, more than 60% of the total FRB in the BTH region occur during the winter season. Both the annual total and winter FRB in BTH region are very unevenly distributed in time and space, and both are relatively similar in spatial distribution patterns. The FRB in Beijing railway section is the most frequent, followed by Tianjin, and the lowest frequency is observed in Chengde. It is found that the increasing winter FRB in BTH region and the intensified Siberian high are related. When the Siberian high is strong, the East Asian winter monsoon and the East Asian Trough in the middle troposphere could be enhanced through atmospheric teleconnection, which is conducive to the cold air advection from northern high latitudes to the BTH region, resulting in an abnormally cold winter in BTH region, thus providing low temperatures for broken rails on high-speed railways, and vice versa. The research results might provide a scientific basis for monitoring and predicting the broken rails in BTH high-speed railway during winter, thereby providing a guarantee for the safe operation of the high-speed railway. Full article

(This article belongs to the Special Issue Advances in Transportation Meteorology)

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13 pages, 4685 KiB

Open AccessCommunication

Study of Relative Humidity Vertical Distribution Characteristics before Precipitation by Microwave Radiometer Data over Southeast China

by Yongjiang Yu, Yan Zou and Weihua Pan

Atmosphere 2023, 14(3), 513; https://doi.org/10.3390/atmos14030513 - 07 Mar 2023

Viewed by 1281

Abstract

We investigated the relative humidity (RH) vertical distribution characteristics before precipitation using microwave radiometer measurements over southeast China in 2021. The superposed epoch method is used to analyze the profile and vertical statistical characteristics and evolution of RH during precipitation events. There is [...] Read more.

We investigated the relative humidity (RH) vertical distribution characteristics before precipitation using microwave radiometer measurements over southeast China in 2021. The superposed epoch method is used to analyze the profile and vertical statistical characteristics and evolution of RH during precipitation events. There is a shallow, high-humidity area on the ground, with a thickness of about 0.1–0.2 Km, from 12 to 8 h before precipitation. An obvious dry layer appears in the lower layer near the ground 8–0 h before precipitation, with a thickness of about 1 km and humidity of less than 80%, which continues until the appearance of precipitation. The water vapor content in the air begins to accumulate and the humidity increases before the occurrence of LRs, MRs, and HRs, classified by total rainfall. The SDPs, MDPs, and LDPs, which are classified by precipitation duration, showed more obvious and significant characteristics of humidity increase. The statistical analysis of the 44 precipitation cases shows that the relative humidity on the ground and in the air increases significantly before precipitation, and the vertical distribution of the relative humidity and the increase in the water vapor content in the air have a more direct and obvious impact on the precipitation duration. The deep and high-humidity area of 2–4 km is conducive to maintaining the precipitation process for a long time. Full article

(This article belongs to the Special Issue Advances in Transportation Meteorology)

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19 pages, 3655 KiB

Open AccessArticle

Variation Characteristics of Pavement Temperature in Winter and Its Nowcasting for Xianyang Airport Expressway, China

by Lei Feng, Hua Tian, Xiaoyu Yuan, Lei Miao and Mingyu Lin

Atmosphere 2023, 14(2), 361; https://doi.org/10.3390/atmos14020361 - 11 Feb 2023

Viewed by 1264

Abstract

Based on the pavement temperature observation data of the transportation meteorological stations along the Xianyang Airport Expressway, China, as well as the datasets of precipitation and sunshine hours obtained from the nearby weather stations, the variation characteristics of local pavement temperatures are investigated [...] Read more.

Based on the pavement temperature observation data of the transportation meteorological stations along the Xianyang Airport Expressway, China, as well as the datasets of precipitation and sunshine hours obtained from the nearby weather stations, the variation characteristics of local pavement temperatures are investigated for winter in this study. Results indicate that during the daytime, the pavement temperatures are always higher on sunny and cloudy days than those on rainy and snowy days, while during the nighttime, the temperatures on sunny and cloudy days are higher than those on the days with freezing rain and snow, and with the temperatures on rainy and snowy days without icing being further higher. In general, the pavement temperatures in winter features significant periodic oscillations with cycles of roughly 24 h, 12 h, 8 h, 6 h, 5 h and 4 h, which differ slightly at different times for different stations. Moreover, the nowcasting experiments on the local pavement temperatures are also carried out using a regression model via extracting the corresponding periodic features. It shows the mean absolute errors of about 0.6 °C, 1.2 °C, and 1.5 °C for lead times of 1 h, 2 h, and 3 h, respectively. The nowcasting skills are higher on rainy and snowy days, while are inferior on sunny days. For nowcasting cases initialized at nighttime (daytime), the mean absolute errors are 0.4 °C (0.7 °C) and 0.9 °C (1.4 °C) for lead times of 1 h and 2 h. Examinations suggest that the nowcasting system could be well utilized in plain areas of China, whereas it shows relatively larger biases in plateau areas with complex terrain. Full article

