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Wavelet based correlation coefficient of time series of Saudi Meteorological Data

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  • Rehman, S.
  • Siddiqi, A.H.

Abstract

In this paper, wavelet concepts are used to study a correlation between pairs of time series of meteorological parameters such as pressure, temperature, rainfall, relative humidity and wind speed. The study utilized the daily average values of meteorological parameters of nine meteorological stations of Saudi Arabia located at different strategic locations. The data used in this study cover a period of 16 years between 1990 and 2005. Besides obtaining wavelet spectra, we also computed the wavelet correlation coefficients between two same parameters from two different locations and show that strong correlation or strong anti-correlation depends on scale. The cross-correlation coefficients of meteorological parameters between two stations were also calculated using statistical function. For coastal to costal pair of stations, pressure time series was found to be strongly correlated. In general, the temperature data were found to be strongly correlated for all pairs of stations and the rainfall data the least.

Suggested Citation

  • Rehman, S. & Siddiqi, A.H., 2009. "Wavelet based correlation coefficient of time series of Saudi Meteorological Data," Chaos, Solitons & Fractals, Elsevier, vol. 39(4), pages 1764-1789.
    • Handle: RePEc:eee:chsofr:v:39:y:2009:i:4:p:1764-1789
    DOI: 10.1016/j.chaos.2007.06.054
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    References listed on IDEAS

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    1. Patrice Abry & Darryl Veitch & Patrick Flandrin, 1998. "Long‐range Dependence: Revisiting Aggregation with Wavelets," Journal of Time Series Analysis, Wiley Blackwell, vol. 19(3), pages 253-266, May.
    2. Razdan, Ashok, 2004. "Wavelet correlation coefficient of ‘strongly correlated’ time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 333(C), pages 335-342.
    3. Chang, Ching-Cheng, 2002. "The potential impact of climate change on Taiwan's agriculture," Agricultural Economics, Blackwell, vol. 27(1), pages 51-64, May.
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