Weather Scenarios Research Articles

Probabilistic Air Traffic Complexity Analysis Considering Prediction Uncertainties in Traffic Scenarios

This article presents a methodology for analyzing probabilistic air traffic complexity by integrating prediction uncertainties in convective weather scenarios. With the Performance Review Unit (PRU) model as a base, this method modifies the original framework by incorporating a weather-related complexity indicator. The approach was tested in Austrian airspace using ensemble weather forecasts and historical flight plan data. The results demonstrated that a probabilistic model effectively assesses traffic complexity and captures trends in complexity over time, providing greater reliability in high-complexity sectors. Validation revealed a strong alignment between simulator complexity values and probabilistic complexity, especially in sectors characterized by dense data distributions. In contrast, sectors with more elongated distributions tended to overestimate complexity. Quantitative analysis indicated that the error between the probabilistic mean complexity and the simulator complexity values ranged from 12% to 23%, with higher errors in sectors with lower complexity. This validation confirmed the model’s ability to predict complexity trends, thereby assisting flow manager positions (FMPs) in traffic flow and airspace management. Overall, this study demonstrated that probabilistic complexity assessment provides a deeper understanding of traffic behaviour, facilitating more effective air traffic flow management in uncertain and dynamic conditions.

Hybrid local energy markets: Incorporating utility function-based peer risk attributes, renewable energy integration, and grid constraints analysis

This study proposes a novel hybrid Local Energy Market (LEM) trading framework meticulously designed for efficient energy trading among distribution network participants. The proposed LEM hybrid modeling framework aims to maximize social welfare by considering the behavior of market participants and incorporating network constraints into the transactional framework. To address the consumption behavior of market participants, the concept of the utility function with peer risk attributes is employed, differentiating between flexible and inflexible demands. Additionally, it adeptly manages various generation types, including diesel generators, wind and solar generators, and storage, each characterized by a unique cost function. The investigation extends to the analysis of the Japan Electric Power Exchange market (JEPX), taking into account the residential electricity demand in Tokyo, Japan, by comparing with community-based (CB) and peer-to-peer (P2P) markets and scrutinizing market player behavior across multiple scenarios. These scenarios include comparisons of different market structures, variations in prices, considerations of grid constraints, and diverse weather scenarios. The proposed framework undergoes rigorous evaluation utilizing the IEEE 33-bus and 69-bus standard test systems, with results demonstrating its practicality, superiority of the proposed market compared to the CB and P2P markets, and providing valuable insights into the intricate interplay between market players’ behavior and the dynamics of diverse market structures, price fluctuations, grid constraints, and weather scenarios.

A Data-driven Race Strategy Tool for Olympic Sailing Competitions

_ Venue-specific training has, over the years, proven to be a key asset for sailing teams performing at major championships and at the Olympics. A comprehensive understanding of the environmental features and possible weather scenarios in an outdoor sport like sailing can provide athletes with a significant strategic advantage. At the same time, GPS tracking is becoming a readily available technology that athletes use both in training and during races to analyse their own and their competitor's performance. This work couples environmental and meteorological data with GPS tracks for Olympic sailing classes, linking weather features with strategic decisions on the race track. We propose a greedy algorithm to search for an optimal route based on weather forecasts to present the best strategy prior to the race. Our results show the potential of this approach to provide valuable decision support for athletes in Olympic sailing competitions, demonstrated for the 470 Olympic class. Keywords Olympic sailing; path planning; data-driven methods; meteorology; A* algorithm

Assessing the Impact of Adverse Weather on Performance and Safety of Connected and Autonomous Vehicles

