• Atmosphere
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• v.25 no.2
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• pp.261-270
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• 2015

Characteristics of snowfall and snow crystal habits have been investigated in the campaign of Experiment on Snow Storms At Yeongdong (ESSAY) using radiosonde soundings, Global Navigation Satellite System (GNSS), and a digital camera with a magnifier for taking a photograph of snowfall crystals. The analysis period is 6 to 14 February 2014, when the accumulated snowfall amount is 192.8 cm with the longest snowfall duration of 9 days. The synoptic situations are similar to those of the previous studies such as the Low pressure system passing by the far South of the Korean peninsula along with the Siberian High extending to northern Japan, which eventually results in the northeasterly or easterly flows and the long-lasting snowfall episodes in the Yeongdong region. In general, the ice clouds tended to exist below around 2~3 km with the consistent easterly flows, and the winds shifted to northerly~northwesterly above the clouds layer. The snow crystal habits observed in the ESSAY campaign were mainly dendrite, consisting of 70% of the entire habits. The rimed habits were frequently captured when two-layered clouds were observed, probably through the process of freezing of super-cooled droplets on the ice particles. The homogeneous habit such as dendrite was shown in case of shallow clouds with its thickness of below 500 m whereas various habits were captured such as dendrites, rimed dendrites, aggregates of dendrites, plates, rimed plates, etc in the thick cloud with its thickness greater than 1.5 km. The dendrites appeared to be dominant in the condition of cloud top temperature specifically ranging $-12{\sim}-16^{\circ}C$. However, the association of snow crystal habits with temperature and super-saturation in the cloud could not be examined in the current study. Better understandings of characteristics of snow crystal habits would contribute to preventing breakdown accidents such as a greenhouse destruction and collapse of a temporary building due to heavy snowfall, and traffic accidents due to snow-slippery road condition, providing a higher-level weather information of snow quality for skiers participating in the winter sports, and estimating more accurate snowfall amount, location, and duration with the fallspeed of solid precipitation.

• Journal of Wetlands Research
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• v.20 no.2
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• pp.105-115
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• 2018

The frequency analysis of hydrometeorological data is one of the most important factors in response to natural disaster damage, and design standards for a disaster prevention facilities. In case of frequency analysis of hydrometeorological data, it assumes that observation data have statistical stationarity, and a parametric method considering the parameter of probability distribution is applied. For a parametric method, it is necessary to sufficiently collect reliable data; however, snowfall observations are needed to compensate for insufficient data in Korea, because of reducing the number of days for snowfall observations and mean maximum daily snowfall depth due to climate change. In this study, we conducted the frequency analysis for snowfall using the Bootstrap method and SIR algorithm which are the resampling methods that can overcome the problems of insufficient data. For the 58 meteorological stations distributed evenly in Korea, the probability of snowfall depth was estimated by non-parametric frequency analysis using the maximum daily snowfall depth data. The results of frequency based snowfall depth show that most stations representing the rate of change were found to be consistent in both parametric and non-parametric frequency analysis. According to the results, observed data and Bootstrap method showed a difference of -19.2% to 3.9%, and the Bootstrap method and SIR(Sampling Importance Resampling) algorithm showed a difference of -7.7 to 137.8%. This study shows that the resampling methods can do the frequency analysis of the snowfall depth that has insufficient observed samples, which can be applied to interpretation of other natural disasters such as summer typhoons with seasonal characteristics.

• Atmosphere
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• v.24 no.1
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• pp.29-38
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• 2014

A characteristic of snowfall and minimum temperature variability in South Korea with respect to the variability of Arctic Oscillation (AO) was investigated. The climatic snowfall regions of South Korea based on daily new fresh snowfall data of 59 Korea Meteorological Administration (KMA) stations data corresponding to the sign of AO index during December to February 1979~2011 were classified. Especially, the differences between snowfalls of eastern regions and that of western regions in South Korea were seen by each mean 1000hPa geopotential height fields, which is one of physical structure, for the selected cases over the East Asia including the Korean Peninsula. Daily minimum temperature variability of 59 KMA station data and daily AO index during the same period were investigated using Cyclo-stationary empirical orthogonal function (CSEOF) analysis. The first CSEOF of wintertime daily AO index and that of minimum temperature of 59 KMA stations explain 33% and 66% of total variability, respectively. Correlation between principal component time series corresponding to the first CSEOF of AO index and that of temperature at the period of 1990s is over about -0.7 when that of AO index leads about 40 days.

• International Journal of Highway Engineering
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• v.15 no.2
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• pp.19-27
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• 2013

