Impact Of Severe Weather Research Articles

Modelling Severe Weather Impact on Traffic Mobility and Evaluating Cross-Regional Applicability to Functionally Distinct Segments in Cold Region Highway Networks

This study in Alberta, Canada, focuses on winter traffic modeling, crucial for transportation agencies in cold regions. By gathering data from six Weigh-in-motion (WIM) sites, models were developed and tested for three vehicle types. Notably, the research assessed the transferability of models between different highway segments, indicating successful transfer of coefficients to varied road functionalities. This suggests that agencies can optimize model structures for specific vehicle classes, enhancing usability without additional monitoring sites. Overall, the study highlights the feasibility of improving winter traffic models and their applicability across diverse road segments, benefiting transportation planning and management in cold climates.

Resilience assessment and recovery of airport departure flights under severe weather

In order to ensure the overall performance of the airport under severe weather, scientifically evaluate the resilience of the airport's flight operations, improve flight recovery capabilities, and alleviate the impact of severe weather effectively. This article first gives the definition of airport departure flight operation. Starting from the performance of the airport departure flight operation system, it analyzes flight departure delay time, total departure delay time, departure flight normality rate and departure airport flight operation system comprehensive resilience index four indicators to evaluate the resilience changes of the system under severe weather conditions; put forward the concept of the performance recovery strategy of the airport departure flight operation system, and established the corresponding resilience model, and used the genetic algorithm to optimize the sequence of the delayed departure flights; finally, take the "721" heavy rain event in Beijing Capital International Airport in 2012 as an example to analyze the data, obtain the performance index and resilience index of the Capital Airport under the influence of heavy rain, and use the optimized departure flight schedule to compare and analyze the changes in airport departure flight operating system performance and resilience level. The results indicate that: under the influence of heavy rain, the comprehensive resilience index of the airport departure flight operation system decreased from 0.4573 to 0.0628 , and increased to 0.2223 after the rainstorm decreased; after the flight optimization and sequencing was carried out in the recovery stage of the airport departure flight operation performance, the total number of departure flights decreased. The delay time is reduced by 24.85%, the airport performance recovery speed is increased by 13.89%after optimization, and the minimum resilience index of the optimized airport departure flight operation system is increased by 13.38%, and under the same heavy rain, the system performance is given priority to restore to the initial state, indicating that the evaluation Indicators are valid.

Hybrid neural network-aided strong wind speed prediction along rail network

With the advent of networked rail operations and increasing risks caused by extreme weather conditions, there is an urgent need to enhance the safety early warning and emergency protection for rail under extreme wind environments. By deploying anemometers along the railway and monitoring the surrounding wind speeds, dispatchers can proactively take protective measures to mitigate the impact of severe weather on rail transportation. In particular, accurate time-series forecasting during periods when wind speeds exceed the threshold is critical for railway anomaly monitoring. This study employs Extreme Value Theory (EVT) to determine the threshold for strong winds. In order to identify high wind speeds, memory networks are used to memorize strong wind events in historical data. By combining the Gated Recurrent Unit (GRU) module with the attention mechanism, this study ultimately develops a hybrid neural network model in the prediction of wind speed. Based on real-world datasets of wind speeds along the Hangzhou-Haining intercity railway and along the Norway rail network via meteorological stations, the effectiveness of the hybrid neural network model has been verified by comparing base models. The proposed strong wind speed prediction has practical applications in enhancing early warning and rail safety under complex wind climate.

