• Journal of the Korean Society for Aviation and Aeronautics
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• v.21 no.4
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• pp.106-111
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• 2013

The heavy snow event occurred on JAN 4, 2010 brought huge disaster such as Gimpo International Airport runway closed, heavy delays of other airport, and property damage of 16 billion won. Though this heavy snow event is involved in the general synoptic scale heavy snow forecast, it recorded too much snow amount and longer duration than expected. To explain this unusual event, we used the conveyor belt theory. By combining the synoptic scale heavy snow forecast and the conveyor belt theory, the characteristics of heavy snow event was well explained.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.6
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• pp.77-93
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• 2022

In this study, we present an algorithm that analyzes the risk by reflecting regional characteristics for factors affected by direct and indirect damage from heavy-snow. Factors affected by heavy-snow damage by 29 regions are selected as influencing variables, and the concept of sensitivity is derived through the relationship with the amount of damage. A snow damage risk prediction model was developed using a machine learning (XGBoost) algorithm by setting weather conditions (snow cover, humidity, temperature) and sensitivity as independent variables, and setting the risk derived according to changes in the independent variables as dependent variables.

• Journal of the Society of Disaster Information
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• v.13 no.2
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• pp.199-212
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• 2017

Rapid roadway snow removal is significantly important due to difficult occurrence estimation of heavy snowfall disasters by global warming and climate change. Local governments of S. Korea have snow removal equipments and vehicles based on past experiences without considering snowfall and roadway characteristics. The objective of this research is to develop the demand estimation procedure for snow removal equipments and vehicles based on regional snowfall and roadway characteristics. This research first classifies regional snowfall characteristics using KMO's ten-year snowfall data. Second, roadway snow removal length is computed for local governments. Real possession data is compared with demand estimation of snow removal equipments & vehicles for each local government with roadway snow removal scenarios. Finally, required demands of snow removal equipments & vehicles are predicted by concerning regional snowfall amount and required snow removal hours. Results from this research are used for developing heavy snowfall disaster management policies for optimal demands and snow removal routes of 229 local governments.

• International Journal of Highway Engineering
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• v.19 no.3
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• pp.83-90
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• 2017

PURPOSES : In this study, systematic road snow-removal capabilities were estimated based on previous historical data for road-snowremoval works. The final results can be used to aid decision-making strategies for cost-effective snow-removal works by regional offices. METHODS : First, road snow-removal historical data from the road snow-removal management system (RSMS), operated by the Ministry of Land, Infrastructure and Transport, were employed to determine specific characteristics of the snow-removal capabilities by region. The actual owned amount and actual used amount of infrastructure were analyzed for the past three years. Second, the regional offices were classified using K-means clustering into groups "close" to one another. Actual used snow-removal infrastructure was determined from the number of snow-removal working days. Finally, the correlation between the de-icing materials used and infrastructure was analyzed. Significant differences were found among the amounts of used infrastructure depending on snowfall intensity for each regional office during the past three years. RESULTS:The results showed that the amount of snow-removal infrastructure used for low heavy-snowfall intensity did not appear to depend on the amount of heavy snowfall, and therefore, high variation is observed in each area. CONCLUSIONS:This implies that the final analysis results will be useful when making decisions on snow-removal works.

• Journal of the Korean Association of Geographic Information Studies
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• v.18 no.1
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• pp.1-12
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• 2015

This study is to analyze the heavy snowfall vulnerable area of snow load design criteria for greenhouse, cattle shed and building using ground measured snow depth data and Terra MODIS snow cover area(SCA). To analyze the heavy snowfall vulnerable area, Terra MODIS satellite images for 12 years(2001-2012) were used to obtain the characteristics of snow depth and snow cover areas respectively. By comparing the snow load design criteria for greenhouse(cm), cattle shed($kg/m^2$), and building structure($kN/m^2$) with the snow depth distribution results by Terra MODIS satellite images, the facilities located in Jeolla-do, Chungcheong-do, and Gangwon-do areas were more vulnerable to exceed the current design criteria.