(This article belongs to the Special Issue Advances in Transportation Meteorology)

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14 pages, 7293 KiB

Open AccessArticle

Time-Series Prediction of Intense Wind Shear Using Machine Learning Algorithms: A Case Study of Hong Kong International Airport

by Afaq Khattak, Pak-Wai Chan, Feng Chen and Haorong Peng

Atmosphere 2023, 14(2), 268; https://doi.org/10.3390/atmos14020268 - 28 Jan 2023

Cited by 4 | Viewed by 1579

Abstract

Machine learning algorithms are applied to predict intense wind shear from the Doppler LiDAR data located at the Hong Kong International Airport. Forecasting intense wind shear in the vicinity of airport runways is vital in order to make intelligent management and timely flight [...] Read more.

Machine learning algorithms are applied to predict intense wind shear from the Doppler LiDAR data located at the Hong Kong International Airport. Forecasting intense wind shear in the vicinity of airport runways is vital in order to make intelligent management and timely flight operation decisions. To predict the time series of intense wind shear, Bayesian optimized machine learning models such as adaptive boosting, light gradient boosting machine, categorical boosting, extreme gradient boosting, random forest, and natural gradient boosting are developed in this study. The time-series prediction describes a model that predicts future values based on past values. Based on the testing set, the Bayesian optimized-Extreme Gradient Boosting (XGBoost) model outperformed the other models in terms of mean absolute error (1.764), mean squared error (5.611), root mean squared error (2.368), and R-Square (0.859). Afterwards, the XGBoost model is interpreted using the SHapley Additive exPlanations (SHAP) method. The XGBoost-based importance and SHAP method reveal that the month of the year and the encounter location of the most intense wind shear were the most influential features. August is more likely to have a high number of intense wind-shear events. The majority of the intense wind-shear events occurred on the runway and within one nautical mile of the departure end of the runway. Full article

(This article belongs to the Special Issue Advances in Transportation Meteorology)

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11 pages, 1817 KiB

Open AccessArticle

Spatiotemporal Patterns of Sea Ice Cover in the Marginal Seas of East Asia

by Lei Zhang, Guoyu Ren, Mei Xu, Fanchao Meng, Rongwei Liao, Duanyang Liu, Minyan Wang and Dan Jia

Atmosphere 2023, 14(2), 207; https://doi.org/10.3390/atmos14020207 - 19 Jan 2023

Cited by 1 | Viewed by 1714

Abstract

Using multisource sea ice fusion data, the spatiotemporal characteristics of sea ice cover were analyzed for the marginal seas of East Asia for the period 2005–2021. The results show that there were obvious differences in the beginning and end dates of the sea [...] Read more.

Using multisource sea ice fusion data, the spatiotemporal characteristics of sea ice cover were analyzed for the marginal seas of East Asia for the period 2005–2021. The results show that there were obvious differences in the beginning and end dates of the sea ice in the different sea areas. The northern Sea of Japan had the longest ice period, and Laizhou Bay and Bohai Bay in the Bohai Sea had the shortest ice period. The time when the largest sea ice extent appeared was relatively stable and mostly concentrated in late January to mid-February. There were obvious spatial differences in the duration of the sea ice cover in the marginal seas of East Asia. The duration of the sea ice cover gradually decreased from high latitude to low latitude and from nearshore to open seas. The annual average duration of the sea ice cover was more than 100 days in most of the Sea of Japan and approximately 20 days in most of Laizhou Bay and Bohai Bay. The melting speed was significantly faster than the freezing speed in the Bohai Sea and Yellow Sea, resulting in asymmetric changes in the daily sea ice extent in the two seas. The increasing trends in the maximum sea ice extent and total sea ice extent were 0.912 × 10⁵ km²/10 yr and 0.722 × 10⁷ km²/10 yr, respectively, from 2005 to 2013, both of which passed the significance test at the 0.05 level. Full article

(This article belongs to the Special Issue Advances in Transportation Meteorology)