Connected and Autonomous Vehicles (CAVs) might significantly enhance the transportation system by improving safety, accessibility, efficiency, and sustainability. However, a major challenge lies in ensuring CAVs can operate properly under diverse weather conditions, which have already proven to impair human driving capabilities. This pioneering study aims to bridge a crucial research gap by comprehensively assessing the performance of CAVs on traffic operations and safety across varying weather scenarios. Using microscopic traffic simulation in VISSIM and the Surrogate Safety Assessment Model (SSAM), this study evaluates key metrics, including average speed, delay, number of stops, travel time, and number of conflicts for different CAV market penetration rates. The analysis spans 21 scenarios under clear, light rain, heavy rain, and foggy conditions within a selected urban corridor in the United Arab Emirates. The results showed that the average speed rose by 55% in clear weather, while the average delay, the number of stops, travel time, and the number of accidents decreased by 50%, 50%, 95%, and 68%, respectively. In light rain, the average speed improved by 43%, while the average delay, number of stops, travel time, and the number of accidents reduced by 43%, 56%, 96%, and 74%, respectively. The average speed increased by 82% under heavy rain, while the average delay, the number of stops, the travel time, and the number of accidents all fell by 62%, 68%, 96%, and 74%, respectively. In fog, the average speed rose by 32%, while the average delay, average stop number, travel time, and the number of accidents decreased by 33%, 47%, 90%, and 83%, respectively. Overall, this paper highlights the need for resilient CAV systems adaptable to diverse environmental conditions. It helps advance the understanding of how CAVs can be optimized for safety and efficiency in urban settings, contributing to sustainable transportation solutions. It provides insights into the challenges and innovative approaches for CAV deployment in adverse weather, laying a foundation for future research and the broader implementation of these technologies in urban mobility. Doi: 10.28991/CEJ-2024-010-09-019 Full Text: PDF

Risk Analysis of Small LPG Storage Tank Explosion in the School Cafeteria

This study aimed to determine the best safety measures in case of accidental gas leakage, fire, or explosion in the LPG storage tank of a domestic school kitchen by analyzing the ranges potentially affected by toxicity, overpressure, and radiant heat. Using the Areal Locations of Hazardous Atmospheres (ALOHA) program, the range of damage impact of gas leakage, toxicity owing to VCE and boiling liquid expanding vapor explosion (BLEVE), overpressure, and radiant heat were analyzed for a 1,000 kg-capacity small LPG storage tank installed in the kitchen. It was classified into worst-case and alternative (local weather) scenarios, depending upon the weather conditions, and analyzed by dividing into 50% and 100% storage, as per the difference in storage. The observed risk of toxicity placed the entire school area at risk of death or disability. The risk of overpressure posed a threat to buildings in the entire school area and around the school. The risk of blasts revealed a risk of death up to a distance of 135 m, and the farther regions were at risk of second-degree burns. Additionally, the risks of toxicity and overpressure are affected by weather conditions and LPG storage, whereas blasts are primarily affected by LPG storage with minimal impact from weather conditions.

Climate change mitigation and adaptation in Spanish office stock through cool roofs

In a context of climate change, the cooling period of buildings is increasing every year in Mediterranean countries. This will become a relevant issue when existing buildings are retrofitted. Cool roofs have a passive cooling effect on a building and can contribute to decreasing the annual cooling demand. In office buildings, thermal comfort has a considerable influence on workers’ performance. In these buildings cooling energy demands tend to be high due to the high internal thermal loads. This paper describes annual energy simulations of Spanish office stock to evaluate the potential energy savings of cool roofs within the context of building retrofitting. The energy demand and thermal comfort of representative office buildings is compared before and after the application of a cool roof coating, in the present time and in the year 2050, for all climate areas of Spain, under the most optimistic and most pessimistic future weather scenarios. Results showed that the cooling energy demand of Spanish office stock can be potentially reduced by 25 % when a cool roof is applied, and the annual energy demand for heating and cooling can be cut by 6 %, 11 % and 12 % in the present, the optimistic future scenario and the pessimistic future scenario, respectively.

Investigating the Potential of Using Mixdown Altitudes to Forecast Peak Wind Gusts

Abstract Forecast models based on the gust factor, the ratio of peak gust to sustained wind speed, have shown promise in predicting peak wind gusts in recent years. These models assume that turbulent vertical transport forced by wind flowing over upstream terrain mixes high-momentum air aloft down to the surface. A recently constructed database of hourly peak gusts, together with approximately coincident, nearby upper-air wind observations, is used to identify mixdown altitudes, the altitudes from which peak gusts descend, during different weather scenarios at 16 locations across the United States. Median mixdown altitudes generally ranged from 50 to 450 m AGL with occasional exceptions, particularly for convective gusts and at mountainous locations where terrain effects are likely to amplify gusts. A mixdown model in which surface peak gusts are predicted by obtaining forecast upper-air winds within this altitude interval was developed and tested. Our results suggest that a mixdown model methodology for forecasting peak gusts may be feasible at locations and during weather conditions where terrain-forced turbulent mixing is the principal cause of wind gusts.