Predictive modeling of severe weather impact on individuals and populations using Machine Learning

In this work, Machine Learning (ML) techniques are used to develop tools capable of accurately predicting the impact of severe weather events. We use readily accessible predictors, including daily meteorological data, basic demographics, geographic and terrain features, along with the number of daily meteorological incidents reported to the emergency services. The model was built using disaggregated data from the 947 municipalities from the region of Catalonia between January 1, 2015 and June 30, 2021. Catalonia’s region is situated in the northeastern part of Spain along the Mediterranean Basin, and frequently experiences storms with strong winds and intense rainfall. In 2020, such weather events resulted in 64 injuries and damages amounting to approximately 70 million USD. The ML-based model presented in this study shows a predictive capacity for extreme weather risk superior to that of the daily meteorological warning system of the Meteorological Service of Catalonia. In addition, the model has been used to estimate how urbanization modifies the extreme weather impact. Given the expected increase in the intensity, frequency, and duration of extreme weather events in the context of global warming, the methodology presented in this work could be helpful in developing tools to assist emergency service managers and policy makers in making rapid and effective decisions.

Impact of Severe Winter Weather on Operations of a Radiation Oncology Department

IntroductionDuring winter of 2022, Western New York faced two major storms with blizzard conditions and record-breaking snowfall. The severe weather resulted in power outages, and travel bans. This study investigates the impact of these conditions on patient adherence to radiotherapy. Combining data from a large academic center and its satellite clinic, this single-center study is shedding light on the challenges faced by cancer care facilities during severe weather and proposing suggestions to prevent and mitigate harm done by severe weather. MethodsIn this study, data was collected using the MOSAIQ Record and Verify system (v. 2.81) to generate deidentified reports of scheduled and treated patients. The treatment adherence rate was calculated by dividing the number of patients treated by the total number of patients scheduled. Data was specifically collected for patients undergoing treatment on linear accelerators at a primary academic center and a satellite facility. The study focused on working days from November 1, 2022, to March 31, 2023, excluding weekends and holidays (as treatments are not routinely scheduled). Severe weather days were identified using advisories from the National Weather Service and local institution, including specific periods in November, December, and January. ResultsIn the study, 15,010 scheduled treatment visits were recorded across the academic center and the satellite clinic. The mean daily treatment adherence rate was 91.7%. Severe weather conditions led to a significant reduction in adherence, with rates dropping to 77.8%. Adherence rates during non-severe weather days were notably higher at 93.9%. Statistical analysis confirmed the substantial impact of severe weather on adherence (p<0.001). Severe weather had a more pronounced impact on the satellite clinic during periods of severe weather with absolute reduction in adherence rates of 21.9% vs. 15% in primary hospital). Moreover, adherence rates at the satellite clinic vs. primary hospital site was lower even under standard operating conditions (92.2% vs 94.0%, p<0.001). ConclusionAs a part of operational planning, it is important to be aware how severe weather can impact treatment adherence. The study findings underscore the importance of proactive measures to ensure patient access to healthcare services during adverse weather events and highlight the broader significance of incorporating Social Determinants of Health (SDOH) considerations into contingency planning for maintaining treatment continuity.

Multi-faceted spatio-temporal network for weather-aware air traffic flow prediction in multi-airport system

As one of the core modules for air traffic flow management, Air Traffic Flow Prediction (ATFP) in the Multi-Airport System (MAS) is a prerequisite for demand and capacity balance in the complex meteorological environment. Due to the challenge of implicit interaction mechanism among traffic flow, airspace capacity and weather impact, the Weather-aware ATFP (Wa-ATFP) is still a nontrivial issue. In this paper, a novel Multi-faceted Spatio-Temporal Graph Convolutional Network (MSTGCN) is proposed to address the Wa-ATFP within the complex operations of MAS. Firstly, a spatio-temporal graph is constructed with three different nodes, including airport, route, and fix to describe the topology structure of MAS. Secondly, a weather-aware multi-faceted fusion module is proposed to integrate the feature of air traffic flow and the auxiliary features of capacity and weather, which can effectively address the complex impact of severe weather, e.g., thunderstorms. Thirdly, to capture the latent connections of nodes, an adaptive graph connection constructor is designed. The experimental results with the real-world operational dataset in Guangdong-Hong Kong-Macao Greater Bay Area, China, validate that the proposed approach outperforms the state-of-the-art machine-learning and deep-learning based baseline approaches in performance. The case study of convective weather scenarios further proves the adaptability of the proposed approach.