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19 pages, 3708 KiB

Open AccessArticle

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

by Afaq Khattak, Pak-Wai Chan, Feng Chen and Haorong Peng

Atmosphere 2023, 14(1), 37; https://doi.org/10.3390/atmos14010037 - 25 Dec 2022

Cited by 3 | Viewed by 2152

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Advances in Transportation Meteorology)

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18 pages, 2660 KiB

Open AccessArticle

Prediction of Aircraft Go-Around during Wind Shear Using the Dynamic Ensemble Selection Framework and Pilot Reports

by Afaq Khattak, Pak-Wai Chan, Feng Chen and Haorong Peng

Atmosphere 2022, 13(12), 2104; https://doi.org/10.3390/atmos13122104 - 15 Dec 2022

Cited by 4 | Viewed by 1724

Abstract

Pilots typically implement the go-around protocol to avoid landings that are hazardous due to wind shear, runway excursions, or unstable approaches. Despite its rarity, it is essential for safety. First, in this study, we present three Dynamic Ensemble Selection (DES) frameworks: Meta-Learning for [...] Read more.

Pilots typically implement the go-around protocol to avoid landings that are hazardous due to wind shear, runway excursions, or unstable approaches. Despite its rarity, it is essential for safety. First, in this study, we present three Dynamic Ensemble Selection (DES) frameworks: Meta-Learning for Dynamic Ensemble Selection (META-DES), Dynamic Ensemble Selection Performance (DES-P), and K-Nearest Oracle Elimination (KNORAE), with homogeneous and heterogeneous pools of machine learning classifiers as base estimators for the prediction of aircraft go-around in wind shear (WS) events. When generating a prediction, the DES approach automatically selects the subset of machine learning classifiers which is most probable to perform well for each new test instance to be classified, thereby making it more effective and adaptable. In terms of Precision (86%), Recall (83%), and F1-Score (84%), the META-DES model employing a pool of Random Forest (RF) classifiers outperforms other models. Environmental and situational factors are subsequently assessed using SHapley Additive exPlanations (SHAP). The wind shear magnitude, corridor, time of day, and WS altitude had the greatest effect on SHAP estimation. When a strong tailwind was present at low altitude, runways 07R and 07C were highly susceptible to go-arounds. The proposed META-DES with a pool of RF classifiers and SHAP for predicting aircraft go-around in WS events may be of interest to researchers in the field of air traffic safety. Full article

(This article belongs to the Special Issue Advances in Transportation Meteorology)

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19 pages, 5285 KiB

Open AccessArticle

Prediction and Interpretation of Low-Level Wind Shear Criticality Based on Its Altitude above Runway Level: Application of Bayesian Optimization–Ensemble Learning Classifiers and SHapley Additive exPlanations

by Afaq Khattak, Pak-Wai Chan, Feng Chen and Haorong Peng

Atmosphere 2022, 13(12), 2102; https://doi.org/10.3390/atmos13122102 - 15 Dec 2022

Cited by 10 | Viewed by 1672

Abstract

Low-level wind shear (LLWS) is a rare occurrence and yet poses a major hazard to the safety of aircraft. LLWS event occurrence within 800 feet of the runway level are dangerous to approaching and departing aircraft and must be accurately predicted. In this [...] Read more.

Low-level wind shear (LLWS) is a rare occurrence and yet poses a major hazard to the safety of aircraft. LLWS event occurrence within 800 feet of the runway level are dangerous to approaching and departing aircraft and must be accurately predicted. In this study, first the Bayesian Optimization–Ensemble Learning Classifiers (BO-ELCs) including Adaptive Boosting, Light Gradient Boosting Machine, Categorical Boosting, Extreme Gradient Boosting, and Random Forest were trained and tested using a dataset of 234 LLWS events extracted from pilot flight reports (PIREPS) and weather reports at Hong Kong International Airport. Afterward, the SHapley Additive exPlanations (SHAP) algorithm was utilized to interpret the best BO-ELC. Based on the testing set, the results revealed that the Bayesian Optimization–Random Forest Classifier outperformed the other BO-ELCs in accuracy (0.714), F1-score (0.713), AUC-ROC (0.76), and AUR-PRC (0.75). The SHAP analysis found that the hourly temperature, wind speed, and runway 07LA were the top three crucial factors. A high hourly temperature and a moderate-to-high wind speed made Runway 07LA vulnerable to the occurrence of critical LLWS events. This research was a first attempt to forecast the criticality of LLWS in airport runway vicinities and will assist civil aviation airport authorities in making timely flight operation decisions. Full article

