• Journal of the Korean Geographical Society
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• v.38 no.2
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• pp.173-183
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• 2003

This study aims to understand the patterns of spatial distribution of snowfall by Siberian High in the Honam region in Korea. In the Honam region, Siberian High induces snowfall dominantly. There is a huge amount of snowfall in the westward of the Noryung mountain ranges to the Wert coast in the Honam region affected by northwesterly wind directly from the Siberian High. The westward of the Noryung mountain ranges such as a heavy snowfall region has a various pattern of distribution of snowfall due to topography. The coast region has a large amount of snowfall by sea effect. And, snowfall amount is decreased from the coast to the inland plain. However, in front of mountain, snowfall is increase by reason of a forced ascending air to the mountain. In general the region where frequently appear a cumuliform cloud has a large amount of snowfall. A cumuliform cloud is frequent in the mountainous region in inland, the coast, and the inland plain in order Snowfall is intense in the coast and the mountainous region, and weak in the inland plain. In the mountainous region, a cumuliform cloud induced tv a forced ascending air by reason of topography generates snowfall mostly. This fact is the main difference with snowfall in the mountainous region and the coast region. In the result, in the Honam region, snowfall distribution and snowfall pattern are various, according to geographical climate factor such as sea and topography. The heavy snowfall region in the Honam region is divided into the coast region affected by sea effect and the mountainous region affected by topography effect.

• Journal of the Korean earth science society
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• v.38 no.3
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• pp.182-193
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• 2017

A typical snowfall pattern occurs over the east coastal region of the Korean Peninsula, known as the Yeongdong region. The precipitation over the Yeongdong region is influenced by the cold and dry northeasterly wind which advects over warm and moist sea surface of the East Sea of Korea. This study reveals the influence of large-scale factors, affecting local to remote areas, on the mesoscale snowfall system over the Yeongdong region. The National Centers for Environmental Prediction-Department of Energy reanalysis dataset, Extended Reconstructed sea surface temperature, and observed snowfall data are analyzed to reveal the relationship between February snowfall and large-scale factors from 1981 to 2014. The Yeongdong snowfall is associated with the sea level pressure patterns over the Gaema Plateau and North Pacific near the Bering Sea, which is remotely associated to the sea surface temperature (SST) variability over the North Pacific. It is presented that the relationship between the Yeongdong snowfall and large-scale factors is strengthened after 1999 when the central north Pacific has warm anomalous SST. These enhanced relationships explain the atmospheric patterns of recent strong snowfall years (2010, 2011, and 2014). It is suggested that the newly defined index in this study based on related SST variability can be used for a seasonal predictor of the Yeongdong snowfall with 2-month leading.

• Journal of the Korean Geographical Society
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• v.41 no.4 s.115
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• pp.457-469
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• 2006

This study aims to understand the characteristics of spatial distribution of snowfall and to analyze its development mechanism in Honam province in Korea. The areas of snowfall in Honan area can be divided into the seven sub-area by snowfall pattern. In the west coastal area of heavy snowfall and the southwest coastal area of heavy snowfall, snowfall develops over reason of ocean by Siberian High while in the northern inland area of heavy snowfall and the southern inland area of heavy snowfall, it develops when a strong Siberian High affects to inland. Then, much snowfall is by a forced ascending due to topography in Namwon, Imsil and Gwangju of the northwestward of the Noryung and Sobaek mountain ranges while it is weak in Jeonju and Suncheon of the low plains and the southeastward. In the mountainous area of heavy snowfall and the south coastal area of light snowfall, cyclone is also one of causes of snowfall. In the southwest coastal area, snowfall is meager than the southwest coastal area of heavy snowfall because this area is far from the west coast. It is confirmed that the snowfall difference of the coast, inland and mountainous area appears by temperature difference of sea surface and 850hPa temperature, wind speed of Siberian High.

• Atmosphere
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• v.16 no.3
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• pp.203-213
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• 2006

The objective of this paper is to investigate the effects of physical parameterization on the simulation of a snowfall event over Korea caused by air-mass transformation by using the PSU/NCAR MM5. A heavy snowfall event over Korea during 3-5 January 2003 is selected. In addition to the control experiments employing simple-ice microphysics scheme, MRF PBL scheme, and original surface layer process, three consequent physics sensitivity experiments are performed. Each experiment exchanges microphysics (Reisner Graupel), boundary layer (YSU PBL) schemes, and revised surface layer process with a reduced thermal roughness length for the control run. The control run reproduces an overall pattern of snowfall over Korea, but with a high bias by a factor of about 2. As revealed in the previous studies, the cloud microphysics and PBL parameterizations do not show a significant sensitivity for the case of snowfall. A more sophisticated cloud processes does not reveal a discernible effect on the simulated snowfall. Further, high bias in snowfall is exaggerated when a more realistic PBL scheme is employed. On the other hand, it is found that the revised surface layer process plays a role in improving the prediction of snowfall by reducing it. Thus, it is found that a realistic design of surface layer physics in mesoscale models is an important factor to the reduction of systematic bias of the snowfall over Korea that is caused by air-mass transformation over the Yellow sea.