Impact of Severe Weather on Operations of a Radiation Oncology Department

In 2022, two major storms in Western New York brought blizzard conditions and historic snowfall amounts to the region. Beyond the immediate impact to life and property, these weather events resulted in disruptions to cancer care as a result of hazardous travel conditions, and facility closures as a result of regional travel bans. We sought to understand the impact of these disruptive incidents on departmental operations to develop better contingency plans for future occurrences. The radiation treatment machine schedules for a large academic center in Western New York, as well as a neighboring affiliated satellite clinic were queried for daily treatment adherence rates from November 1, 2022 through February 4, 2023. We classified "severe weather days" based on travel advisories issued by the National Weather Service or from local county officials. Out of a total of 68 working days during the study period, 15 days were classified as having severe weather. We compared the percentage of scheduled patients treated (adherence rates) on severe weather days compared with days without severe weather. Statistical analysis was performed using a two-tailed Z-test for proportions. Under routine operating conditions, out of a total of 7045 scheduled treatment visits, we found a mean daily treatment adherence rate of 93.57%. On severe weather days, out of a total of 2093 scheduled treatments, we found a mean daily treatment adherence rate of 77.83%. Out of these 15 days, the satellite clinic closed for a total of 3 days, while the academic center closed for 1 day. Excluding the dates of facility closure, overall treatment adherence rate was 84.76% on severe weather days (Risk Ratio: 0.91). We found that the difference in treatment adherence rates between regular and severe weather days was statistically significant (p<0.001), even when excluding dates of facility closure. This study demonstrates a significant reduction in treatment adherence rates around severe winter storms, even when excluding dates of facility closure. We suggest that preparation for such incidents be a part of facility contingency planning. Weekend openings can be considered for closures ≥ 2 days, or sustained adherence rates of 85% or less for 3 or more days.

Towards nowcasting in Europe in 2030

AbstractThe increasing impact of severe weather over Europe on lives and weather‐sensitive economies can be mitigated by accurate 0–6 h forecasts (nowcasts), supporting a vital ‘last line of defence’ for civil protection and many other applications. Recognizing lack of skill in some complex situations, often at convective and local sub‐kilometre scales and associated with rare events, we identify seven recommendations with the aim to improve nowcasting in Europe by the national meteorological and hydrological services (NMHSs) by 2030. These recommendations are based on a review of user needs, the state of the observing system, techniques based on observations and high‐resolution numerical weather models, as well as tools, data and infrastructure supporting the nowcasting community in Europe. Denser and more accurate observations are necessary particularly in the boundary layer to better characterize the ingredients of severe storms. A key driver for improvement is next‐generation European satellite data becoming available as of 2023. Seamless ensemble prediction methods to produce enhanced weather forecasts with 0–24 h lead times and probabilistic products require further development. Such products need to be understood and interpreted by skilled forecasters operating in an evolving forecasting context. We argue that stronger co‐development and collaboration between providers and users of nowcasting‐relevant data and information are key ingredients for progress. We recommend establishing pan‐European nowcasting consortia, better exchange of data, common development platforms and common verification approaches as key elements for progressing nowcasting in Europe in this decade.

Fatalities related to sudden meteorological events across Central Europe from 2010 to 2020

Under current climate change, severe weather impacts human life in Central Europe, which is a relatively safe climate zone concerning the occurrence of the most violent weather events. This impact is projected to increase due to ongoing climate change. Therefore, this study, for the first time, discusses the death toll related to selected sudden severe weather phenomena in Central Europe in the 2010–2020 period with particular attention put to the death circumstances caused by the severe weather fatality, and their spatial and temporal distribution. In this study, severe wind, severe rainfall, lightning strikes, tornadoes and avalanches are considered. The data on phenomena occurrence were taken from the verified European Severe Weather Database. The study revealed that an average of 73 people died per year, and 365 people were hurt annually due to severe weather in the study period. Avalanches were found to be the deadliest phenomena, followed by severe wind, lightning and heavy rain. The majority of victims were killed during outdoor activities, such as spending free time outside or driving; however, the dominating death circumstances varied within the analysed domain and were different than those in the other regions of the world. This findings should be taken into account while creating national early-warning systems and better gathering severe weather impact data. Education and raising society awareness according to our results should be implemented as well.