• Journal of the Society of Disaster Information
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• v.11 no.1
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• pp.23-34
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• 2015

This Study analyzed heavy snow properties according to the area that was based by winter weather properties and the damage data by the heavy snow among each local government of Chungcheongbuk-do. The result of analysis, Jecheon-si and Boeun-gun are represented the highest dangerous regions by potential degree of risk by average amount of snowfall for 35 years. But, the potential degree of risk by maximum amount of snowfall for 35 years is different with it. Cheongju-si and Youngdong-gun, Goesan-gun, Boeun-gun are represented the highest dangerous regions. Examining the frequency of regions with potential danger factors according to the characteristics of heavy snowfall, Boeun-gun and Jecheon-si, Goesan-gun, Youngdong-gun, Cheongju-si is derived the highest dangerous regions in Chungcheongbuk-do.

• Journal of Korea Water Resources Association
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• v.52 no.12
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• pp.1001-1009
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• 2019

Due to recent increasing heavy snow in Korea, the damage caused by heavy snow is also increasing. In Korea, there are many efforts including establishing disaster prevention measures to reduce the damage throughout the country, but it is difficult to establish the design criteria due to the characteristics of heavy snow. In this study, snowfall frequency analysis was performed to estimate design snow depths using observed snow depth data at Jinju, Changwon and Hapcheon stations. The conventional frequency analysis is sometime limted to apply to the snow depth data containing zero values which produce unrealistc estimates of distributon parameters. To overcome this problem, this study employed mixed distributions based on Lognormal, Generalized Pareto (GP), Generalized Extreme Value (GEV), Gamma, Gumbel and Weibull distribution. The results show that the mixed distributions produced smaller design snow depths than single distributions, which indicated that the mixed distributions are applicable and practical to estimate design snow depths.

• Journal of Wetlands Research
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• v.25 no.4
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• pp.345-352
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• 2023

Recently, the risk of snow disasters has been increasing South Korea. The damages of heavy snow were categorized into direct and indirect. Direct damage is usually the collapse of buildings as houses, greenhouse or barns. Indirect damage is various, for example, traffic congestion, traffic acident, drop damage, and so on. In South Korea, direct damage is severe in rural area, mosty collapse of greenhouse or barns. However, indirect damage such as traffic accident is mostly occurred in urban area. Therefore, the regional characteristics should be considered when vulnerability is evaluated. Therefore, in this study, the PSR and DPSIR method were applied by regional scale in South Korea. The PSR evaluation method is divided into pressure, state, and reaction index. however, the DPSIR evaluation method is divided into Driving force, Pressure, State, Impact, and Response index. the DPSIR evaluation method is divided into Driving force, Pressure, State, Impact, and Response index. Data corresponding to each indicator were collected, and the weight was calculated using the entropy method to calculate the snowfall vulnerability index by regional scale in South Korea. Calculated heavy snow damage vulnerabilities from the two methods were compared. The calculated vulnerabilities were validated using the recent snow damage in South Korea from 2018 to 2022. Snow vulnerability index calculated using the DPSIR method showed more reliable results. The results of this study could be utilized as an information to prepare the mitigation of heavy snow damage and to establish an efficient snow removal response system.

• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.2
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• pp.75-84
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• 2021

As the weather changes become frequent, weather disasters are increasing, causing more damage to plastic greenhouses. Among the damage caused by various disasters, damage by snow to the greenhouse takes a relatively long time, so if an alarm system is properly prepared, the damage can be reduced. Existing greenhouse design standards and snow warning systems are based on snow depth. However, even in the same depth, the load on the greenhouse varies depending on meteorological characteristics and snow density. Therefore, this study aims to secure the structural safety of greenhouses by developing sensors that can directly measure snow loads, and analysing the warning criteria for load using a stochastic model. Markov chain was applied to estimate the failure probability of various types of greenhouses in various regions, which let users actively cope with heavy snowfall by selecting an appropriate time to respond. Although it was hard to predict the precise snow depth or amounts, it could successfully assess the risk of structures by directly detecting the snow load using the developed sensor.

• Journal of Environmental Science International
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• v.16 no.12
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• pp.1439-1449
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• 2007

Heavy snowfall was occurred over the south-western part of the Korean Peninsula called as Honam Districts, on two days from 21 December 2005. The development mechanism of snowfall and its characteristics were analysed using observation and numerical data provided by Korea Meteorological Administration. In comparison with other years Arctic air mass developed and maintained during all December 2005 due to active planetary waves with three branches. And jet streams at lower and higher levels make easy development of snow convection cells. Especially thermal low induced by mesoscale heat and dynamic sources, also help the developments of convection cells in strong ascension. The understanding the relation between synoptic and mesoscale circumstance, therefore, is also important to predict the heavy snowfall and to prevent the disaster.