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Multiscale multifractal analysis of near-surface wind speed time series

机译:近地表风速时间序列的多尺度多重分形分析

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Understanding the temporal/spatial behavior and dynamics of wind speed is of great importance for model building, wind prediction and sensor design. In the present study, we employ a recently proposed approach of multiscale multifractal analysis (MMA) to investigate the scaling properties of a group of wind speed time series recorded at ten locations with 1m interval in line. The results of MMA show that all wind speed time series not only exhibit strong multifractal properties, but also that these properties depend on the time scale, indicating the great necessity to study wind speed time series from multiple scales perspective. Subsequent analysis of shuffled and surrogate series reveals that the multifractality of wind speed time series is mainly stemming from the long-range correlation, while has less to do with broad probability density function. Furthermore, based on the results of Hurst surface analysis, we find that the scaling behaviors of all wind speed time series are similar for large fluctuations, while exhibit apparent distinction for small fluctuations.
机译:了解风速的时空行为和动态特性对于模型构建,风能预测和传感器设计非常重要。在本研究中,我们采用一种最近提出的多尺度多重分形分析(MMA)方法来研究一组风速时间序列在10个位置(线距为1m)记录的比例缩放特性。 MMA的结果表明,所有风速时间序列不仅表现出很强的多重分形特性,而且这些特性都取决于时间尺度,这表明从多尺度角度研究风速时间序列非常必要。随后对改组和替代序列的分析表明,风速时间序列的多重性主要源于远距离相关性,而与广泛的概率密度函数无关。此外,基于赫斯特表面分析的结果,我们发现所有风速时间序列的缩放行为对于大的波动都是相似的,而对于小波动则表现出明显的区别。

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