The research of the contribution of big data in enhancing power battery safety

The use of oil and gas is the main cause behind the increased impact of severe weather over the past two decades. In the use of oil and natural gas, fuel powered vehicles account for a large part. Therefore, at present, countries are vigorously promoting the settlement of new energy vehicles. With the support of national policies, new energy manufacturers are springing up. There are also many large factories with good qualifications, but there are also many small factories mixed with other factories. With the increase of new energy vehicles, more and more traffic accidents caused by new energy vehicles, especially the self-destruction of new energy vehicles caused by power batteries. Therefore, this paper attempts to make suggestions and analysis on the production, charging, driving and handling of power batteries after accidents through big data monitoring.

Weather impact quantification on airport arrival on-time performance through a Bayesian statistics modeling approach

Compared with departures, predicting the weather impact on arrival delays is more challenging because of possible non-linear, cascading effects, and higher uncertainty. Existing weather impact studies are location-dependent and often neglect the impacts of dangerous phenomena. We propose a data-driven model for severe weather impact quantification on airport arrival on-time performance based on the Bayesian approach to address these issues. Our model considers the impact of the dangerous phenomenon by evaluating the mean shift and is flexible enough to be applied to different airports. Using two years’ worth of data (2017-2018) from the Hong Kong International Airport, we studied over 55,000 local meteorological reports and analyzed over 430,000 arrival flights. Across all three key performance metrics considered, a non-linear relationship with the weather score, akin to a phase transition, could be observed. This framework allows a comparison between the sensitivity of each airport’s arrival performance metric towards severe weather. Delay rate is the most sensitive metric, while cancellation rate is the least. For the impacts of dangerous phenomena, cumulonimbus has the most significant impact on the delay rate. Shower rainfall/cumulonimbus has a similar and vital impact on the mean arrival delay per hour. Because of its potential applications in different airports, this framework can provide a deeper insight into weather impact on air traffic networks.

Covering Synergistic Effects of Climate Change: Global Challenges for Journalism

ABSTRACT This writing positions the accompanying volume within a realm of complications of climate change journalism that operates amid recognized pressures of journalistic practice through notions of political economy, the social and cultural influences shaping news explanations, and critical interpretations of the current global crisis of a warming planet. Specifically, this volume is interested in how journalism may function among “synergistic effects” of climate change, the compounded impact of severe weather, social and political responses to changing global warming, and the often-unfortunate results and impacts on our environments as global communities attempt to address climate events already challenging for journalists to cover and the social and cultural outcomes associated with them.

Transient stability risk assessment framework incorporating circuit breaker failure and severe weather

ABSTRACT Due to the rising uncertainties in the power system and the need to develop a competitive electricity market, the risk of transient stability is an important issue to address. The traditional deterministic methods of transient stability assessment fail to consider the uncertain nature of input variables. Moreover, there is a dire requirement to incorporate the impact of circuit breaker failure and severe weather in the transient stability risk assessment procedure. Majority of transient stability risk research assumes that the circuit breaker will always trip upon fault detection; however, in practice, the circuit breaker may not operate due to internal fault. Thus, this paper proposes a transient stability risk assessment framework,incorporating circuit breaker failure and severe weather. It considers the impact of severe weather, in the form of wind storms. The concept of fragility curves is used to model this impact. The IEEE 14-bus and the IEEE 39-bus test systems were used to test and validate the effectiveness of the proposed approach. DIgSILENT PowerFactory was utilised for time-domain simulations for transient stability risk evaluation. The results show that it is vital to consider the circuit breaker failure and severe weather in the transient stability risk assessment procedure.

