Empirical mode decomposition analysis of climate changes with special reference to rainfall data

<p>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 &#8220;intrinsic mode functi...

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Format: Article
Language:English
Published: Wiley 2006-01-01
Series:Discrete Dynamics in Nature and Society
Online Access:http://www.hindawi.com/GetArticle.aspx?doi=10.1155/DDNS/2006/45348
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description <p>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 &#8220;intrinsic mode functions&#8221; (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 <mml:math> <mml:mrow> <mml:msup> <mml:mi>&#x03C7;</mml:mi> <mml:mn>2</mml:mn> </mml:msup> </mml:mrow> </mml:math>-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.</p>
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institution Kabale University
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spelling doaj-art-ff43544365d14535b990f78d260cc1652025-02-03T07:24:57ZengWileyDiscrete Dynamics in Nature and Society1026-02262006-01-012006Empirical mode decomposition analysis of climate changes with special reference to rainfall data<p>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 &#8220;intrinsic mode functions&#8221; (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 <mml:math> <mml:mrow> <mml:msup> <mml:mi>&#x03C7;</mml:mi> <mml:mn>2</mml:mn> </mml:msup> </mml:mrow> </mml:math>-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.</p>http://www.hindawi.com/GetArticle.aspx?doi=10.1155/DDNS/2006/45348
spellingShingle Empirical mode decomposition analysis of climate changes with special reference to rainfall data
Discrete Dynamics in Nature and Society
title Empirical mode decomposition analysis of climate changes with special reference to rainfall data
title_full Empirical mode decomposition analysis of climate changes with special reference to rainfall data
title_fullStr Empirical mode decomposition analysis of climate changes with special reference to rainfall data
title_full_unstemmed Empirical mode decomposition analysis of climate changes with special reference to rainfall data
title_short Empirical mode decomposition analysis of climate changes with special reference to rainfall data
title_sort empirical mode decomposition analysis of climate changes with special reference to rainfall data
url http://www.hindawi.com/GetArticle.aspx?doi=10.1155/DDNS/2006/45348