Chapter 6 ______________________________________________________ Processing Algorithms
VAISALA______________________________________________________________________ 229
When thresholding dual pol, dBZ, and dBT reflectivity data with SQI, the
comparison value for accepting those data is the secondary SQI threshold
that is defined via a slope and offset from the primary user value (see
Section 4.2.3 Mf — Clutter Filters on page 112). This secondary threshold
is more permissive (lower valued), and is traditionally used to qualify LOG
data only in the Random Phase processing mode. But the secondary SQI
threshold is applied uniformly in all processing modes whenever dual pol
or reflectivity data are specified as being thresholded by SQI.
This gives you more freedom in applying an SQI threshold to your LOG
data, because the cutoff value for dual pol and reflectivity can be chosen
independently from the cutoff value for the other Doppler parameters. The
full SQI test would not normally be applied to LOG data, because of the
so-called "black hole" problem, which is the loss of LOG data within
regions of high shear, even though, for instance, the reflectivity itself was
strong. You can experiment with applying a secondary SQI threshold to
help clean up the LOG data, without introducing any significant black
holes.
6.4.3 Speckle Filters
A speckle filter is a final pass over each output ray, in which isolated bins
are removed. There are two speckle removers in the RVP900:
- 1D single-ray speckle filter (default)—This is used for any output
parameter.
PMI To optimize the PMI level, consider data acquired in the mode
of (H+V) in PPP processing. Having first optimized the
previous four Doppler qualifiers, inspect echo classification
data of DB_HCLASS and seek for gates declared as
“NoMet”, which are unlikely of meteorological origin. These
bins may appear as “NoMet” data in your display, or
DB_HCLASS data might be readily thresholded, depending
on your color scales and the HydroClass configuration. In
order to get the other data types thresholded in the same
fashion, activate PMI as a thresholding mechanism in task
configuration. The PMI threshold value to 0.45 implies the
same strength of suppression to other selected data types as
seen in DB_HCLASS. It is possible to recover more
precipitation data, typically at edges of precipitation and the
most far echoes (virga) by reducing the PMI threshold, as
appropriate. In these customizations, the behavior of
DB_HCLASS remains unchanged.
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