PURPOSES : This study aims to investigate the snow-melt effects of an underground electric heater's snow-melt system via a field performance test, for evaluating the suitability of the system for use on a concrete pavement. The study also investigates the effectiveness of dynamic measures for clearing snow after snowfall events. METHODS : In order to check the field applicability, in November 2010, specimens were prepared from materials used for constructing concrete pavements, and underground electric heating meshes (HOT-mesh) were buried at depths of 50 mm and 100 mm at the site of the Incheon International Airport Construction Research Institute. Further, an automatic heating control system, including a motion sensor and pavement-temperature-controlled sensor, were installed at the site; the former sensor was intended for determining snow-melt effects of the heating control system for different snowfall intensities. Pavement snow-melt effects on snowy days from December 2010 to January 2011 were examined by managing the electric heating meshes and the heating control system. In addition, data on pavement temperature changes resulting from the use of the heating meshes and heating control system and on the dependence of the correlation between the outdoor air temperature and the time taken for the required temperature rise on the depth of the heating meshes were collected and analyzed. RESULTS : The effects of the heating control system's preheat temperature and the hot meshes buried at depths of 50 mm and 100 mm on the melting of snow for snowfalls of different intensities have been verified. From the study of the time taken for the specimen's surface temperature to increase from the preheat temperature ($0^{\circ}C$) to the reference temperature ($5{\sim}8^{\circ}C$) for different snowfall intensities, the correlation between the burial depth and outdoor air temperature has been determined to be as follows: Time=15.10+1.141Depth-6.465Temp CONCLUSIONS : The following measures are suggested. For the effective use of the electric heating mesh, it should be located under a slab it may be put to practical use by positioning it under a slab. From the management aspect, the heating control system should be adjusted according to weather conditions, that is, the snowfall intensity.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.9 no.4
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• pp.68-79
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• 2010

Weather like rain, strong wind or snowfall may make the road condition deteriorated and sometimes induce traffic accidents, which lead to severe traffic congestion, thereby travelers may change their destinations elsewhere. Although origin-destination trip information is required to analyze transportation planning in urban area, there are little researches on the relationship between weather condition and travel patterns. This paper investigates the characteristics of travel patterns on expressway in snowing days of 1998-2008. We compare the normal travel patterns with those of snowing days by the travel distance for each vehicle type. Results show that traffic volume and travel distance have been reduced in snowing days as we expect, and also show different travel patterns for weekday and weekend.

• 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.

• Atmosphere
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• v.27 no.2
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• pp.177-188
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• 2017

The development mechanisms of an unusual heavy snowfall event, which occurred in the coast of Jeju Island on 23 January 2016 were investigated through a thermodynamic approach. The formation of heavy snowfall was attributed to the enhanced thermal convection in two ways. First, the convection was enhanced by the air-sea temperature difference between the cold air advection in low-troposphere associated with the strengthening of the Siberian High and abnormal warm sea surface temperature, which is $1{\sim}2^{\circ}C$ higher than normal year over the Yellow Sea (YS). Second, the convective instability was increased by the vertical temperature gradient between the 7 days-sustained cold air advection in low-troposphere and the abrupt cold air intrusion in mid-troposphere induced by the southward shift of a cold cut-off vortex ($-45^{\circ}C$) at the formation stage. Compared to the twelve hours prior to the formation, the low-level moisture increased by 5% through the moisture supply from the YS, and the air-sea temperature difference increased from $18.5^{\circ}C$ to $28.5^{\circ}C$. Furthermore, the upward sensible (latent) heat flux increased 1.5 (1.2) times over the YS before the twelve hours prior to the formation. Thereafter, the sustained moisture supply and upward turbulent heat flux helped to maintain the snowstorm.

• International Journal of Highway Engineering
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• v.19 no.6
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• pp.83-95
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• 2017

PURPOSES : The purpose of this study is to develop a regression model to predict the International Roughness Index(IRI) and Surface Distress(SD) for the estimation of HPCI using Expressway Pavement Management System(PMS). METHODS : To develop an HPCI prediction model, prediction models of IRI and SD were developed in advance. The independent variables considered in the models were pavement age, Annual Average Daily Traffic Volume(AADT), the amount of deicing salt used, the severity of Alkali Silica Reaction(ASR), average temperature, annual temperature difference, number of days of precipitation, number of days of snowfall, number of days below zero temperature, and so on. RESULTS : The present IRI, age, AADT, annual temperature differential, number of days of precipitation and ASR severity were chosen as independent variables for the IRI prediction model. In addition, the present IRI, present SD, amount of deicing chemical used, and annual temperature differential were chosen as independent variables for the SD prediction model. CONCLUSIONS : The models for predicting IRI and SD were developed. The predicted HPCI can be calculated from the HPCI equation using the predicted IRI and SD.

• Journal of the Korean Geographical Society
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• v.43 no.5
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• pp.715-727
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• 2008

This study analyzed changes on the best condition of temperature and relative humidity for making artificial snows in the Yongpyong Ski Resort using data from Daegwallyeong. Depth of snowfall and snowfall days have decrease since 1990s. If the Yongpyong Ski Resort has only to depend on natural snows, it would be difficult to make and maintain ski slope. There are two times of snowmaking during ski seasons: one is the first snowmaking (October-November) for opening ski slopes and the other is the reinforcement of snowmaking (December-March) for maintaining snow quality during the seasons. Days having the best condition for the first snowmaking (daily minimum temperature below $-1^{\circ}C$ and daily average relative humidity 60 to 80 percent) decreased after 1970s. Days having the best condition for the reinforcement of snowmaking also decreased. While temperature changes are more evident than humidity changes for the first snowmaking, humidity changes are more obvious than change of temperature for the reinforcement of snowmaking. In the future climate projection by A1B scenarios, the length of ski seasons projected to decrease a 10 to 40 percent against the period of 1973-2008. The climate condition for the snowmaking projected to be poor, especially the due to increase of temperature.

• 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.