Journal of the Korean earth science society (한국지구과학회지)

The Korean Earth Science Society (한국지구과학회)
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Time Series Analysis of the Subsurface Oceanic Data and Prediction of the Sea Surface Temperature in the Tropical Pacific

적도 태평양 아표층 자료의 시계열 분석 및 표층 수온 예측

  • Chang You-Soon (Marine Meteorology & Earthquake Research Laboratory, Meteorological Research Institute) ;
  • Lee Da-Un (Marine Meteorology & Earthquake Research Laboratory, Meteorological Research Institute) ;
  • Youn Yong-Hoon (Marine Meteorology & Earthquake Research Laboratory, Meteorological Research Institute) ;
  • Seo Jang-Won (Marine Meteorology & Earthquake Research Laboratory, Meteorological Research Institute)
  • 장유순 (기상청 기상연구소 해양기상지진연구실) ;
  • 이다운 (기상청 기상연구소 해양기상지진연구실) ;
  • 윤용훈 (기상청 기상연구소 해양기상지진연구실) ;
  • 서장원 (기상청 기상연구소 해양기상지진연구실)
  • Published : 2005.10.01
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Abstract

Subsurface oceanic data (Z20; Depth of $20^{\circ}C$ isotherm and WWV; Warm Water Volume) from the tropical Pacific Ocean from 1980 to 2004 were utilized to examine upper ocean variations in relation to E1 Nino. Time series analysis using EOF, composite, and cross-correlation methods indicated that there are significant time delays between subsurface oceanic parameters and the Nino3.4 SST. It implied that Z20 and WWV would be more reliable predictors of El Nino events. Based on analyzed results, we also constructed neural network model to predict the Nino3.4 SST from 1996 to 2004. The forecasting skills for the model using WWV were statistically higher than that using the trade wind except for short range forecasting less than 3 months. This model greatly predicted SST than any other previous statistical model, especially at lead times of 5 to 8 months.

엘니뇨현상과 관련된 해양 아표층 변동성을 조사하기 위해 1980년부터 2004년까지의 적도 해역의 20도 등온선 깊이(Z20)와 난수질량(WWV) 자료를 분석하였다. 주성분 분석, 합성 분석 및 교차상관 분석 결과, 아표층 시계열 자료는 Nino3.4 SST와 유의미한 시간 지연을 가지고 강한 상관성을 보였다. 이 결과는 아표층 해양 변수가 엘니뇨현상에 유용한 예측 인자임을 시사한다. 분석된 결과를 근거로 1996년부터 2004년까지 Nino3.4 SST를 예측하기 위해 신경망 예측 모델을 구성하였다. 해상풍을 입력 자료로 사용하였을 경우 보다 WWV를 적용하였을 때 3개월 이하의 단기 예측을 제외하고 모든 예측 시간에서 더 우수한 예측력을 보였으며, 5-8개월의 예측에 있어서는 기존의 여러 통계 모델 결과보다 예측 성능이 우수함을 확인하였다.

Keywords

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