SD-YOLO-AWDNet: A hybrid approach for smart object detection in challenging weather for self-driving cars

Several deep learning algorithms are currently focused on object detection in adverse weather scenarios for autonomous driving systems. However, these algorithms face challenges in real-time scenarios, leading to a reduction in detection accuracy. To tackle these issues, this paper introduces a lightweight object detection model named Self Driving Cars You Only Look Once Adverse Weather Detection Network (SD-YOLO-AWDNet), derived from enhancements to the YOLOv5 algorithm. The model incorporates four progressive improvement levels within the YOLOv5 framework. This includes integrating C3Ghost and GhostConv modules in the backbone to enhance detection speed by reducing computational overhead during feature extraction. To address potential accuracy issues arising from these modules, Depthwise-Separable Dilated Convolutions (DSDC) are introduced, striking a balance between accuracy and parameter reduction. The model further incorporates a Coordinate Attention (CA) module in the GhostBottleneck to enhance feature extraction and eliminate unnecessary features, improving precision in object detection. Additionally, a novel “Focal Distribution Loss” replaces CIoU Loss, accelerating bounding box regression and loss reduction. Test dataset experiments demonstrate that SD-YOLO-AWDNet outperforms YOLOv5 with a 54% decrease in FLOPs, a 52.53% decrease in model parameters, a 2.24% increase in mAP, and a threefold improvement in detection speed.

Assessing thermal resilience to overheating in a Belgian apartment: impact of building parameters

Globally and in European Union, climate change and heatwaves (HWs) challenges the thermal resilience performance (buildings’ ability to withstand and recover from a shock) of airtight, highly insulated buildings with no cooling strategies. Ensuring thermal resilience in buildings is crucial for maintaining habitability amid climate change uncertainties. This study, under the framework of IEA EBC Annex 80, aims to explore the impact of design parameters (thermal mass, window to wall ratio (WWR), operation of solar shading and natural night ventilation) on the thermal resilience in a Belgian apartment. Although a number of recent studies explored impact of building design on overheating, there is still a lack in the state-of-the-art exploring and varying ranges of the design parameters that impact the thermal resilience performance of apartments during short-term shocks such as heatwaves. In order to reach the aim of this study, building energy simulations were performed. Initially, the default apartment was evaluated during current weather scenario varying the parameters. Results showed overheating risk in default apartment during current weather scenario. Then the worst, improved and, the optimized variation of the building is evaluated against 6 days HW. The apartments’ thermal resilience performance assessment conclude that high window-to-wall ratios (WWR >30%) without solar shading and lighter thermal mass decrease thermal resilience during shocks, highlighting WWR’s significance. For short-term overheating events, heavy thermal mass improves the thermal resilience by pro-longing the absorptivity time (withstanding capability) of the excess heat. However, if excess heat is absorbed by the building (higher WWR resulting in higher solar gains), lighter thermal mass enhances the thermal resilience performance (recovery capability) by flushing out the excess heat. Solar shading and passive cooling strategy like natural night ventilation aids in temperature management, improving the buildings’ thermal resilience performance by prolonging the absorptivity and speeding the recovery from the shock.

DDR: A network of image deraining systems for dark environments

In the domain of computer vision, addressing the degradation of image quality under adverse weather conditions remains a significant challenge. To tackle the challenges of image enhancement and deraining in dark settings, we have integrated image enhancement and deraining technologies to develop the DDR (Dark Environment Deraining Network) system. This specialized network is designed to enhance and clarify images in low-light conditions compromised by raindrops. DDR employs a strategic divide-and-conquer approach and an apt network selection to discern patterns of raindrops and background elements within images. It is capable of mitigating noise and blurring induced by raindrops in dark settings, thus enhancing the visual fidelity of images. Through testing on real-world imagery and the Rain LOL dataset, this innovative network offers a robust solution for deraining tasks in dark conditions, inspiring advancements in the performance of computer vision systems under challenging weather scenarios. The research of DDR provides technical and theoretical support for improving image quality in dark environment.

