Heavy Snow Research Articles

Examining partial proportional odds model in analyzing severity of high-speed railway accident

Purpose The operation safety of the high-speed railway has been widely concerned. Due to the joint influence of the environment, equipment, personnel and other factors, accidents are inevitable in the operation process. However, few studies focused on identifying contributing factors affecting the severity of high-speed railway accidents because of the difficulty in obtaining field data. This study aims to investigate the impact factors affecting the severity of the general high-speed railway. Design/methodology/approach A total of 14 potential factors were examined from 475 data. The severity level is categorized into four levels by delay time and the number of subsequent trains that are affected by the accident. The partial proportional odds model was constructed to relax the constraint of the parallel line assumption. Findings The results show that 10 factors are found to significantly affect accident severity. Moreover, the factors including automation train protection (ATP) system fault, platform screen door and train door fault, traction converter fault and railway clearance intrusion by objects have an effect on reducing the severity level. On the contrary, the accidents caused by objects hanging on the catenary, pantograph fault, passenger misconducting or sudden illness, personnel intrusion of railway clearance, driving on heavy rain or snow and train collision against objects tend to be more severe. Originality/value The research results are very useful for mitigating the consequences of high-speed rail accidents.

Observational study of a severe snowfall avalanche over a state in North India in November 2019 using GIS

Heavy snowfall in the first week of November 2019 caused snow avalanche that resulted in huge loss in the form of lives and properties over Kashmir Valley in North India. About 17 people were killed including 11 soldiers by the snow avalanche resulting from heavy snowfall. Data from seven stations of India Meteorological Department (IMD) have been used to study the snowfall events. These stations include Kupwara, Gulmarg, Sher-e-Kashmir University of Agricultural Sciences & Technology (SKUAST), Pahalgam, Kokernag, Qazigund and Konibal. Kokernag recorded the highest snowfall of about 120 mm on November 8. SKUAST recorded snowfall of about 80 mm on November 8. Heavy snowfall started on November 6 and lasted up to November 8, 2019. Gulmarg, Kokernag and Kupwara reported cumulative snowfall of more than 100 mm during November 6–8, 2019. These stations are located in hilly region. Heavy snowfall combined with steep slope resulted in heavy snow avalanche causing heavy damage. An army camp situated in northern Gulmarg was swept away due to avalanche resulting in four causalities. Heavy snowfall also affected agriculture, horticulture, transportations and economy of the valley.

국내 물 산업체의 기후변화 대응현황과 재무적 리스크 분석

This study investigated the private sector’s climate change risk, focusing on the financial sector and its risk management, specifically the case of the water industry in Korea. The water industry tends to be particularly vulnerable to climate change and requires adaptation strategies to address potential risks. This study quantified the potential financial risk to the water industry from four extreme weather events: heat waves, cold waves, heavy rain and heavy snow. These events are classified according to the Intergovernmental Panel on Climate Change (IPCC) scenarios, RCP 4.5 and RCP 8.5, using a business decision supporting tool, the Climate Change Risk Assessment System (CRAS), which was developed by Korea Environment Institute. The results of analysis revealed that small- and medium-sized enterprises (SMEs) are more vulnerable in responding to climate change risk than large enterprises, that SME manufacturers in particular are the most vulnerable in responding to climate change risk. Of heat waves, cold waves, heavy rain and heavy snow, the greatest climate change-related financial risk was from heat waves. Lost sales due to climate change risk ranged from 152.9 to 210.7 billion won per year on average, according to the RCP 4.5 scenario. Furthermore, when the RCP 8.5 scenario was considered, the scale of financial risk increased to between 250.9 and 345.8 billion won per year. This study provides quantitative information that helps to increase awareness on the financial risks of the water industry from climate change and to explore measures for business adaptation.

The Future of the Arctic: What Does It Mean for Sea Ice and Small Creatures?

