Wavelet for Climate Data Analysis

Though there are a lots of factors affecting climate time series, non-linear interaction between different scales or interference of frequency component from its sidebands is associated with amplitude modulation which can be seen with frequency components in Fourier Spectrum without time information.

If some fundamental physical properties of the climate system undergoes secular changes which is followed by frequency modulation. Some of physical properties are increase in moisture content due to global warming. If the there is not finite time of occurrence then Fourier Spectrum is not sufficient to get dominant frequencies.

Another properties associated, abrupt change in frequency of climate time series is due to occurrence of catastrophic event with long term impact, which can be analysed with spikes or peaks with corresponding frequencies in Fourier Spectrum but contains no time information at which such abrupt changes occurred.

Similarly, another short term effect associated with sudden finite amplitude perturbation. This is specially due to volcanic eruption which causes global temperature variations, which can be detected in Fourier Spectrum with large number of component without time information.

For this all Wavelet Transform could be better solution which can give time information as well as frequency information though both time and frequency information could not be determined at the same time according to Heigenburg Principal, small frequency band can be localized at the small time interval.

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