Some like it hot: Seed thermal threshold variation in obligate seeding Acacia pulchella along a climate gradient

Dormancy in seeds is a key persistence mechanism for many flowering plants. Physically dormant (PY) seeds have water impermeable seed coats, and in fire-prone systems a common mechanism for dormancy release is fire-induced soil heating. However, the thermal thresholds innate to seeds with PY may be influenced by vegetation, climate, and fire regimes, varying substantially between populations of the same species. To investigate intraspecific variation of thermal thresholds in PY seeds, we sampled obligate seeding Acacia pulchella (Fabaceae) which produces PY seeds. Sampling was undertaken from 13 populations across a climate gradient of rainfall and temperature, and between two vegetation communities in fire-prone Mediterranean-type ecosystems of south-west Western Australia. To test a range of weather and fire-induced soil heating dormancy-break scenarios, we conducted dry heat shock experiments between 40 and 140 °C for 10 min and scored germination for 16 weeks. We created population-specific thermal performance curves and extracted the dormancy release temperature at which 50 % of the seeds had germinated (DRT50), the optimum dormancy-breaking temperature to stimulate maximum germination (T0), and the lethal temperature at which 50 % of the seeds were killed (LT50). Generalised linear models were used to examine relationships between thermal thresholds and possible vegetation, climate, and fire regime drivers of intraspecific variation in seed traits. We found that thermal thresholds differed between vegetation communities, with thresholds consistently higher in forest-type ecosystems compared to open woodland, and the influence of climate varied significantly between the two communities. Seeds from Jarrah Forest populations had a DRT50 16.0 °C higher, a T0 9.7 °C higher, and LT50 7.8 °C higher than seeds from Banksia woodlands. A high rate of non-dormancy was identified in one population that had lost fire in its system and displayed significant germination after both summer and fire-related temperatures. The PY thermal thresholds modelled here provide insight into the strong influence of variable soil heating as a function of vegetation and fuel dynamics in fire-prone environments. Our findings highlight the significant intraspecific variation for this species and suggest that fire-induced soil heating generated by vegetation characteristics may be an overlooked element of fire regimes shaping seed traits.

Enhancing 3D object detection by using neural network with self-adaptive thresholding

Robust 3D object detection remains a pivotal concern in the domain of autonomous field robotics. Despite notable enhancements in detection accuracy across standard datasets, real-world urban environments, characterized by their unstructured and dynamic nature, frequently precipitate an elevated incidence of false positives, thereby undermining the reliability of existing detection paradigms. In this context, our study introduces an advanced post-processing algorithm that modulates detection thresholds dynamically relative to the distance from the ego object. Traditional perception systems typically utilize a uniform threshold, which often leads to decreased efficacy in detecting distant objects. In contrast, our proposed methodology employs a Neural Network with a self-adaptive thresholding mechanism that significantly attenuates false negatives while concurrently diminishing false positives, particularly in complex urban settings. Empirical results substantiate that our algorithm not only augments the performance of 3D object detection models in diverse urban and adverse weather scenarios but also establishes a new benchmark for adaptive thresholding techniques in field robotics.

Enhancing 3D object detection by using neural network with self-adaptive thresholding

Robust 3D object detection remains a pivotal concern in the domain of autonomous field robotics. Despite notable enhancements in detection accuracy across standard datasets, real-world urban environments, characterized by their unstructured and dynamic nature, frequently precipitate an elevated incidence of false positives, thereby undermining the reliability of existing detection paradigms. In this context, our study introduces an advanced post-processing algorithm that modulates detection thresholds dynamically relative to the distance from the ego object. Traditional perception systems typically utilize a uniform threshold, which often leads to decreased efficacy in detecting distant objects. In contrast, our proposed methodology employs a Neural Network with a self-adaptive thresholding mechanism that significantly attenuates false negatives while concurrently diminishing false positives, particularly in complex urban settings. Empirical results substantiate that our algorithm not only augments the performance of 3D object detection models in diverse urban and adverse weather scenarios but also establishes a new benchmark for adaptive thresholding techniques in field robotics.

