• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.992-996
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• 2008

In this study, changes in precipitation across South Korea during snow seasons (November-April) and their potential are examined. Current (1973/74-2006/07) and future (2081-2100) time series of snow indices including snow season, snow-to-precipitation ratio, and snow impossible day are extracted from observed snow and precipitation data for 61 weather stations as well as observed and modeled daily temperature data. Analyses of linear trends reveal that snow seasons have shortened by 3-13 days/decade; that the snow-to-precipitation ratio (the percentage of snow days relative to precipitation days) has decreased by 4-8 %/decade. These changes are associated with pronounced formations of a positive pressure anomaly core over East Asia during the positive Arctic Oscillation winter years since the late 1980s. A snow-temperature statistical model demonstrates that the warming due to the positive core winter intensifies changes from snow to rain at the rate of $4.7cm/^{\circ}C$. The high pressure anomaly pattern has also contributed to decreases of air-sea thermal gradient which are associated with the reduction of snow could formation. Modeled data predict that a fingerprint of wintertime global warming causing changes from snow to rain will continue to be observed over the 21st century.

• International Journal of Highway Engineering
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• v.14 no.4
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• pp.141-148
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• 2012

PURPOSES : This study demonstrates the need for the collection of road weather information in order to perform efficient snow removal works during the winter season. Snow removal operations are usually dependent upon weather information obtained from the Automatic Weather Station provided by the Korea Meteorological Administration. However, there are some difference between road weather and weather forecasts in their scope. This is because general weather forecasts are focused on macroscopic standpoints rather than microscopic perspectives. METHODS : In this study, the relationship between snow removal works and historical weather forecasts are properly analyzed to prove the importance of road weather information. We collected both weather data and snow removal works during winter season at "A" regional offices in Gangwon areas. RESULTS : Results showed that the validation of weather forecasts for snow removal works were depended on the height difference between AWS location and its neighboring roadway. CONCLUSIONS : Namely, it appears that road weather information should be collected where AWS location and its neighboring roadway have relatively big difference in their heights.

• Communications for Statistical Applications and Methods
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• v.13 no.2
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• pp.369-378
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• 2006

This Study focuses on the binary forecast of occurrence of heavy snow in Honam area based on the MOS(model output statistic) method. For our study daily amount of snow cover at 17 stations during the cold season (November to March) in 2001 to 2005 and Corresponding 45 RDAPS outputs are used. Logistic regression model and neural networks are applied to predict the probability of occurrence of Heavy snow. Based on the distribution of estimated probabilities, optimal thresholds are determined via true shill score. According to the results of comparison the logistic regression model is recommended.

• Proceedings of the KSME Conference
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• 2000.11a
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• pp.801-806
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• 2000

This study deals with the design of snow plow for the special equipment vehicle. The purpose of the study is to develop the snow plow that can install in the special equipment vehicle such as clean up vehicle or dump truck in winter season. To do so, it is designed by 3 sub-assembly - snow shovel, main frame, and hydraulic cylinder and its support. The snow shovel consists of 3 pieces to meet the road profile and to exchange easily the damaged parts. Main frame connects the snow shovel with the hydraulic cylinder and its support and supports the weight of snow. Finally, the hydraulic cylinder and its support move the snow shovel up and down and tilt it. We designed it using 3D commercial CAD software for concurrent engineering design.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.6B
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• pp.355-361
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• 2012

Gwangdong Dam Watershed is affected by the increased discharge caused by the melting snow in the spring season. Therefore, simulation results obtained using hydrologic models have generally been inaccurate in relation to discharge without snow pack and melt modules. In this research, a grid based distributed rainfall runoff model (K-DRUM) was developed using a snow pack and melt module, and has been applied in the Gwangdong Dam Watershed to simulate the discharge for a four year period. A previous version of K-DRUM, which does not include a snow pack or melt module, was used to calculate the discharge in order to compare the snow melt effect. The simulation period lasted about 7 months from October of the previous year to April of this year using hourly precipitation and weather observed data. To evaluate the model performance, NSE, PBIAS and RSR statistics techniques were applied using the simulation results of the discharge. From the results of reliability evaluation, the K-DRUM model, which uses a snow pack and melt module, had a good applicability for the runoff simulation considering the snow melt effect in the spring.

• Proceedings of the Korean Quaternary Association Conference
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• 2004.06a
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• pp.24-25
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• 2004

It is well known that there is an inverse relationship between the strength of Indian summer monsoon Rainfall (ISMR) and extent of Eurasian snow cover/depth in the preceding season. Tibetan snow cover/depth also affects the Asian monsoon rainy season largely. The positive correlation between Tibetan sensible heat flux and southeast Asian rainfall suggest an inverse relationship between Tibetan snow cover and southeast Asian rainfall. Developments in Regional Climate Models suggest that the effect of Tibetan snow on the ISMR can be well studied by Limited Area Models (LAMs). LAMs are used for regional climate studies and operational weather forecast of several hours to 3 days in future. The Eta model developed by the National Center for Environmental Prediction (NCEP), the Fifth-Generation NCAR/Penn State Mesoscale Model (MM5) and Regional Climate Model (RegCM) have been used for weather prediction as well as for the study of present-day climate and variability over different parts of the world. Regional Climate Model (RegCM3) has been widely . used for various mesoscale studies. However, it has not been tested to study the characteristics of circulation features and associated rainfall over India so far. In the present study, Regional Climate Model (RegCM-3) has been integrated from 1 st April to 30th September for the years 1993-1996 and monthly mean monsoon circulation features and rainfall simulated by the model at 55km resolution have been studied for the Indian summer monsoon season. Characteristics of wind at 850hPa and 200hPa, temperature at 500hPa, surface pressure and rainfall simulated by the model have been examined for two convective schemes such as Kuo and Grell with Arakawa-Schubert as the closure scheme, Model simulated monsoon circulation features have been compared with those of NCEP/NCAR reanalyzed fields and the rainfall with those of India Meteorological Department (IMD) observational rainfall datasets, Comparisons of wind and temperature fields show that Grell scheme is closer to the NCEP/NCAR reanalysis, The influence of Tibetan snowdepth in spring season on the summer monsoon circulation features and subsequent rainfall over India have been examined. For such sensitivity experiment, NIMBUS-7 SMMR snowdepth data have been used as a boundary condition in the RegCM3, Model simulation indicates that ISMR is reduced by 30% when 10cm of snow has been introduced over Tibetan region in the month of previous April. The existence of Tibetan snow in RegCM3 also indicates weak lower level monsoon westerlies and upper level easterlies.