The warming of our planet is changing the Arctic dramatically. The area covered by sea-ice is shrinking and the ice that is left is younger and thinner. We took part in an expedition to the Arctic, to study how these changes affect organisms living in and under the ice. Following this expedition, we found that storms can more easily break the thinner ice. Storms form cracks in the sea ice, allowing sunlight to pass into the water below, which makes algal growth possible. Algae are microscopic “plants” that grow in water or sea ice. Storms also brought thick heavy snow, which pushed the ice surface below the water. This flooded the snow and created slush. We discovered that this slush is another good habitat for algae. If Arctic sea ice continues to thin, and storms become more common, we expect that these algal habitats will become more important in the future.

An Examination of the Impact of Grid Spacing on WRF Simulations of Wintertime Precipitation in the Mid-Atlantic United States

AbstractA large set of deterministic and ensemble forecasts was produced to identify the optimal spacing for forecasting U.S. East Coast snowstorms. WRF forecasts were produced on cloud-allowing (~1-km grid spacing) and convection-allowing (3–4 km) grids, and compared against forecasts with parameterized convection (>~10 km). Performance diagrams were used to evaluate 19 deterministic forecasts from the winter of 2013–14. Ensemble forecasts of five disruptive snowstorms spanning the years 2015–18 were evaluated using various methods to evaluate probabilistic forecasts. While deterministic forecasts using cloud-allowing grids were not better than convection-allowing forecasts, both had lower bias and higher success ratios than forecasts with parameterized convection. All forecasts were underdispersive. Nevertheless, forecasts on the higher-resolution grids were more reliable than those with parameterized convection. Forecasts on the cloud-allowing grid were best able to discriminate areas that received heavy snow and those that did not, while the forecasts with parameterized convection were least able to do so. It is recommended to use convection-resolving and (if computationally possible) to use cloud-allowing forecast grids when predicting East Coast winter storms.

Modeled Climate Responses to Realistic Extremes of Northern Hemisphere Spring and Summer Snow Anomalies

AbstractNorthern Hemisphere (NH) snow cover extent (SCE) has diminished in spring and early summer since the 1960s. Historical simulations from phase 5 of the Coupled Model Intercomparison Project (CMIP5) estimated about half as much NH SCE reduction as observed, and thus underestimated the associated climate responses. This study investigates atmospheric responses to realistic decreasing snow anomalies using multiple ensemble transient integrations of climate models forced by observed light and heavy NH snow cover years, specifically satellite-based observations of NH SCE and snow water equivalent from March to August in 1990 (light snow) and 1985 (heavy snow), as a proxy for the trend. The primary atmospheric responses to March–August NH snow reduction are decreased soil moisture, increased surface air temperature, general tropospheric warming in the extratropics and the Arctic, increased geopotential heights, and weakening of the midlatitude jet stream and eddy kinetic energy. The localized response is maintained by persistent increased diabatic heating due to reduced snow anomalies and resulting soil moisture drying, and the remote atmospheric response results partly from horizontal propagation of stationary Rossby wave energy and also from a transient eddy feedback mechanism. In summer, atmospheric responses are significant in both the Arctic and the tropics and are mostly induced by contemporaneous snow forcing, but also by the summer soil moisture dry anomaly associated with early snow melting.

CFD simulation of snow redistribution on a bridge deck: Effect of barriers with different porosities

Snow redistribution caused by snowdrift may worsen the traffic condition of bridge decks in snowy regions, hence it needs to be considered in bridge designs. In this study, a quasi-steady numerical method is adopted to simulate snowdrift on a typical bridge deck with barriers. The whole process of snowdrift on bridge decks is divided into several phases to simulate the variation in profile of snowpack, and a two-dimensional (2D) steady Computational Fluid Dynamics (CFD) simulation method is employed in each phase. To investigate the effect of barriers on the snow redistribution on bridge decks, the simulation results of wind velocity field, wall friction velocity, snow concentration field and snow redistributions are analyzed in detail. Generally, as the barrier porosity decreases, the total amount of snow erosion decreases due to the blocking effect of the barrier, but the decrease is limited when the barrier porosity is below 37.5%. Heavy snow accumulation mainly occurs behind the windward barrier when the barrier porosity is below 62.5%. To avoid snow accumulation, barrier porosity above 75% is suggested as the optimal choice for improving the traffic condition of bridge decks.