Optimal strategy selection framework for minimizing the economic impacts of severe weather induced power outages

Every year power outages cost billions of dollars and affect millions of people. In the U.S., historical data show that between 2000 and 2016, 75% of the outages (in terms of duration) were caused due to severe weather events. Due to climate change and aging of infrastructures, these weather-related events are getting more frequent. To mitigate the risk of economic losses from severe weather induced power outages, utilities need to invest heavily for building resilience against these events. This research has established a framework for risk-based decision making for identifying optimal risk reduction strategies to minimize the economic impact of severe weather induced power outages. The framework determines the economic impacts of severe weather induced power outages in terms of loss of Gross Domestic Product (GDP) for a state and utilizes a Benefit-Cost Analysis (BCA) based optimization technique to identify the optimal scope of different strategies which will maximize the benefits from them. The benefit has been defined as the reduction of expected GDP loss due to implementation of risk reduction strategies. The framework requires the users to identify strategies, their feasible scope, and cost. This has been illustrated through an example. The proposed framework can be effectively used by the investor-owned utilities for getting their resilience building projects approved from state utility regulatory commissions.

Augmentation of Severe Weather Impact to Far-Infrared Sensor Images to Improve Pedestrian Detection System

Pedestrian detection is an essential task for computer vision and the automotive industry. Complex systems like advanced driver-assistance systems are based on far-infrared data sensors, used to detect pedestrians at nighttime, fog, rain, and direct sun situations. The robust pedestrian detector should work in severe weather conditions. However, only a few datasets include some examples of far-infrared images with distortions caused by atmospheric precipitation and dirt covering sensor optics. This paper proposes the deep learning-based data augmentation technique to enrich far-infrared images collected in good weather conditions by distortions, similar to those caused by bad weather. The six most accurate and fast detectors (TinyV3, TinyL3, You Only Look Once (YOLO)v3, YOLOv4, ResNet50, and ResNext50), performing faster than 15 FPS, were trained on 207,001 annotations and tested on 156,345 annotations, not used for training. The proposed data augmentation technique showed up to a 9.38 mean Average Precision (mAP) increase of pedestrian detection with a maximum of 87.02 mAP (YOLOv4). Proposed in this paper detectors’ Head modifications based on a confidence heat-map gave an additional boost of precision for all six detectors. The most accurate current detector, based on YOLOv4, reached up to 87.20 mAP during our experimental tests.

Dangerous weather phenomena and their effect on en-route flight delays in Europe

Meteorological reports from Europe indicate that the number and intensity of summer thunderstorms are from year to year, gradually increasing. Climate change is seen as a difference in mean temperature and precipitation level and can lead to dangerous weather phenomena. This paper is driven by the idea to determine the impact of severe weather on air transport in Europe during the summer months. For the analysis, Performance Review Unit (PRU) Dashboard data from 2013 to 2019 were used. The more recent period was not considered due to the outbreak of the COVID-19 pandemic. The methodology covers the statistical analysis to confirm the theory. Research has shown that weather-related en-route delays are growing, influencing air transport’s future operation and economics. This paper also presents a medium-term forecast (3 years period) of the weather-related delays using the Exponential Triple Smoothing (ETS) Holt-Winters model. Based on the obtained outcomes, recommendations are presented in the conclusion.