• Atmosphere
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• v.16 no.3
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• pp.247-257
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• 2006

The Yeong-dong heavy snowfall forecast supporting system has been developed during the last several years. In order to construct the conceptual model, we have examined the characteristics of heavy snowfalls in the Yeong-dong region classified into three precipitation patterns. This system is divided into two parts: forecast and observation. The main purpose of the forecast part is to produce value-added data and to display the geography based features reprocessing the numerical model results associated with a heavy snowfall. The forecast part consists of four submenus: synoptic fields, regional fields, precipitation and snowfall, and verification. Each offers guidance tips and data related with the prediction of heavy snowfalls, which helps weather forecasters understand better their meteorological conditions. The observation portion shows data of wind profiler and snow monitoring for application to nowcasting. The heavy snowfall forecast supporting system was applied and tested to the heavy snowfall event on 28 February 2006. In the beginning stage, this event showed the characteristics of warm precipitation pattern in the wind and surface pressure fields. However, we expected later on the weak warm precipitation pattern because the center of low pressure passing through the Straits of Korea was becoming weak. It was appeared that Gangwon Short Range Prediction System simulated a small amount of precipitation in the Yeong-dong region and this result generally agrees with the observations.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.35 no.3
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• pp.545-556
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• 2015

This study is to project the future snowfall and to assess heavy snowfall vulnerable area in South Korea using ground measured snowfall data and RCP climate change scenarios. To identify the present spatio-temporal heavy snowfall distribution pattern of South Korea, the 40 years (1971~2010) snowfall data from 92 weather stations were used. The heavy snowfall days above 20 cm and areas has increased especially since 2000. The future snowfall was projected by HadGEM3-RA RCP 4.5 and 8.5 scenarios using the bias-corrected temperature and snow-water equivalent precipitation of each weather station. The maximum snowfall in baseline period (1984~2013) was 122 cm and the future maximum snow depth was projected 186.1 cm, 172.5 mm and 172.5 cm in 2020s (2011~2040), 2050s (2041~2070) and 2080s (2071~2099) for RCP 4.5 scenario, and 254.4 cm, 161.6 cm and 194.8 cm for RCP 8.5 scenario respectively. To analyze the future heavy snowfall vulnerable area, the present snow load design criteria for greenhouse (cm), cattleshed ($kg/m^2$), and building structure ($kN/m^2$) of each administrative district was applied. The 3 facilities located in present heavy snowfall areas were about two times vulnerable in the future and the areas were also extended.

• Journal of the Korean Association of Geographic Information Studies
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• v.14 no.3
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• pp.188-202
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• 2011

Due to emissions of greenhouse gases caused by increased use of fossil fuels, the climate change has been detected and this phenomenon would affect even larger changes in temperature and precipitation of South Korea. Especially, the increase of temperature by climate change can affect the amount and pattern of snowfall. Accordingly, we tried to predict future snowfall and the snowfall pattern changes by using the downscaled GCM (general circulation model) scenarios. Causes of snow varies greatly, but the information provided by GCM are maximum / minimum temperature, rainfall, solar radiation. In this study, the possibility of snow was focused on correlation between minimum temperatures and future precipitation. First, we collected the newest fresh snow depth offered by KMA (Korea meteorological administration), then we estimate the temperature of snow falling conditions. These estimated temperature conditions were distributed spatially and regionally by IDW (Inverse Distance Weight) interpolation. Finally, the distributed temperature conditions (or boundaries) were applied to GCM, and the future snowfall was predicted. The results showed a wide range of variation for each scenario. Our models predict that snowfall will decrease in the study region. This may be caused by global warming. Temperature rise caused by global warming highlights the effectiveness of these mechanisms that concerned with the temporal and spatial changes in snow, and would affect the spring water resources.

