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Wavelet spectral analysis of the temperature and wind speed data at Adrar, Algeria

机译:阿尔及利亚阿德拉尔温度和风速数据的小波频谱分析

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Spectra of many meteorological data such as wind speed and temperature are time variable. Thus a Fourier analysis is not sufficient. In the present work, the wavelet transform is applied as a time-frequency analysis to the meteorological data for the region of Adrar (27.9°N, 0.3°W, 263 m), Algeria. This analysis is carried out in order to investigate the power spectra behaviors of both temperature and wind speed and their variations with time.rnTo determine the relationship between these two meteorological parameters, the cross wavelet analysis is also applied. The study is carried out using data extending over a period of four years. The analysis is applied over a frequency range from 0.002 to 0.5 cycles per day.rnThe results show that significant synoptic oscillations of periods between 2 and 16 days occur mainly in the cold season in both wind and temperature time series. Those oscillations are characterized by short life durations of one to few weeks. Wavelet power spectrum has also revealed the presence of intra-seasonal oscillations of periods between 30 and 60 days. These intra-seasonal oscillations have been observed mainly in the warm seasons. This study reveals also that temperature and wind speed co-vary especially at the synoptic and the intra-seasonal frequencies.
机译:许多气象数据的频谱(例如风速和温度)是随时间变化的。因此,傅立叶分析是不够的。在目前的工作中,小波变换作为时频分析应用于阿尔及利亚阿德拉尔地区(27.9°N,0.3°W,263 m)的气象数据。进行此分析是为了研究温度和风速的功率谱特性及其随时间的变化。为了确定这两个气象参数之间的关系,还应用了交叉小波分析。这项研究使用了长达四年的数据。该分析在每天0.002到0.5个周期的频率范围内进行。结果表明,2到16天之间周期的明显天气振荡主要发生在风和温度时间序列的寒冷季节。这些振荡的特征是寿命短至一到几周。小波功率谱还显示了30到60天之间的季节内振荡的存在。这些季节内振荡主要在温暖季节被观察到。这项研究还揭示出温度和风速会发生变化,尤其是在天气和季节内频率处。

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