Journal of Korea Water Resources Association (한국수자원학회논문집)

Korea Water Resources Association (한국수자원학회)
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Correlation analysis between climate indices and Korean precipitation and temperature using empirical mode decomposition : I. Data decomposition and characteristic analysis

경험적 모드분해법을 이용한 기상인자와 우리나라 강수 및 기온의 상관관계 분석 : I. 자료의 분해 및 특성 분석

  • Ahn, Si-Kweon (Graduate Program in Technology Policy, Yonsei Univ.) ;
  • Choi, Wonyoung (School of Civil and Environmental Engineering, Yonsei Univ.) ;
  • Kim, Taereem (School of Civil and Environmental Engineering, Yonsei Univ.) ;
  • Heo, Jun-Haeng (School of Civil and Environmental Engineering, Yonsei Univ.)
  • 안시권 (연세대학교 대학원 기술정책 협동과정) ;
  • 최원영 (연세대학교 대학원 토목공학과) ;
  • 김태림 (연세대학교 대학원 토목공학과) ;
  • 허준행 (연세대학교 대학원 토목공학과)
  • Received : 2016.01.06
  • Accepted : 2016.01.15
  • Published : 2016.03.31
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Abstract

Recently, natural hazards have occurred frequently due to climate change. The research need for predicting variability and tendency of precipitation and temperature has been increased. However, it is difficult to determine the characteristics of precipitation and temperature within a confidence range since they change due to complex factors with choppy and too many components. If their characteristics having more than one component are decomposed, then it can be useful for determining the variation of such characteristics more accurately. In this study, Korean precipitation and temperature were decomposed and their Intrinsic Mode Function (IMF) were extracted from Empirical Mode Decomposition (EMD). Finally, the characteristics of Korean precipitation and temperature data were analyzed in terms of periodicity and tendency.

최근 기후변화로 인한 자연재해가 증가하면서 강수 및 기온자료의 시계열에 대한 변동성과 추세를 분석하여 그 변화를 예측하는 연구의 필요성이 점점 커지고 있다. 하지만 강수나 기온의 경우 복합적인 요소에 의해 변동이 일어나 자료의 변동성이 매우 심하고 너무 많은 요소를 포함하게 되어 그 특성을 정확히 판단하기가 쉽지 않다. 따라서 자료의 시계열을 분해하게 되면 각 특성을 가진 요소를 추출할 수 있으므로, 정확한 변동 특성을 파악할 수 있다. 본 연구에서는 우리나라 강수 및 기온자료를 경험적 모드분해법(Empirical Mode Decomposition, EMD)을 통해 주기별로 분해하여 각각의 내재모드함수(Intrinsic Mode Function, IMF)를 추출하였다. 또한, 추출된 내재모드함수의 에너지 밀도를 이용한 유의성 검정을 통해 원자료로부터 유의미한 자료를 포함하고 있는 내재모드함수를 선별하고, 이들의 주기성, 경향성을 분석하였다.

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References

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