딥러닝 기반의 동풍 예측 및 강수 분류

Understanding the characteristics of the easterly-related weather phenomena in the eastern coast in Korean Peninsula is very important to analyze abnormal atmospheric phenomena such as heavy rain, heavy snow, and hot-dry wind. As data science techniques have steadily improved, a data driven prediction models are becoming more powerful in the quantitative forecasting weather. In this paper, we apply the LSTM based deep learning method to predict the velocity of the easterly wind around the Korean peninsula. Bi-directional data shape of input data and cascaded LSTM structure are proposed. The modified LSTM based method for prediction of easterly wind is experimented in years form 2013 to 2017 for the Korean Peninsula and East Sea using ERA5 data. Experiments of precipitation classification for Gangwon and Gyeongsang area are executed in years form 2008 to 2017.

Failure mechanisms and cracking performance of T-shaped SCC beam-column connections at top floor: Test results and FE modeling

In recent decades, self-compacting concrete (SCC) has gradually gained popularity based on its unique workability without any internal and external vibration. SCC development must ensure an acceptable level of balance between deformability and stability by following the characteristics of materials and the mix proportions. In the case of the structural performance of reinforced concrete (RC) structures throughout the earthquake, beam-column connections behavior is one of the most common causes of failures. On the other hand, T-shaped beam-column connections on the top floor have a critical condition based on the different types of live loads. As an alternative, SCC with specific properties includes workability and resilience against segregation, which is a great solution to improving the behavior of RC connections in a seismic area. In this paper, experimentally and numerically, investigation of six T-shaped SCC beam-column connections through different reinforcing bars ratio (ρ) are presented to better understand their behavior during the gravitational load like heavy snow, particularly in terms of failure mechanisms and cracking performance. In the experimental stage, firstly, the mechanical specifications of SCC have been examined in terms of slump flow, V funnel, J ring, and L box tests. Secondly, the load-displacement performance of T-shaped beam-column connections at the top floor has been reported in terms of bonding behavior, ductility, and energy dissipation capacity. The displacement ductility of the experimental results are decreased from 12.4 to 3.5 while the ratio of the reinforcing bars was increased from %15 to %66 of the balanced section, and the energy dissipations capacity was increased by increasing the reinforcing ratios until %56 of the balanced section then started to decrease. In the theoretical part of the study, a three-dimensional nonlinear finite element method (FEM) model (ABAQUS) was employed along with the load-deflection curves were reported to compare the experimental results with the numerical output. The test results demonstrate, employing SCC as a type of concrete that has enough workability in RC connections had successful fulfillment in terms of ductility, load-carrying capacity, and energy dissipation.

Vehicle Detection and Tracking in Adverse Weather Using a Deep Learning Framework

Vehicle detection and tracking play an important role in autonomous vehicles and intelligent transportation systems. Adverse weather conditions such as the presence of heavy snow, fog, rain, dust or sandstorm situations are dangerous restrictions on camera’s function by reducing visibility, affecting driving safety. Indeed, these restrictions impact the performance of detection and tracking algorithms utilized in the traffic surveillance systems and autonomous driving applications. In this article, we start by proposing a visibility enhancement scheme consisting of three stages: illumination enhancement, reflection component enhancement, and linear weighted fusion to improve the performance. Then, we introduce a robust vehicle detection and tracking approach using a multi-scale deep convolution neural network. The conventional Gaussian mixture probability hypothesis density filter based tracker is utilized jointly with hierarchical data associations (HDA), which splits into detection-to-track and track-to-track associations. Herein, the cost matrix of each phase is solved using the Hungarian algorithm to compensate for the lost tracks caused by missed detection. Only detection information (i.e., bounding boxes with detection scores) is used in HDA without visual features information for rapid execution. We have also introduced a novel benchmarking dataset designed for research in applications of autonomous vehicles under adverse weather conditions called DAWN. It consists of real-world images collected with different types of adverse weather conditions. The proposed method is tested on DAWN, KITTI, and MS-COCO datasets and compared with 21 vehicle detectors. Experimental results have validated effectiveness of the proposed method which outperforms state-of-the-art vehicle detection and tracking approaches under adverse weather conditions.

