Discrete Dynamics in Nature and Society
Volume 2006 (2006), Article ID 45348, 17 pages
doi:10.1155/DDNS/2006/45348
Empirical mode decomposition analysis of climate changes with special reference to rainfall data
1Department of Frontier Informatics, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
2Center for Climate System Research, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa-shi, Chiba 277-8568, Japan
3Agricultural Science Unit, Indian Statistical Institute, Kolkata, India
Received 27 July 2005; Accepted 6 November 2005
Copyright © 2006 Md. Khademul Islam Molla et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
We have used empirical mode decomposition (EMD) method, which is especially well fitted for analyzing time-series data representing nonstationary and nonlinear processes. This method could decompose any time-varying data into a finite set of functions called “intrinsic mode functions” (IMFs). The EMD analysis successively extracts the IMFs with the highest local temporal frequencies in a recursive way. The extracted IMFs represent a set of successive low-pass spatial filters based entirely on the properties exhibited by the data. The IMFs are mutually orthogonal and more effective in isolating physical processes of various time scales. The results showed that most of the IMFs have normal distribution. Therefore, the energy density distribution of IMF samples satisfies χ2-distribution which is statistically significant. This study suggested that the recent global warming along with decadal climate variability contributes not only to the more extreme warm events, but also to more frequent, long lasting drought and flood.