A Systematic Approach to Map and Evaluate the Wildfire Behavior at a Territorial Scale in the Northwestern Iberian Peninsula

In the current context of extreme wildfires, understanding fire behavior at a territorial level has proven crucial for territory planning. This type of analysis is usually conducted by analyzing past wildfire statistics. In this study, we forego the past information related to wildfires and analyze, instead, the behavior of the entire territory in the face of wildfires. This allows for the distribution of ignition points to be systematized and for typical and atypical weather scenarios to be considered. This analysis relies on the use of wildfire simulation software. Ignition points used for the simulations were distributed using a systematic 1 × 1 km grid throughout the whole study area. Wildfires were simulated for each ignition point using eight different weather scenarios representing both typical and atypical weather conditions. The fire behavior on the territory was analyzed using rate of spread and intensity parameters for each simulated wildfire. It was observed that this territory is extremely prone to large wildfires both in typical and atypical weather conditions and that there is a tendency for extreme behaviors to develop. Some features were identified as prevention issues that ought to be addressed. This study develops a strategy to evaluate, in a systematic manner, the response of the territory to the threat of wildfires.

A preliminary investigation of the small rockfall triggering conditions along a road network in Slovenia

AbstractRockfalls are among the natural hazards that endanger infrastructure, cause major economic disruptions, and threaten human lives. These phenomena result from long-term geological processes such as tectonic rock deformation or weathering, but the actual rockfall itself occurs suddenly, usually without warning. The triggering mechanisms are complex and difficult to trace. In this study, we investigated the triggering mechanisms of more than 2100 small rockfalls that occurred in 2021 in Slovenia, Europe, along the 51,000 km long national road network. We analyzed their spatial and temporal characteristics, as well as the triggering mechanisms of their occurrence, based on different weather scenarios. Multiple data mining methods were used to investigate triggering conditions, and scenario analyses were used to understand the triggering mechanisms. Most small rockfalls in 2021 were recorded in winter and spring at a density of 1 rockfall per 10 km2. The results show that winter weather conditions have the greatest potential for triggering small rockfalls in Slovenia, both in terms of spatial extent and frequency of triggering weather conditions. The analysis showed that summer storms are an important but indeterminant factor for the occurrence of small rockfalls. Among the scenarios tested, the winter scenario, which includes a combination of antecedent precipitation, snowmelt, and freeze‒thaw cycles a few days before the event, was able to predict 72% of the events in the colder season.

Distributed Photovoltaic Output Prediction Using Machine Learning and Numerical Model Combined Method

Abstract In recent years, distributed photostatic (PV) has developed rapidly. Accurate prediction of distributed PV output can improve the reliable, efficient and economical grid-connected operation of distributed PV, and enhance the reliability of power supply. In this study, we first collected the datasets of distributed PV output and centralized PV station output in Dongan County, Yongzhou City, Hunan Province. Then, we analyzed the relationship between distributed PV station output and centralized PV output under different weather conditions, and constructed the centralized PV output prediction curve by combining machine learning and numerical prediction model, and finally obtained the distributed PV output prediction of the whole county. The results show that the correlation coefficient changes between the output of distributed PV stations and that of centralized PV stations under different weather scenarios (sunny, cloudy, etc.), but they all show a good consistency (the correlation coefficient of more than 0.6 accounts for more than 95% of the PV stations), indicating that the output change of centralized PV stations in one county can be used to characterize the output change of distributed stations. The distributed PV output prediction model built by LSTM and numerical model combined method has achieved a prediction accuracy of 90.4% for a single station and 94.31% for the whole county within 24 hours, both of which are 4%˜5% higher than the requirements of national standards. The model can be further extended to forecast the distributed PV output of the whole province.

Optimizing ORB-SLAM For Varied Weather Conditions Using Genetic Algorithm

Abstract In the rapidly evolving domains of self-driving cars, the resilience of Simultaneous Localization and Mapping (SLAM) algorithms to varying environmental conditions remains a critical challenge. This paper leverages the CARLA simulator to create comprehensive datasets that encompass an array of weather scenarios, ranging from clear sky to complex combinations of fog and rain, during both daytime and nighttime. The primary objective of this study is to optimize the performance of ORB-SLAM2 under these harsh conditions, improving resilience and robustness against different weather conditions. The evaluation is conducted using the Root Mean Square Error (RMSE) as the key metric. Genetic Algorithm (GA) is developed to optimize the parameters of ORB-SLAM. The GA aims to reduce the RMSE for each unique weather situation. The results show a significant improvement in ORB-SLAM’s performance and resilience, contributing to its potential applications in the broader landscape of autonomous systems and intelligent mobility networks.

