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영동과 영서 호우의 특성 비교
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저자명
권태영,김재식,김병곤,Kwon. Tae-Yong,Kim. Jae-Sik,Kim. Byung-Gon
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권/호정보
2013년|23권 3호|pp.245-264 (20 pages)
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한국기상학회
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이 논문은 한국과학기술정보연구원과 논문 연계를 통해 무료로 제공되는 원문입니다.
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기타언어초록

Heavy rain over the Gangwon region has distinct characteristics in the temporal and spatial distribution of rainfall, most of which are concentrated on a very short period of time and either part of Yeongdong and Yeongseo regions. According to its regional distribution, heavy rain events over the Gangwon region may be classified into Yeongdong and Yeongseo heavy rain in which rainfalls of more than 110 mm $(6 hrs)^{-1}$ (heavy rain warning) have been observed in at least one of the weather stations over only Yeongdong or Yeongseo region, but over the other region the rainfalls are less than 70 mm $(6 hrs)^{-1}$ (heavy rain advisory). To differentiate between Yeongdong and Yeongseo heavy rain, 9 cases for Yeongdong heavy rain and 8 cases for Yeongseo heavy rain are examined on their synoptic and mesoscale environments using some meteorological parameters and ingredients. In addition, 8 cases are examined in which heavy rain warning or advisory are issued in both Yeongdong and Yeongseo regions. The cases for each heavy rain type have shown largely similar features in some meteorological parameters and ingredients. Based on an ingredient analysis, there are three common and basic ingredients for the three heavy rain types: instability, moisture, and lift. However, it is found that the distinct and important process producing strong upward vertical motions may discriminate among three heavy rain types very well. Yeongdong heavy rain is characterized by strong orographic lifting, Yeongseo heavy rain by high instability (high CAPE), and heavy rain over both regions by strong synoptic-scale ascent (strong 850 hPa Q-Vector convergence, diagnostics for ascent). These ingredients and diagnostics for the ingredients can be used to forecasting the potential for regional heavy rain. And also by knowing which of ingredients is important for each heavy rain type, forecasters can concentrate on only a few ingredients from numerous diagnostic and prognostic products for forecasting heavy rain events.