Bivariate EMD-Based Data Adaptive Approach to the Analysis of Climate Variability
This paper presents a data adaptive approach for the analysis of climate variability using bivariate empirical mode decomposition (BEMD). The time series of climate factors: daily evaporation, maximum and minimum temperatures are taken into consideration in variability analysis. All climate data are...
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| Format: | Article |
| Language: | English |
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Wiley
2011-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2011/935034 |
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| author | Md. Khademul Islam Molla Poly Rani Ghosh Keikichi Hirose |
| author_facet | Md. Khademul Islam Molla Poly Rani Ghosh Keikichi Hirose |
| author_sort | Md. Khademul Islam Molla |
| collection | DOAJ |
| description | This paper presents a data adaptive approach for the analysis of climate variability using bivariate empirical mode decomposition (BEMD). The time series of climate factors: daily evaporation, maximum and minimum temperatures are taken into consideration in variability analysis. All climate data are collected from a specific area of Bihar in India. Fractional Gaussian noise (fGn) is used here as the reference signal. The climate signal and fGn (of same length) are combined to produce bivariate (complex) signal which is decomposed using BEMD into a finite number of sub-band signals named intrinsic mode functions (IMFs). Both of climate signal as well as fGn are decomposed together into IMFs. The instantaneous frequencies and Fourier spectrum of IMFs are observed to illustrate the property of BEMD. The lowest frequency oscillation of climate signal represents the annual cycle (AC) which is an important factor in analyzing climate change and variability. The energies of the fGn's IMFs are used to define the data adaptive threshold to separate AC. The IMFs of climate signal with energy exceeding such threshold are summed up to separate the AC. The interannual distance of climate signal is also illustrated for better understanding of climate change and variability. |
| format | Article |
| id | doaj-art-9151e0402a044ab18cf4cc4dbed80b22 |
| institution | Kabale University |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2011-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-9151e0402a044ab18cf4cc4dbed80b222025-02-03T01:32:13ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2011-01-01201110.1155/2011/935034935034Bivariate EMD-Based Data Adaptive Approach to the Analysis of Climate VariabilityMd. Khademul Islam Molla0Poly Rani Ghosh1Keikichi Hirose2Geophysical Sciences, University of Alberta, Edmonton, AB, T6G 2G7, CanadaDepartment of Computer Science and Engineering, The University of Rajshahi, Rajshahi 6205, BangladeshDepartment of Information and Communication Engineering, The University of Tokyo, Tokyo 113-0033, JapanThis paper presents a data adaptive approach for the analysis of climate variability using bivariate empirical mode decomposition (BEMD). The time series of climate factors: daily evaporation, maximum and minimum temperatures are taken into consideration in variability analysis. All climate data are collected from a specific area of Bihar in India. Fractional Gaussian noise (fGn) is used here as the reference signal. The climate signal and fGn (of same length) are combined to produce bivariate (complex) signal which is decomposed using BEMD into a finite number of sub-band signals named intrinsic mode functions (IMFs). Both of climate signal as well as fGn are decomposed together into IMFs. The instantaneous frequencies and Fourier spectrum of IMFs are observed to illustrate the property of BEMD. The lowest frequency oscillation of climate signal represents the annual cycle (AC) which is an important factor in analyzing climate change and variability. The energies of the fGn's IMFs are used to define the data adaptive threshold to separate AC. The IMFs of climate signal with energy exceeding such threshold are summed up to separate the AC. The interannual distance of climate signal is also illustrated for better understanding of climate change and variability.http://dx.doi.org/10.1155/2011/935034 |
| spellingShingle | Md. Khademul Islam Molla Poly Rani Ghosh Keikichi Hirose Bivariate EMD-Based Data Adaptive Approach to the Analysis of Climate Variability Discrete Dynamics in Nature and Society |
| title | Bivariate EMD-Based Data Adaptive Approach to the Analysis of Climate Variability |
| title_full | Bivariate EMD-Based Data Adaptive Approach to the Analysis of Climate Variability |
| title_fullStr | Bivariate EMD-Based Data Adaptive Approach to the Analysis of Climate Variability |
| title_full_unstemmed | Bivariate EMD-Based Data Adaptive Approach to the Analysis of Climate Variability |
| title_short | Bivariate EMD-Based Data Adaptive Approach to the Analysis of Climate Variability |
| title_sort | bivariate emd based data adaptive approach to the analysis of climate variability |
| url | http://dx.doi.org/10.1155/2011/935034 |
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