Nonlinear analysis of the near-surface wind speed time series

Abstract

Research on characteristics of the near-surface wind speed time series can be of great help to understand the mechanisms of odor/gas dispersal and moreover provides useful clues for the optimization of odor/gas source localization algorithms. In this paper, an integrated technique which combines a direct identification method of chaos, i.e., the saturated correlation dimension algorithm with the surrogate data method (an indirect identification method) is proposed to analyze the chaotic characteristics of the near-surface wind speed time series. The analysis procedure includes two stages. Firstly, the GP algorithm is applied to calculate the saturated correlation dimension of the wind speed time series. The value of correlation dimension is 4.1628 ± 0.0022, which indicates that the wind speed time series probably has chaotic characteristics. Then 30 surrogate data sets of original wind speed series are generated by amplitude adjusted Fourier transform (AAFT), and saturated correlation dimension is employed as the statistic of Sigma test. Simulation experiments and numerical analysis show that some deterministic nonlinear components exist in the original wind speed time series. This conclusion provides further confirmation to our pre-assumption, i.e. the near-surface wind speed signal may have chaotic characteristics.