Temporal-spatial cross-correlation analysis of non-stationary near-surface wind speed time series

• Published in: Journal of Central South University Vol. 24; no. 3; pp. 692 - 698
• Main Authors: Zeng, Ming, Li, Jing-hai, Meng, Qing-hao, Zhang, Xiao-nei
• Format: Journal Article
• Language: English
• Published: Changsha Central South University 01.03.2017; Springer Nature B.V
• Subjects: Anemometers; Correlation analysis; Correlation coefficients; Engineering; Metallic Materials; Pattern recognition; Surface wind; Time series; Variations; Wind speed; near-surface wind speed time series; cross-correlation function; Pearson coefficient; detrended cross-correlation analysis (DCCA); cross-correlation coefficient; temporal-spatial cross-correlation
• ISSN: 2095-2899; 2227-5223
• DOI: 10.1007/s11771-017-3470-4

Temporal-spatial cross-correlation analysis of non-stationary wind speed time series plays a crucial role in wind field reconstruction as well as in wind pattern recognition. Firstly, the near-surface wind speed time series recorded at different locations are studied using the detrended fluctuation analysis (DFA), and the corresponding scaling exponents are larger than 1. This indicates that all these wind speed time series have non-stationary characteristics. Secondly, concerning this special feature (i.e., non-stationarity) of wind signals, a cross-correlation analysis method, namely detrended cross-correlation analysis (DCCA) coefficient, is employed to evaluate the temporal-spatial cross-correlations between non-stationary time series of different anemometer pairs. Finally, experiments on ten wind speed data synchronously collected by the ten anemometers with equidistant arrangement illustrate that the method of DCCA cross-correlation coefficient can accurately analyze full-scale temporal-spatial cross-correlation between non-stationary time series and also can easily identify the seasonal component, while three traditional cross-correlation techniques (i.e., Pearson coefficient, cross-correlation function, and DCCA method) cannot give us these information directly.