• Atmosphere
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• v.26 no.2
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• pp.227-241
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• 2016

This study investigated the cause of the heavy snowfall that occurred in the East Coast of Korea from 6 February to 14 February 2014. The synoptic conditions were analyzed using blocking index, equivalent potential temperature, potential vorticity, maritime temperature difference, temperature advection, and ground convergence. During the case period, a large blocking pattern developed over the Western Pacific causing the flow to be stagnant, and there was a North-South oriented High-to-Low pressure system over the Korean Peninsula because of this arrangement. The case period was divided into three parts based on the synoptic forcing that was responsible for the heavy snowfall; detailed analyses were conducted for the first and last period. In the first period, a heavy snowfall occurred over the entire Korean Peninsula due to strong updrafts from baroclinic instability and a low pressure caused by potential vorticity located at the mid-troposphere. In the lower atmosphere, a North-South oriented High-to-Low pressure system over the Eastern Korea intensified the easterly airflow and created a convergence zone near the ground which strengthened the upslope effect of the Taebaek Mountain range with a cumulative fresh snowfall amount of 41 cm in the East Coast region. In the last period, the cold air nestled in the Maritime Province of Siberia and Manchuria strengthened much more than that in the first half and extended to the East Sea. The temperature difference between the 850 hPa air and the SST was large and convective clouds developed over the sea. The highest cumulative fresh snow amount of 39.7 cm was recorded in the coastal area during this period. During the entire period, vertically oriented equivalent potential temperature showed neutral stability layer that helped the cloud formation and development in the East Coast. The 2014 heavy snowfall case over the East Coast provinces of Korea were due to: 1) stagnation of the system by blocking pattern, 2) the dynamic effect of mid-level potential vorticity of 1.6 PVU, 3) the easterly air flow from North-South oriented High-to-Low pressure system, 4) the existence of vertically oriented neutral stable layer, and 5) the expansion of strong cold air into the East Sea which created a large temperature difference between the air and the ocean.

• Atmosphere
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• v.29 no.4
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• pp.451-461
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• 2019

Medium-range forecast is highly dependent on ensemble forecast data. However, operational weather forecasters have not enough time to digest all of detailed features revealed in ensemble forecast data. To utilize the ensemble data effectively in medium-range forecasting, representative weather patterns in East Asia in this study are defined. The k-means clustering analysis is applied for the objectivity of weather patterns. Input data used daily Mean Sea Level Pressure (MSLP) anomaly of the ECMWF ReAnalysis-Interim (ERA-Interim) during 1981~2010 (30 years) provided by the European Centre for Medium-Range Weather Forecasts (ECMWF). Using the Explained Variance (EV), the optimal study area is defined by 20~60°N, 100~150°E. The number of clusters defined by Explained Cluster Variance (ECV) is thirty (k = 30). 30 representative weather patterns with their frequencies are summarized. Weather pattern #1 occurred all seasons, but it was about 56% in summer (June~September). The relatively rare occurrence of weather pattern (#30) occurred mainly in winter. Additionally, we investigate the relationship between weather patterns and extreme weather events such as heat wave, cold wave, and heavy rainfall as well as snowfall. The weather patterns associated with heavy rainfall exceeding 110 mm day^-1 were #1, #4, and #9 with days (%) of more than 10%. Heavy snowfall events exceeding 24 cm day^-1 mainly occurred in weather pattern #28 (4%) and #29 (6%). High and low temperature events (> 34℃ and < -14℃) were associated with weather pattern #1~4 (14~18%) and #28~29 (27~29%), respectively. These results suggest that the classification of various weather patterns will be used as a reference for grouping all ensemble forecast data, which will be useful for the scenario-based medium-range ensemble forecast in the future.

• Journal of Korean Society of Transportation
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• v.18 no.5
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• pp.7-16
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• 2000

This paper is focused on verifying time-space repetition of the highway accident and finding the their causes and deterrents. We classify all months into several seasonal groups, develop the model for each seasonal group and analyze the results of these models for Joong-bu highway. The existence of seasonal effect is verified by the analysis or self-organizing map and the accident indices. Agglomerative hierarchical cluster analysis which is used to decide the seasonal groups in accordance with accident patterns, winter group, spring-fall group. and summer group. The accident features of winter group are that the accident rate is high but the severity rate is low. while those of summer group are that the accident rate is low but the severity rate is high. Also, the regression model which is developed to identify the accident Pattern or each seasonal group represents that the season-related factors, such as the amount of rainfall, the amount of snowfall, days of rainfall, days of snowfall etc. are strongly related to the accident pattern of evert seasonal group and among these factors the traffic volume, amount of rainfall. the amount of snowfall and days of freezing importantly affect the local accident Pattern. So, seasonal effect should be considered to the identification of high-risk road section. the development of descriptive and Predictive accident model, the resource allocation model of accident in order to make safety management plan efficient.