A Review of the Measures to Enhance Power Systems Resilience

Rare and extreme climate events may result in wide power outages or blackouts. The concept of power system resilience has been introduced for focusing on high-impact and low-probability (HILP) events such as a hurricane, heavy snow, and floods. Power system resilience is the ability of a system to reduce the likelihood of blackout or wide power outages due to HILP events. Indeed, in a resilient power system, as the severity of HILP events increases, the rate (but not the amount) of unserved loads diminishes. Suitable measures for managing power system resilience can be classified into three categories in terms of time, known as “resilience-based planning,” “resilience-based response,” and “resilience-based restoration.” The most widely used approaches, methods, and techniques in each of these categories, as well as the future trends for improving the power system resilience are reviewed in this article. The challenges of resilience in power systems with high penetration of renewable energy sources are also discussed in each of these categories.

Non-Parametric Association Rules Mining and Parametric Ordinal Logistic Regression for an In-Depth Investigation of Driver Speed Selection Behavior in Adverse Weather using SHRP2 Naturalistic Driving Study Data

Human error is considered to be one of the major causes of crashes, especially in inclement weather. Although many studies have investigated the effect of adverse weather on traffic safety and operations, there is a lack of research into the differences in driving behavior and performance during adverse weather, particularly at a trajectory level. With this research gap in mind, this study presents a novel approach for an in-depth investigation of driver speed selection behavior in adverse weather utilizing trajectory-level data acquired from the SHRP2 Naturalistic Driving Study using a promising association rules data mining technique. The preliminary analysis revealed that drivers reduced their speeds by 3.9% in the presence of light rain, by 10.2% in heavy rain, 15.2% in light snow, 29.8% in heavy snow, 1.8% with distant fog, and 7.4% with near fog. The findings from the association rules mining approach indicated that driving more than 5 mph above the speed limit was closely associated with clear weather as well as young and inexperienced drivers; whereas a reduction in speed to more than 5 mph below the speed limit was closely associated with snowy road surfaces combined with affected visibility. These findings are also in line with the results from the ordered logistic regression, which revealed that drivers were 1.4 times more likely to reduce their speeds in light rain, 1.7 times in heavy rain, 4.3 in light snow, 12.2 in heavy snow, 1.7 with distant fog, and 2.0 with near fog. The findings from this study provide an unprecedented opportunity to develop a Human-in-the-Loop Variable Speed Limit algorithm.

Application of Machine learning Algorithms in Autonomous Vehicles Navigation System

Autonomous vehicles are the future of transportation and it also expected to become a fully-fledged reality within a decade. The major giants in the automotive industry like Toyota with Microsoft and Amazon, Mercedes with Bosch, Audi with Huawei, etc. are hard pressing their transition from conventional vehicle to autonomous vehicles. The state of Karnataka, for instance, had approximately 205,200 registered taxis higher than Madhya Pradesh 174,900 registered cabs from 2014 to 2015. This results in higher traffic congestion, pollution and high fuel consumption due to unorganized driving practice. As a consequence, this presents a great deal of opportunities for autonomous cars as they can significantly reduce the accident rate and improve effective and stress free parking, optimal running time, fuel economy etc. It also reduces traffic congestion as all the autonomous cars can be synchronized together through cloud. But the crucial part is their navigation system. Although a number of sophisticated forms of technology like such as radars, lasers, and high definition cameras for mapping, localization, obstacle detection, etc, are used, autonomous vehicles are still trying to reach perfection to navigate precisely under uninformed terrains and dynamics obstacles on the way like pedestrians, erratic drivers, traffic and climatic conditions. Events like heavy snow and rain, improper lane marking makes it hard to detect an obstacle on the road for evasive maneuver within limited response time period. Machine learning is one of the fast growing technologies that provide optimal solution to overcome the challenges in the autonomous navigation system. This paper focuses on the application of different machine learning algorithms and compares the use, challenge and scope of the same. The paper also focuses on the future direction of machine learning with reference to autonomous navigation system.