(This article belongs to the Special Issue Advances in Transportation Meteorology)

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18 pages, 2599 KiB

Open AccessArticle

Analyses on the Multimodel Wind Forecasts and Error Decompositions over North China

by Yang Lyu, Xiefei Zhi, Hong Wu, Hongmei Zhou, Dexuan Kong, Shoupeng Zhu, Yingxin Zhang and Cui Hao

Atmosphere 2022, 13(10), 1652; https://doi.org/10.3390/atmos13101652 - 10 Oct 2022

Cited by 7 | Viewed by 1820

Abstract

In this study, wind forecasts derived from the European Centre for Medium-Range Weather Forecasts (ECMWF), the National Centers for Environmental Prediction (NCEP), the Japan Meteorological Agency (JMA) and the United Kingdom Meteorological Office (UKMO) are evaluated for lead times of 1–7 days at [...] Read more.

In this study, wind forecasts derived from the European Centre for Medium-Range Weather Forecasts (ECMWF), the National Centers for Environmental Prediction (NCEP), the Japan Meteorological Agency (JMA) and the United Kingdom Meteorological Office (UKMO) are evaluated for lead times of 1–7 days at the 10 m and multiple isobaric surfaces (500 hPa, 700 hPa, 850 hPa and 925 hPa) over North China for 2020. The straightforward multimodel ensemble mean (MME) method is utilized to improve forecasting abilities. In addition, the forecast errors are decomposed to further diagnose the error sources of wind forecasts. Results indicated that there is little difference in the performances of the four models in terms of wind direction forecasts (DIR), but obvious differences occur in the meridional wind (U), zonal wind (V) and wind speed (WS) forecasts. Among them, the ECMWF and NCEP showed the highest and lowest abilities, respectively. The MME effectively improved wind forecast abilities, and showed more evident superiorities at higher levels for longer lead times. Meanwhile, all of the models and the MME manifested consistent trends of increasing (decreasing) errors for U, V and WS (DIR) with rising height. On the other hand, the main source of errors for wind forecasts at both 10 m and isobaric surfaces was the sequence component (SEQU), which rose rapidly with increasing lead times. The deficiency of the less proficient NCEP model at the 10 m and isobaric surfaces could mainly be attributed to the bias component (BIAS) and SEQU, respectively. Furthermore, the MME tended to produce lower SEQU than the models at all layers, which was more obvious at longer lead times. However, the MME showed a slight deficiency in reducing BIAS and the distribution component of forecast errors. The results not only recognized the model forecast performances in detail, but also provided important references for the use of wind forecasts in business departments and associated scientific researches. Full article

(This article belongs to the Special Issue Advances in Transportation Meteorology)

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18 pages, 2592 KiB

Open AccessArticle

Potential Effect of Air Pollution on the Urban Traffic Vitality: A Case Study of Nanjing, China

by Yang Cao, Hao Wu, Hongbin Wang, Duanyang Liu and Shuqi Yan

Atmosphere 2022, 13(10), 1592; https://doi.org/10.3390/atmos13101592 - 29 Sep 2022

Viewed by 1544

Abstract

Studies on the vitality of urban residents’ daily commuting and air pollution are scarce. Based on the cell phone mobile signaling data, urban air quality observation data, and urban transportation infrastructure environment data of Nanjing in 2019, and through the panel regression model [...] Read more.

Studies on the vitality of urban residents’ daily commuting and air pollution are scarce. Based on the cell phone mobile signaling data, urban air quality observation data, and urban transportation infrastructure environment data of Nanjing in 2019, and through the panel regression model and the standard deviation ellipse analysis (SDE) to measure the impact of air pollution on residents’ daily traffic vitality, we construct the survey panel matrix data system with streets as spatial units. Through SDE and panel regression model analysis, we measured the restraining effect of air pollution on the traffic vitality. The scope of the traffic vitality area SDE was found to shrink as the air quality index (AQI) increases. The study found three main characteristics: (1) Under different transportation models and different location conditions, there are obvious differences in traffic vitality. The entire city presents a trend of “northeast-southwest” axial expansion in the spatial pattern of the traffic vitality. Compared with the urban core area, the traffic vitality of residents in the north-south areas of Nanjing’s periphery has declined significantly. (2) The inhibitory effect of air pollution on public traffic vitality and self-driving traffic vitality are differences. Approximately one-tenth of traffic activities may be inhibited by air pollution. The weakening of traffic vitality greatly reduces the city’s ability to attract and gather people, materials, and resources. (3) The inhibitory effect of air pollution on traffic vitality is heterogeneous under different transportation infrastructure environments. The higher the public transportation station density and public transportation frequency of the street, the more obvious the suppression effect of air pollution. The higher the parking density, station accessibility, road intersections density, and transportation facility diversity, the lower the suppression effect of air pollution. This study elucidates the relationship among air pollution, the transportation infrastructure environment, and the traffic vitality, and provides significant guidelines for optimizing the organization of elements in the transportation infrastructure environment, thereby mitigating the inhibitory effect of air pollution on traffic vitality. Full article

