Long-term forecasting cimate nonstationary oscillation processes using EMD

• 영문명: Long-term forecasting cimate nonstationary oscillation processes using EMD
• 발행기관: 한국방재학회
• 저자명: Lee, Taesam Ouarda, Taha B.M.J.
• 간행물 정보: 『3. 한국방재학회 학술대회논문집』2014년, 383~383쪽, 전체 1쪽
• 주제분류: 공학 > 기타공학
• 파일형태: PDF
• 발행일자: 2014.02.19

영문 초록

We developed a stochastic model that captures long term nonstationary oscillations (NSOs) within a given variable. The model employs a data-adaptive decomposition method named empirical mode decomposition (EMD). Irregular oscillatory processes in a given variable can be extracted into a finite number of intrinsic mode functions with the EMD approach. A unique data-adaptive algorithm is proposed in the present paper in order to study the future evolution of the NSO components extracted from EMD. To evaluate the model performance, the model is tested with the synthetic data set from Rossler attractor and with global surface temperature anomalies (GSTA) data. The results of the attractor show that the proposed approach provides a good characterization of the NSOs. For GSTA data, the last 30 observations are truncated and compared to the generated data. Then the model is used to predict the evolution of GSTA data over the next 50 years. The results of the case study confirm the power of the EMD approach and the proposed NSO resampling (NSOR) method as well as their potential for the study of climate variables.

키워드

empirical mode decomposition nonstationary oscillation processes prediction of climate