DOI QR코드

DOI QR Code

Homogeneous Regions Classification and Regional Differentiation of Snowfall

적설의 동질지역 구분과 지역 차등화

  • KIM, Hyun-Uk (High Impact Weather Research Center(HIWRC), National Institute of Meteorological Sciences (NIMS), Korean Meteorological Administration (KMA)) ;
  • SHIM, Jae-Kwan (High Impact Weather Research Center(HIWRC), National Institute of Meteorological Sciences (NIMS), Korean Meteorological Administration (KMA)) ;
  • CHO, Byung-Choel (High Impact Weather Research Center(HIWRC), National Institute of Meteorological Sciences (NIMS), Korean Meteorological Administration (KMA))
  • 김현욱 (국립기상과학원 관측기반연구과 재해기상연구센터) ;
  • 심재관 (국립기상과학원 관측기반연구과 재해기상연구센터) ;
  • 최병철 (국립기상과학원 관측기반연구과 재해기상연구센터)
  • Received : 2017.05.26
  • Accepted : 2017.08.28
  • Published : 2017.09.30

Abstract

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.

대설은 우리나라의 법적 자연재해 중 하나이다. 최근 기상현상에 의한 사회경제적 영향력을 함께 예보하는 영향예보가 부각되고 있으며, 이를 위해서는 먼저 각 지역의 기후적 특징을 분석할 필요가 있다. 본 연구에서는 영향예보의 기반마련을 위해 자기조직화지도를 활용하여 적설동질지역을 구분하여 지역별 적설 특징을 분석했다. 연구결과 적설동질지역은 7개 군집으로 나타났으며, 강설량 및 관측일수, 최대강설량을 이용하여 각 그룹의 특징을 구분했다. 대관령, 강릉시, 정읍시는 강설량이 많은 지역으로, 경상도는 강설량이 적은 지역으로 구분되었다. 선행연구와 비교결과 대표적인 지역이 잘 구분되었으나 강설의 특징은 차이가 있는 것으로 나타났다. 본 연구의 결과는 각 지역의 영향예보를 위한 정책결정에 기초자료로 활용될 수 있다.

