Detection of low-dimensional chaos in wind time series

In the present work we investigated the existence of low-dimensional deterministic chaos in wind time series. The time series were obtained from the New Anchialos (Greece) Air Base measurement station. In a first place we used the raw data without any noise filtering. Characteristic times were extracted using power spectrum and average mutual information function. The estimation of invariant measures, such as the correlation dimension and Lyapunov exponents indicate the possible existence of a low-dimensional attractor. After noise removal with the use of the local projective method the analysis indicates in a more clear way the existence of a low-dimensional attractor. In addition, the null hypothesis was tested for the dynamical characteristics of the wind time series by using the surrogate data test and the corresponding results provide significant evidence for the existence of low-dimensional chaotic dynamics underlying the wind time series.