A Review on Handicap Sections and Situations to Improve Driving Safety of Automated Vehicles

An automated vehicle performs self-driving by utilizing information gathered through sensors attached to the vehicle. Sensor accuracy is thus mentioned as the major technology for enhancing driving safety. However, since urban centers are replete with sections and situations that handicap driving such as sensor recognition limitations and failures, it is necessary to conduct a study that prepares for driving handicaps. As such, this study aims to derive the sections and situations where driving safety is depreciated and review those problems via an analytic hierarchy process (AHP) analysis on driving handicap factors. The analysis result showed that the importance of these handicap situations is high, and it was confirmed that it is necessary to first review off-road sections, environmental factors (heavy rain or snow), merge sections, and sections with poor lane conditions. The result of this research has significance in reviewing road sections and potential situations that require primary verification for securing the driving safety of automated vehicles. It is expected to be utilized in the relevant studies as a basic study.

Thermodynamics of Fast Ice off the Northeast Coast of Greenland (79°N) Over a Full Year (2012–2013)

AbstractNorske Øer Ice Barrier (NØIB) is a vast region of fast ice off northeast Greenland. To understand its thermodynamics, an ice mass balance buoy (IMB) was deployed in late August 2012. The IMB operated for a full year, providing unique year‐round observations of air, snow, ice, and upper ocean. Here, we examine atmospheric conditions and snow and sea ice evolution, derive the ocean heat flux at the deployment site, and investigate the effect of snow on the sea ice mass balance. Although an extremely low surface air temperature of −52°C was recorded at the IMB site, the sum of freezing‐degree days (4,848 K·d) is similar to that in the central Arctic. The maximum snow depth 0.73 m (on 29 May 2013, when ice was 2.19 m thick) is thicker than typically observed on level Arctic landfast ice and drift ice, and thick snow appears common in this region. Ocean heat flux was large in autumn and early winter (>7 W m−2), attributed in part to local solar heating of surface waters in summer and advected ocean heat later in the year. The combination of high ocean heat flux and thick snow resulted in a net loss of ice at the ice bottom over the year. A one‐dimensional sea ice model suggests that the thick snow cover limits bottom ice growth significantly. The heavy snow load could potentially have contributed 0.29 m snow ice; however, we cannot unambiguously prove this happened.

2018年2月の北陸地方における大雪の被害と影響に関する一考察 ―金沢市・福井市を対象として―

2018年2月の北陸地方における大雪の被害と影響に関する一考察 ―金沢市・福井市を対象として―

Dynamical response of atmospheric circulation to below-normal East Sea sea surface temperatures associated with heavy snowfall in eastern Korea

Prior studies have shown that above-normal sea surface temperatures (SSTs) enhance snowfall over Korea. Here, we show that heavy snow is also associated with below-normal East Sea SSTs, and we investigate the dynamical response of the atmosphere to this surface condition using observations and numerical modeling. The results indicate that anomalous southeasterly/easterly winds are induced by heavy snowfall-related cold SST anomalies, and consequently, the moisture flux is converged. The existence of the southeasterly winds and the accompanied moisture flux convergence appear to be instrumental in producing the heavy snowfall events. The anomalous southeasterly/easterly winds associated with heavy snowfall-related cold SST anomalies reduce the climatological northwesterly/westerly winds, leading to relatively warm and wet conditions over the east coast of Korea that are favorable for forming and intensifying snowfall events in the region.

Coping with Risks from Natural Disasters on Farms in Wales – Does Insurance Have a Role?

SummaryRisks faced by agricultural producers come in many forms and can be classified in different ways. Similarly, tools to enable farmers to cope with risks are diverse. This article focuses on natural disasters, which seem set to increase in frequency with global warming, and the possible use of insurance to mitigate their effects. The specific example chosen is extreme weather incidents in Wales, where heavy snow and low temperatures in localised hill and mountain areas occasionally have substantial impacts on livestock numbers and the viability of farms there. Among the policy responses considered by the Welsh Government is the use of subsidised insurance. We review the attributes of such a scheme, set up in advance of extreme weather incidents, and what factors have prevented their establishment so far. A lack of statistical evidence on past farm‐level losses is only part of the explanation; and weather incidents are only one form of impact that has to be taken into account. Even if subsidised insurance can be made to work as a policy tool, this is not necessarily the most efficient use of public funds. Welfare support as used in Wales in 2013 or education and training in resilience to risk may be preferable.