(This article belongs to the Special Issue Advances in Transportation Meteorology)

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18 pages, 4645 KiB

Open AccessArticle

Attention-Based BiLSTM Model for Pavement Temperature Prediction of Asphalt Pavement in Winter

by Shumin Bai, Wenchen Yang, Meng Zhang, Duanyang Liu, Wei Li and Linyi Zhou

Atmosphere 2022, 13(9), 1524; https://doi.org/10.3390/atmos13091524 - 18 Sep 2022

Cited by 3 | Viewed by 1621

Abstract

Pavement temperature is the main factor determining road icing, and accurate and timely pavement temperature prediction is of significant importance to regional traffic safety management and preventive maintenance. The prediction of pavement temperature at the micro-scale has been a challenge to be tackled. [...] Read more.

Pavement temperature is the main factor determining road icing, and accurate and timely pavement temperature prediction is of significant importance to regional traffic safety management and preventive maintenance. The prediction of pavement temperature at the micro-scale has been a challenge to be tackled. To solve this problem, a bidirectional extended short-term memory network model based on the attention mechanism (Att-BiLSTM) was proposed to improve the prediction performance by using the time series features of pavement temperature and meteorological factors. Pavement temperature data and climatic data were collected from a road weather station in Yunnan, China. The results show that the MAE, MSE, and MAPE of the proposed Att-BiLSTM model were 0.330, 0.339, and 10.1%, respectively, which were better than the other baseline models. It was shown that 93.4% of the predicted values had an error less than 1 °C, and 82.1% had an error less than 0.5 °C, indicating that the proposed Att-BiLSTM model enables significant performance improvement. In addition, this paper quantified and analyzed the effects of parameters such as the size of the sliding window, the number of hidden layer neurons, and the optimizer on the performance of the prediction model. Full article

(This article belongs to the Special Issue Advances in Transportation Meteorology)

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Review

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19 pages, 1033 KiB

Open AccessReview

Observations and Forecasts of Urban Transportation Meteorology in China: A Review

by Shoupeng Zhu, Huadong Yang, Duanyang Liu, Hongbin Wang, Linyi Zhou, Chengying Zhu, Fan Zu, Hong Wu, Yang Lyu, Yu Xia, Yanhe Zhu, Yi Fan, Ling Zhang and Xiefei Zhi

Atmosphere 2022, 13(11), 1823; https://doi.org/10.3390/atmos13111823 - 02 Nov 2022

Cited by 6 | Viewed by 2141

Abstract

Against the backdrop of intensified global warming, extreme weather events such as dense fog, low visibility, heavy precipitation, and extreme temperatures have been increased and enhanced to a great extent. They are likely to pose severe threats to the operation of urban transportation [...] Read more.

Against the backdrop of intensified global warming, extreme weather events such as dense fog, low visibility, heavy precipitation, and extreme temperatures have been increased and enhanced to a great extent. They are likely to pose severe threats to the operation of urban transportation and associated services, which has drawn much attention in recent decades. However, there are still plenty of issues to be resolved in improving the emergency meteorological services and developing targeted urban transportation meteorological services in modern cities. The present review briefly illustrates the current cutting-edge developments and trends in the field of urban transportation meteorology in China, including the establishment of observation networks and experiments and the development of early warning and prediction technologies, as well as the related meteorological commercial services. Meanwhile, reflections and discussions are provided in terms of the state-of-the-art observation channels and methods and the application of numerical model forecasts and artificial intelligence. With the advantages of various advanced technologies from multiple aspects, researchers could further expand explorations on urban transportation meteorological observations, forecasts, early warnings, and services. Associated theoretical studies and practical investigations are also to be carried out to provide solid scientific foundations for urban transportation disaster prevention and mitigation, for implementing the action of meteorological guarantees, and for the construction of a high-quality smart society. Full article

(This article belongs to the Special Issue Advances in Transportation Meteorology)

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