Keywords

References

  1. Ahn, S.R., H.J. Shin, and S.J. Kim. 2015. Extraction of heavy snowfall vulnerable area for 3 representative facilities using GIS and remote sensing techniques. Journal of the Korean Association of Geographic Information Studies 18(1):1-12 (안소라, 신형진, 김성준. 2015. GIS/RS를 이용한 3개의 대표 시설물별 폭설 취약지역 추출기법 연구. 한국지리정보학회지 18(1):1-12). https://doi.org/10.11108/kagis.2015.18.1.001
  2. Arribas-Bel, D., P. Nijkamp, and H. Scholten. 2011. Multidimensional urban sprawl in Europe: a self-organizing map approach. Computers, Environment and Urban Systems 35(4):263-275. https://doi.org/10.1016/j.compenvurbsys.2010.10.002
  3. Budayan, C., I. Dilmen, and M.T. Birgonul. 2009. Comparing the performance of traditional cluster analysis, self-organizing maps and Fuzzy C-Means method for strategic grouping. Expert Systems with Applications 36:11772-11781. https://doi.org/10.1016/j.eswa.2009.04.022
  4. Changnon, S.A. and D. Changnon. 2006. A spatial and temporal analysis of damaging snowstorms in the United States. Natural Hazards 37(3):373-389. https://doi.org/10.1007/s11069-005-6581-4
  5. Choi, J.S. 1990. The classification of snowfall area and its regional characteristics of South Korea. Journal of the Korean Geographical Society 25(1):35-48 (최진식. 1990. 남한의 강설지역 구분과 강설의 지역적 특성. 대한지리학회지 25(1):35-48).
  6. Donganews. 2014. [Busan/Gyeungnam] traffic chaos, it was damaged by snowfall within 3cm. Available at: http://news.donga.com (Accessed December 9, 2014).
  7. Han, W.S. 2014. The response for an increase of vulnerable areas in heavysnowfall to climate change. KRIHS Policy Brief 450:1-6 (한우석. 2014. 기후변화에따른 폭설 취약지역 증가와 대응방향. 국토연구원. 국토정책 Brief 450:1-6).
  8. IPCC(Intergovernmental Panel on Climate Change). 2007. Climate change (impacts, adaptation and vulnerability. Cambridge University Press, Cambridge, UK. pp.469-506.
  9. Kim, H.U., C. Shon, and S.O. Han. 2012. Identifying the optimal number of homogeneous for regional frequency analysis using self-organizing map. Korea Spatial Information Society 20(6):13-21 (김현욱,손철, 한상옥. 2012 자기조직화지도를 활용한 동일강수지역 최적군집수 분석. 한국공간정보학회지 20(6):13-21).
  10. Kim, N.S., and G.S. Kim. 2013. A study on changes of the spatio-temporal distribution of temperature in Korea Peninsular during the past 40 years. Journal of the Korean Association of Geographic Information Studies 16(4):29-38 (김남신, 김경순. 2013. 지난 40년간 한반도 기온의 시,공간적 분포 변화에 관한 연구. 한국지리정보학회지 16(4):29-38). https://doi.org/10.11108/kagis.2013.16.4.029
  11. KMA(Korea Meteorological Administration) 2011. How to use the regional climate change information?. Research report. pp.10-101 (기상청. 2011. 지역기후변화정보 어떻게 활용해야하나? 연구보고서. 10-101쪽).
  12. Ko, J.W., H.J. Baek, and W.T. Kwon. 2005. The charateristics of precipitation and regionalization during rainy season in Korea. Asia-Pacific Journal of Atomspheric Science 41(1):101-114.
  13. Lee, B.S. 1979. The distribution of the fresh snowfall in South Korea. Department of Geography Education in Seoul National University 9(1)224-233 (이병설. 1979. 남한의 강설 분포에 관한 연구. 서울대학교 지리교육학과 9(1)224-233).
  14. Lin, G.F. and L.H. Chen. 2006. Identification of homogeneous regions for regional frequency analysis using the self-organizing map. Journal of Hydrology 324:1-9. https://doi.org/10.1016/j.jhydrol.2005.09.009
  15. Lu, H.C., C.L. Chang, and J.C. Hsieh. 2006. Classification of PM10 distributions in Taiwan. Atmospheric Environment 40(8): 1452-1463. https://doi.org/10.1016/j.atmosenv.2005.10.051
  16. Moon, Y.S. 1990. Division of precipitation regions in Korea through the cluster analysis. Asia-Pacific Journal of Atmospheric Science 26(4):203-215.
  17. Moon, Y.S. and H.J. Kim. 2001. Classification of annual and seasonal precipitation areas in Korea. Journal of Korean Meteorlogical Society 11(1):259-262 (문영수, 김희종. 2001. 한국의 연 및 계절별 강수지역 구분. 한국기상학회지 11(1):259-262).
  18. Nishiyama, K., S. Endo, K. Jinno, C.B. Uvo, J. Olsson, and R. Berndtsson. 2007. Identification of typical synoptic patterns causing heavy rainfall in the rainy season in Japan by a self-organizing Map. Atmospheric Research 83(2-4):185-200. https://doi.org/10.1016/j.atmosres.2005.10.015
  19. Park, S.Y. and H.M. Tak. 2013. Land use change and climate patterns in southeast Korea. Journal of the Korean Association of Geographic Information Studies 16(2): 47-64 (박선엽, 탁한명. 2013. 우리나라 동남부 지역의 토지이용과 기후패턴 변화 분석.한국지리정보학회지 16(2):47-64). https://doi.org/10.11108/kagis.2013.16.2.047
  20. Song, I.H., J.H. Song, S.M. Kim, M.W. Jang, and M.S. Kang. 2012. Spatial distribution and regional characteristics of meteorological damages to agricultural farms in Korea. Journal of the Korean Society of Agricultural Engineers 54(6):45-52 (송인홍, 송정헌, 김상민, 장민원, 강문성. 2012.우리나라 농업기상재해의 공간분포 및 지역특성 분석. 한국농공학회지. 54(6):45-52). https://doi.org/10.5389/KSAE.2012.54.6.045
  21. Um, M.J., C.S. Jeong, W.S. Nam, Y.H. Jung, and J.H. Heo. 2011. The analysis of optimal cluster number of precipitation region with Dunn Index. Proceedings of Korea Water Resources Association. pp.87-90 (엄명진, 정창삼, 남우성, 정영훈, 허준행. 2011. Dunn 지수를 이용한 최적 강수지역 군집수 분석. 한국수자원학회 학술발표회. 87-90쪽).
  22. Vesanto, J. and E. Alhoniemi 2000. Clustering of the self-organizing map. IEEE Transactions on Neural Networks 11(3):586-600. https://doi.org/10.1109/72.846731
  23. WMO(World Meteorological Organization) 2015. WMO guidelines on multi-hazard impact-based forecast and warning services. Research report. 1150:1-23.
  24. Yonhapnews. 2011. Greenhouse collapsed in heavy snow. Available at: http://www.yonhapnews.co.kr(Accessed February 13, 2011).