Spectral Analysis of Turbulent Aerosol Fluxes by Fourier Transform, Wavelet Analysis, and Multiresolution Decomposition
Spectral Analysis of Turbulent Aerosol Fluxes by Fourier Transform, Wavelet Analysis, and Multiresolution Decomposition
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DOI：
10.1007/s10546-013-9889-8
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发表时间：
2014-04
影响因子：
4.3
通讯作者：
A. Held
中科院分区：
文献类型：
--
作者：
A. Held
利用快速傅立叶变换、小波分析和多分辨率分解对气溶胶数通量进行了谱分析。所有这三种方法产生类似的频谱特征一般，虽然详细的评估的cospectra显示出一些差异，例如，由于不同的分辨率在时域和频域。小波分析产生气溶胶通量估计具有高的时间分辨率，可用于评估通量的变化。多分辨率分解已成功地应用于评估的气溶胶数通量，浮力通量和动量通量的3个1天的数据集从不同的环境的共谱。对于所有的标量和所有的环境，无量纲频率（f）的共谱峰被发现之间和0.2。此外，气溶胶数通量的同谱间隙时间尺度在100和1,000 s之间。因此，在这项研究中，一些光谱特征，如占主导地位的时间尺度和气溶胶数通量的同谱间隙时间尺度相似的浮力通量。然而，气溶胶数通量共谱的形状往往偏离浮力和动量通量共谱，特别是在很小和很大的时间尺度。
Aerosol number fluxes are spectrally analyzed using fast Fourier transform analysis, wavelet analysis and multiresolution decomposition. All three methods yield similar spectral features in general, although a detailed evaluation of the cospectra shows some differences, e.g. due to different resolutions in the time and frequency domains. Wavelet analysis yields aerosol flux estimates with a high time resolution that can be used to assess the flux variability. Multiresolution decomposition has been applied successfully to evaluate cospectra of the aerosol number flux, the buoyancy flux and the momentum flux of three 1-day datasets from diverse environments. For all scalars and all environments, the dimensionless frequency (f) of the cospectral peak was found betweenand 0.2. In addition, the cospectral gap time scale of the aerosol number flux was found between 100 and 1,000 s. Thus, in this study several spectral features such as the dominant time scale and the cospectral gap time scale of aerosol number fluxes are similar to buoyancy fluxes. However, the shape of aerosol number flux cospectra often deviates from buoyancy and momentum flux cospectra, especially at very small and at very large time scales.