A study of disaster preparedness of archives & museum in seismic zone - v, flood prone and conflict ridden Kashmir

PurposeThe purpose of this study is to develop a sense of urgency and utmost care for careful disaster management to encourage disaster preparedness for museums and archives.x In particular, this study examines the techniques and methods to be used for response and recovery from different disasters to save valuable resources.Design/methodology/approachA thorough review of literature was done to identify existing plans, strategies, preparedness and management of archives and museums. On the basis of the knowledge and experience gained, it was decided to use a descriptive method for this study. Among the different data collection modes available in the descriptive method, the study framed a structured questionnaire for the collection of data. Thereafter, the questionnaire was tested for loopholes by conducting a pilot study. The questionnaire evolved as such was used to collect data from select archives and museums. The collected data has been tabulated and analyzed in tune with the set objectives. The findings of this study have been correlated with relevant findings and conclusions of previous studies in this domain to provide a better overview and sanctify the revelations further.FindingsFrom the current study, the authors find that the topological location of most of the institutions is near or on river banks which is the cause of concern at the time of heavy rains, after heavy snow fall and at the time of floods. It was also found that the majority of these institutions are located near highways which is also a serious concern in terms of noise, smoke, dust and vibrations produced by the plying of different types of transports on these highways. These kinds of unwanted disturbances create a negative atmosphere on the holdings, visitors and staff of these institutions.Although all of the institutions have apparently strong looking buildings that are made up of good material, the matter of concern is that only one institution (SPS museum) has a building which was specifically made for the purpose. As the state of Jammu and Kashmir is politically and geographically unstable, it is found that most of the institutions are prone to disasters like floods, earthquake and bomb threats which pose a threat to these valuable institutions and their valuable holdings.The most necessary and important parameter that is the training of the staff for disaster preparedness is not provided in majority of these institutions.Despite the fact that funds for majority of these institutions are provided by the government, the funds provided are insufficient for having fool-proof disaster preparedness and management.Research limitations/implicationsBecause of tremendous significance of archives and museums in propagation and preservation of historical and cultural identity of any nation, culture, ethnicity or nation state, the current study sheds light on the status of these important institutions in a very important part of Central Asia. Kashmir in past has been the cradle of knowledge evolution and sharing in the region. As such, it possesses some of the very rare manuscripts and artifacts preserved in her archives and museums.Practical implicationsAs this study is limited to select archives and museums of Jammu and Kashmir, the findings of this study may or may not represent the wider region of Central Asia.

Climate change induces multiple risks to boreal forests and forestry in Finland: A literature review.

Climate change induces multiple abiotic and biotic risks to forests and forestry. Risks in different spatial and temporal scales must be considered to ensure preconditions for sustainable multifunctional management of forests for different ecosystem services. For this purpose, the present review article summarizes the most recent findings on major abiotic and biotic risks to boreal forests in Finland under the current and changing climate, with the focus on windstorms, heavy snow loading, drought and forest fires and major insect pests and pathogens of trees. In general, the forest growth is projected to increase mainly in northern Finland. In the south, the growing conditions may become suboptimal, particularly for Norway spruce. Although the wind climate does not change remarkably, wind damage risk will increase especially in the south, because of the shortening of the soil frost period. The risk of snow damage is anticipated to increase in the north and decrease in the south. Increasing drought in summer will boost the risk of large‐scale forest fires. Also, the warmer climate increases the risk of bark beetle outbreaks and the wood decay by Heterobasidion root rot in coniferous forests. The probability of detrimental cascading events, such as those caused by a large‐scale wind damage followed by a widespread bark beetle outbreak, will increase remarkably in the future. Therefore, the simultaneous consideration of the biotic and abiotic risks is essential.