USER’S MANUAL__________________________________________________________________
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6.2.2 Frequency Domain Processing-
Doppler Power Spectrum
The Doppler power spectrum, or simply the "Doppler spectrum", is the
easiest way to visualize the meteorological information content of the time
series. The bottom part of Figure 38 on page 197 shows an example of a
Doppler power spectrum for the time series shown in the upper part of the
figure. The figure above shows the various components of the Doppler
spectrum, that is, typically there is white noise, weather signal and ground
clutter. Other types of targets such as sea clutter, birds, insects, aircraft,
surface traffic, second trip echo, etc. may also be present.
The "Doppler power spectrum" is obtained by taking the magnitude
squared of the input time series, that is, for a continuous time series,
Here S denotes the power spectrum as a function of frequency ω, and f
denotes the Fourier transform of the continuous complex time series s(t).
The Doppler power spectrum is real-valued since it is the magnitude
squared of the complex Fourier transform of s(t).
In practice a pulsed radar operates with discrete rather than continuous
time series, that is, there is an I and Q value for each range bin for each
pulse. In this case we use the discrete Fourier transform or DFT to calculate
the discrete power spectrum. In the special case when we have 2n input
time series samples (for example, 16, 32, 64, 128, ...), we use the fast
Fourier transform algorithm (FFT), so called because it is significantly
faster than the full DFT.
The DFT has the form:
Typically a weighting function or "window" w
m
is applied to the input time
series s
m
to mitigate the effect of the DFT assumption of periodic time
series. The RVP900 supports different windows such as the Hamming,
Blackman, Von Han, Exact Blackman and of course the rectangular
window for which all spectral components are weighted equally. The
typical form of a spectrum window is shown in the figure below which
illustrates how the edge points of the time series are de-emphasized and the
center points are over emphasized. The dashed line would correspond to
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