• Smart Structures and Systems
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• v.29 no.5
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• pp.653-660
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• 2022

Gravel scattering that is generated during operation of high-speed railway vehicle is cause to damage of vehicle such as windows, axle protector and so on. Especially, those are frequently occurred in winter season when snow ice is generated easily. Above all, damage of vehicle windows has not only caused maintenance cost but also increased psychological anxiety of passengers. Various methods such as heating system using copper wire, heating jacket and heating air are applied to remove snow ice generated on the under-body of vehicle. However, the methods require much run-time and man power which can be low effectiveness of work. Therefore, this paper shows that large-area heating system was developed based on heating coat in order to fundamentally prevent snow ice damage on high-speed railway vehicle in the winter season. This system gives users high convenience because that can remotely control the heating system using IoT-based wireless communication. For evaluating the applicability to railroad sites, a field test on an actual high-speed railroad operation was conducted by applying these techniques to the brake cylinder of a high-speed railroad vehicle. From the results, it evaluated how input voltage and electric power per unit area of the heating specimen influences exothermic performance to draw the permit power condition for icing. In the future, if the system developed in the study is applied at the railroad site, it may be used as a technique for preventing all types of damages occurring due to snow ice in winter.

• Journal of Korea Society of Digital Industry and Information Management
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• v.13 no.4
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• pp.41-48
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• 2017

It is necessary to develop an LED traffic light lens having a snow removal function capable of clearly providing a traffic signal to the driver even when a traffic light is blurred due to heavy snow and wind in the winter season. This study is focused on the research and development of the traffic light lens in the process of developing the LED traffic light with the snow removal function. In the developed traffic light lens, instead of attaching the film heater, the coated nichrome wire was wound into a coil shape and inserted directly into the groove in the lens. The developed heater system facilitates the insertion of the heating wire with high elasticity into a curved lens and can provide a sufficient heat without deformation of the PC lens. The proposed traffic lights were tested in various external environments and the test results showed that complete snow removal is possible without tunnel effect.

• Asian Journal of Turfgrass Science
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• v.26 no.2
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• pp.102-109
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• 2012

Commercial formulation of fungicides was studied in the golf course for evaluation against Typhula incarnata causal agents of gray snow mold. Efficacies of fungicides application, fungicide mixture, fungicides applied method (irrigation and spray) and fungicides applied time (early fall and late fall) were evaluated for their influence on the chemical control of gray snow mold of turfgrass during the winter season in Yongpyeong golf course, Korea. Unsprayed control has significantly more disease severity than three fungicides (azoxystrobin, propiconazole, and tebuconazole) were applied to field plots. Effect of three fungicides was over 80% with control value for controlling gray snow mold on Kentucky bluegrass and creeping bentgrass species. Effect of fungicide mixture with different family groups had an over 93% control value of gray snow mold on Kentucky bluegrass species. It was not significantly difference in fungicidal effect according to applied method (irrigation and spray) with azoxystrobin on Kentucky bluegrass and creeping bentrasss species. Effect of fungicides applied time was a significantly difference on disease control by tebconazole early fall spray.

• Atmosphere
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• v.22 no.1
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• pp.117-128
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• 2012

Does snow depth initialization have a quantitative impact on sub-seasonal to seasonal prediction skill? To answer this question, a snow depth initialization technique for seasonal forecast system has been implemented and the impact of the initialization on the seasonal forecast of surface air temperature during the wintertime is examined. Since the snow depth observation can not be directly used in the model simulation due to the large systematic bias and much smaller model variability, an anomaly rescaling method to the snow depth initialization is applied. Snow depth in the model is initialized by adding a rescaled snow depth observation anomaly to the model snow depth climatology. A suite of seasonal forecast is performed for each year in recent 12 years (1999-2010) with and without the snow depth initialization to evaluate the performance of the developed technique. The results show that the seasonal forecast of surface air temperature over East Asian region sensitively depends on the initial snow depth anomaly over the region. However, the sensitivity shows large differences for different timing of the initialization and forecast lead time. Especially, the snow depth anomaly initialized in the late winter (Mar. 1) is the most effective in modulating the surface air temperature anomaly after one month. The real predictability gained by the snow depth initialization is also examined from the comparison with observation. The gain of the real predictability is generally small except for the forecasting experiment in the early winter (Nov. 1), which shows some skillful forecasts. Implications of these results and future directions for further development are discussed.