Practical Considerations for Computing Dimensional Spectra from Gridded Data
Practical Considerations for Computing Dimensional Spectra from Gridded Data
复制标题
从网格数据计算维谱的实际注意事项
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
M. Menchaca
中科院分区：
文献类型：
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作者：
D. Durran;Jonathan A. Weyn;M. Menchaca
光谱通常是从网格数据计算，以确定水平尺度依赖的数量，如动能，垂直速度，或扰动位温。本文讨论了几个重要的考虑，这种光谱的实际计算。为了确保波数空间中的谱能量密度之和与物理域中的能量之和相匹配（离散Parseval关系），乘以谱能量密度的常系数必须适当地考虑离散傅立叶变换对被归一化的方式。许多旧的基于Fortran的快速傅立叶变换（FFT）的归一化因子与Matlab和Python的numpy.fft中的不同，因此，使用这两种方法计算的一维FFT之间的动能（KE）谱密度的正确比例因子与物理网格点数量的平方因子不同。一种常用的算法...
AbstractSpectra are often computed from gridded data to determine the horizontal-scale dependence of quantities such as kinetic energy, vertical velocity, or perturbation potential temperature. This paper discusses several important considerations for the practical computation of such spectra. To ensure that the sum of the spectral energy densities in wavenumber space matches the sum of the energies in the physical domain (the discrete Parseval relation), the constant coefficient multiplying the spectral energy density must properly account for the way the discrete Fourier transform pair is normalized. The normalization factor appropriate of many older FORTRAN-based fast Fourier transforms (FFTs) differs from that in Matlab and Python’s numpy.fft, and as a consequence, the correct scaling factor for the kinetic energy (KE) spectral density differs between one-dimensional FFTs computed using these two approaches by a factor equal to the square of the number of physical grid points. A common algorithm used ...