Study of the Dynamics of Wind Data Fluctuations: A Wavelet and MFDFA Based Novel Method

The Wind Speed data provides useful information, valuable for modeling and weather forecasting. The fluctuations in wind data depend upon the convection process, causing agitation of wind particles. We separate the average behavior (trend) from high frequency fluctuations through Discrete Wavelet Transform (DWT) using Daubechies wavelets. The fluctuations are characterized by the normalized energy plots, for fluctuations at different scales. This reveals differences in fluctuations of wind speed data for different seasons. Periodic modulations are then identified through Morlet wavelet, composed of a Gaussian window and a sinusoidal function. We further identified multi-fractal behavior through MFDFA, where it is observed that, the value of Hurst exponent decreases and the width of singularity spectrum increases, as the wind particle agitation decreases, due to a corresponding decrease in the convection process.

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