Published April 1, 2021
| Version version 0.1
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Wavelet & Fourier Analysis on the ENSO and monsoon data in Python
Description
I applied the Wavelet analysis on the quarterly dataset for El Niño–Southern Oscillation (ENSO) sea surface temperature in degree Celsius (1871-1997) and Indian monsoon rainfall in mm (1871-1995). I compared the results with the Fourier Transform. For the analysis of the ENSO dataset, we see that most of the power is concentrated in a 2 to 8 year period (or 0.125-0.5 Hz). For the Indian monsoon dataset, although the power is evenly distributed across different periods, there is also a slight shift in power from longer periods to shorter periods as time progresses. We also found that the Wavelet Transform helps in visualizing the dynamic behavior of the signals.
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wavelet-fourier-transform-enso-monsoon-data-main.zip
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(2.6 MB)
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Additional details
References
- Chao, B. F., Chung, W., Shih, Z., & Hsieh, Y. (2014). Earth's rotation variations: A wavelet analysis. Terra Nova, 26(4), 260–264. https://doi.org/10.1111/ter.12094
- Franks, L. E. (1969). Signal theory.
- Papoulis, A. (1977). Signal analysis (Vol. 191). McGraw-Hill New York.
- Torrence, C., & Compo, G. P. (1998). A practical guide to wavelet analysis. Bulletin of the American Meteorological Society, 79(1), 61–78.