• Title/Summary/Keyword: Snowfall Area Classification

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Classification of Snowfalls over the Korean Peninsula Based on Developing Mechanism (발생기구에 근거한 한반도 강설의 유형 분류)

  • Cheong, Seong-Hoon;Byun, Kun-Young;Lee, Tae-Young
    • Atmosphere
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    • v.16 no.1
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    • pp.33-48
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    • 2006
  • A classification of snowfall type based on development mechanism is proposed using previous snowfall studies, operational experiences, etc. Five types are proposed: snowfall caused by 1) airmass transformation (AT type), 2) terrain effects in a situation of expanding Siberian High (TE type), 3) precipitation systems associated with extratropical cyclones (EC type), 4) indirect effects of extratropical cyclones passing over the sea to the south of the Korean peninsula (ECS type), and 5) combined effects of TE and ECS types (COM type). Snowfall events during 1981-2001 are classified according to the 5 types mentioned above. For this, 118 events, with at least one station with daily snowfall depth greater than 20 cm, are selected. For the classification, synoptic weather charts, satellite images, and precipitation data are used. For TE and COM types, local sea-level pressure chart is also used to confirm the presence of condition for TE type (this is done for events in 1990 and thereafter). The classification shows that 109 out of 118 events can be classified as one of the 5 types. In the remaining 8 events, heavy snowfall occurred only in Ullung Island. Its occurrence may be due to one or more of the following mechanism: airmass transformation, mesoscale cyclones and/or mesoscale convergence over the East Sea, etc. Each type shows different characteristics in location of snowfall and composition of precipitation (i.e., dry snow, rain, and mixed precipitation). The AT-type snowfall occurs mostly in the west coast, Jeju and Ullung Islands whereas the TE-type snowfall occurs in the East coast especially over the Young Dong area. The ECS-type snowfall occurs mostly over the southern part of the peninsula and some east cost area (sometimes, whole south Korea depending on the location of cyclones). The EC- and COM-type snowfalls occur in wider area, often whole south Korea. Precipitation composition also varies with the type. The AT-type has a snow ratio (SR) higher than the mean value. The TE- and EC-type have SR similar to the mean. The ECS- and COM-type have SR values smaller than the mean. Generally the SR values at high latitude and mountainous areas are higher than those at the other areas. The SR value informs the characteristics of the precipitation composition. An SR value larger than 10 means that all precipitation is composed of snow whereas a zero SR value means that all precipitation is composed of rain.

The Study for Classifying Snowfall Area Types with Consideration of Snowfall Characteristics and Times (강설특성과 강설시간을 고려한 강설지역의 유형 구분에 관한 연구)

  • Kim, Geunyoung
    • Journal of the Society of Disaster Information
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    • v.16 no.1
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    • pp.21-33
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    • 2020
  • Purpose: The objective of this research is to classify snowfall area types with consideration of past regional snowfall characteristics and times for the effective local snow removal response systems of 229 local government districts. Method: This research first collected snowfall data of South Korea meteorological stations, and classified regional types using successive snowfall time. This research finally produced GIS maps using regional type information of snowfalls by applying GIS analysis methods. Result: This research provides five types of snowfall regions including 'frequent heavy snowfall regions', 'frequent light snowfall regions', 'rare heavy snowfall regions', 'average snowfall regions', and 'rare light snowfall regions' based on analysis results. Conclusion: Results of this research can be used as basic information for regional demand estimations of snow removal equipments, materials, vehicles, and personnel for the efficient snow removal response systems.

Homogeneous Regions Classification and Regional Differentiation of Snowfall (적설의 동질지역 구분과 지역 차등화)

  • KIM, Hyun-Uk;SHIM, Jae-Kwan;CHO, Byung-Choel
    • Journal of the Korean Association of Geographic Information Studies
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    • v.20 no.3
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    • pp.42-51
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    • 2017
  • Snowfall is an important natural hazard in Korea. In recent years, the socioeconomic importance of impact-based forecasts of meteorological phenomena have been highlighted. To further develop forecasts, we first need to analyze the climatic characteristics of each region. In this study, homogeneous regions for snowfall analysis were classified using a self-organizing map for impact-based forecast and warning services. Homogeneous regions of snowfall were analyzed into seven clusters and the characteristics of each group were investigated using snowfall, observation days, and maximum snowfall. Daegwallyeong, Gangneung-si, and Jeongeup-si were classified as areas with high snowfall and Gyeongsangdo was classified as an area with low snowfall. Comparison with previous studies showed that representative areas were well distinguished, but snowfall characteristics were found to be different. The results of this study are of relevance to future policy decisions that use impact-based forecasting in each region.

Classification of Weather Patterns in the East Asia Region using the K-means Clustering Analysis (K-평균 군집분석을 이용한 동아시아 지역 날씨유형 분류)

  • Cho, Young-Jun;Lee, Hyeon-Cheol;Lim, Byunghwan;Kim, Seung-Bum
    • 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.

Study of Satellite Image Analysis Techniques to Investigate Construction Environment Analysis of Resource Development in the Arctic Circle - Alberta, Canada (북극권 자원개발 건설환경 조사를 위한 위성 영상 분석 기법 연구 - 캐나다 앨버타주 대상)

  • Kim, Sewon;Kim, YoungSeok
    • The Journal of Engineering Geology
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    • v.31 no.4
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    • pp.549-559
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    • 2021
  • The Arctic Circle's huge amounts of fossil fuels and mineral resources are being developed and subjected to active construction projects. Global efforts are continuing to actively respond to climate change, but the dependence on fossil fuels remains high. This study reports a preliminary survey conducted in Alberta, Canada, where oil sand resources are actively developed. A land cover map was prepared using satellite imagery to reduce the cost and time of surveying a wide area. Results likely useful to resource development projects such as ground surface temperature and snow cover distribution were derived by using the obtained image classification results. It is expected that the results of the present research and analysis will be used to establish strategies for the successful promotion and operation of projects to develop resources in the Arctic.