Predicting impacts of weather-driven urban disasters in the current and future climate

Effective city operations depend on local weather conditions at the scale of critical urban infrastructure such as power and water distribution systems. This includes both routine and severe weather events. For example, with precipitation events, local topography and weather influence water runoff and infiltration, which directly affect flooding as well as drinking water quality and availability. The impact of such events creates issues of public safety. Thus, the availability of highly localized weather model predictions focused on public safety and operations of infrastructure can mitigate the impact of severe weather. This is especially true if the lead time for the availability of such predictions enables proactive allocation and deployment of resources to minimize recovery time from severe events. Typically, information at such a scale is simply not available. Hence, the ability of municipalities to proactively respond to these events is limited. Available continental- or regional-scale weather models are not appropriately matched to the temporal or spatial scale of such operations. While near-real-time assessment of observations of current weather conditions may have the appropriate geographic locality, by its very nature it is only directly suitable for reactive response. To address this gap, we use state-of-the-art physical weather models at the spatial scale of the city's infrastructure to avoid this mismatch in predictability. Model results are coupled to data-driven stochastic models to represent the actionable prediction of weather (business) impacts. In some cases, an intermediate physical model may be required to translate predicted weather into the phenomena that lead to such impacts. We have applied these ideas to several cities with a diversity of impacts and weather concerns and show how this coupled model methodology enables prediction of storm impacts on local infrastructure. We also discuss how this concept can be extended to a climate scale in order to evaluate the potential localized impacts of a warming planet and the effectiveness of strategies being used to mitigate such impacts.

Analysis of Flood Vulnerability and Transit Availability with a Changing Climate in Harris County, Texas

Hurricanes and other extreme precipitation events can have devastating effects on population and infrastructure that can create problems for emergency responses and evacuation. Projected climate change and associated global warming may lead to an increase in extreme weather events that results in greater inundation from storm surges or massive precipitation. For example, record flooding during Hurricane Katrina or, more recently, during Hurricane Harvey in 2017, led to many people being cut off from aid and unable to evacuate. This study focuses on the impact of severe weather under climate change for areas of Harris County, TX that are susceptible to flooding either by storm surge or extreme rainfall and evaluates the transit demand and availability in those areas. Future risk of flooding in Harris County was assessed by GIS mapping of the 100-year and 500-year FEMA floodplains and most extreme category 5 storm tide and global sea level rise. The flood maps have been overlaid with population demographics and transit accessibility to determine vulnerable populations in need of transit during a disaster. It was calculated that 70% of densely populated census block groups are located within the floodplains, including a disproportional amount of low-income block groups. The results also show a lack of transit availability in many areas susceptible to extreme storm surge exaggerated with sea level rise. Further study of these areas to improve transit infrastructure and evacuation strategies will improve the outcomes of extreme weather events in the future.

The Impact of Severe Weather and Climate Change on Lean Supply Chains

The Impact of Severe Weather and Climate Change on Lean Supply Chains

A radar-based centroid tracking algorithm for severe weather surveillance: identifying split/merge processes in convective systems

Nowcasting tools using radar data are essential when researching severe weather phenomena. Identifying thunderstorms and characterising their properties are usually the main steps to follow in order to make a good short-term forecast. These analyses are normally used to feed early warning systems in many countries, helping to reduce the impact of severe weather. Over the last few decades, research in this meteorological field has increased significantly, defining different methodologies that have worked well for many severe events. Most of these methodologies are based on a prior 3D identification of the thunderstorms in question, by applying intensity and size thresholds and obtaining their characteristics, allowing their centroids to be tracked. In some cases, the thresholds for identification and the distance established to track the centroid are too restrictive, which results in the incorrect identification of individual cells in multicell systems, or even means that the algorithm is unable to distinguish when an anomalous movement takes place. This paper presents a new type of 3D cell identification and tracking, based on the algorithm developed by Rigo and Llasat (2004). This last algorithm is currently used in the Meteorological Service of Catalonia (SMC), and consists of a nowcasting methodology based on centroid tracking. In the new version, a new two-step methodology to construct 3D radar cells has been proposed. A double reflectivity thresholding method plus an overlapping technique have been applied for a better definition of the convective structures. This makes it possible to identify possible changing processes such as splitting or merging within the same thunderstorm. Furthermore, a dynamic distance threshold technique has been adopted to correctly track cells with anomalous movements. An in-depth study has been carried out for a severe weather case in which a multicell system presented a complex merging and splitting process that was correctly forecasted using the proposed algorithm.