Chapter 6 ______________________________________________________ Processing Algorithms
VAISALA______________________________________________________________________ 223
6.3.6 Signal Quality Index (SQI threshold)
An important feature of the RVP900 is its ability to eliminate signals which
are either too weak to be useful, or which have widths too large to justify
further analysis. This is done through SQI, which is defined as:
The SQI is the normalized magnitude of the autocorrelation at lag 1 and
varies between 0 for an uncorrelated signal (white noise) to 1 for a noise-
free zero-width signal (pure tone). Mean velocity estimates are degraded
when the spectrum, width is large or when the signal-to-noise ratio is weak.
The SQI is a good measure of the uncertainty in the velocity estimates and
is a convenient screening parameter to compute. In terms of the Gaussian
model, the SQI is :
where the SNR is the signal-to-noise ratio. For very large SNR's the SQI is
a function of the spectrum width only. For a zero-width pure tone (W=0),
the SQI is a function of the SNR only (for example, for W=0, an SNR of 1
corresponds to SQI=0.5). The SQI threshold is typically set to a value of
0.4 to 0.5.
6.3.7 Clutter Correction (CCOR threshold)
In addition to calculating the R
0
, R
1
and optional R
2
autocorrelation terms,
which are based on filtered time series data, the RVP900 also computes T
0
which is the total unfiltered power. By comparing the total filtered and
unfiltered powers at each range bin, a clutter power, and hence a clutter
correction, for that bin can be derived. The clutter correction is defined as,
where S is the weather signal power, C is the clutter power and CSR is the
clutter-to-signal ratio. The algorithm for calculating CCOR depends on
whether the optional R
2
autocorrelation lag is computed as described
below.
R0, R1, R2 Clutter Correction
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