Marine heatwaves alter competition between the cultured macroalga Gracilariopsis lemaneiformis and the harmful bloom alga Skeletonema costatum

Seaweed cultivation can inhibit the occurrence of red tides. However, how seaweed aquaculture interactions with harmful algal blooms will be affected by the increasing occurrence and intensity of marine heatwaves (MHWs) is unknown. In this study, we run both monoculture and coculture systems to investigate the effects of a simulated heatwave on the competition of the economically important macroalga Gracilariopsis lemaneiformis against the harmful bloom diatom Skeletonema costatum. Coculture with G. lemaneiformis led to a growth decrease in S. costatum. Growth and photosynthetic activity (Fv/Fm) of G. lemaneiformis was greatly reduced by the heatwave treatment, and did not recover even after one week. Growth and photosynthetic activity of S. costatum was also reduced by the heatwave in coculture, but returned to normal during the recovery period. S. costatum also responded to the stressful environment by forming aggregates. Metabolomic analysis suggests that the negative effects on S. costatum were related to an allelochemical release from G. lemaneiformis. These findings show that MHWs may enhance the competitive advantages of S. costatum against G. lemaneiformis, leading to more severe harmful algal blooms in future extreme weather scenarios.

Evaluating Thermal Comfort and Overheating Risks in A Social Housing Prototype: As Built Versus Retrofit Scenarios

Climate change has highlighted the importance of thermal comfort and its health-related outcomes, particularly for the most vulnerable members of society living in social housing. Due to their vulnerable living conditions, low-income people are more exposed to negative outcomes of overheating and cold indoor temperatures in buildings. Previous studies suggest that there is a significant risk of overheating in retrofitted buildings both for the current and future weather scenarios. The UK government has introduced new building regulations to assess and limit the risk of overheating in new buildings; however, there is still a need to assess and improve conditions for existing and retrofitted properties. This study aims to evaluate the effect of retrofit strategies on thermal comfort and the risk of overheating in social housing under current and future climatic conditions. A typical case study building was simulated in DesignBuilder to assess thermal comfort conditions for upgraded building fabric to Part L of the UK building regulations and Passive House standards. The summer results were analyzed according to CIBSE TM59 while the Predicted Mean Vote index (PMV) was used for winter analysis. Findings revealed that the south-facing bedrooms are most exposed to overheating. Risk of overheating significantly increased for the future weather scenarios by up to 10 times while winter thermal comfort improved for the retrofitted scenarios.

TRAFFIC SIGN RECOGNITION IN CHALLENGING WEATHER CONDITIONS USING CONVOLUTIONAL NEURAL NETWORKS

The ability of autonomous driving systems to recognize traffic signs in a variety of environmental conditions is crucial to their reliability. This study uses convolutional neural networks (CNNs) to provide a novel method for improving the accuracy of traffic sign recognition systems under challenging weather situations. The research focuses on developing a CNN-based model that is trained using the augmented German Traffic Sign Recognition Benchmark (GTSRB) dataset, which contains over 50,000 labeled images of road signs across 43 categories. To attempt to overcome the limitations of traditional CNN models under adverse environmental conditions, this work presents adaptive feature extraction layers that are especially made to reduce visibility problems brought on by rain, fog, and snow. By taking a comprehensive approach, the model uses advanced data augmentation methods to simulate different weather scenarios, greatly increasing the diversity of the training dataset. Through an analysis of theoretical and practical aspects, the study demonstrates how CNNs enhance the accuracy and efficiency of road sign detection systems in a different weather condition. Research evaluates the model's effectiveness using metrics such as precision, recall, and F1-score, proving its capability to reduce false positives and accurately detect relevant road sign instances. Additionally, the paper highlights the importance of careful dataset preparation and augmentation, model optimization, and training enhancements to improve the performance of road sign detection systems. The results of this research offer benefits for intelligent transport systems, autonomous driving, and road safety, pointing towards further advancements in precise and dependable road sign recognition technology.