2.2. Convective-Stratiform separation
Several studies have been done for the classification of precipitation
into convective and stratiform parts using in-situ measurements
(Tokay & Short, 1996; Testud et al., 2001; Bringi et al., 2003) and
weather radars (Steiner et al., 1995; Williams et al., 1995; Biggerstaff
& Listemaa, 2000; Ulbrich & Atlas, 2002; Thurai et al., 2010).
Convective and stratiform parts of the cloud systems exhibit
significantly different behaviours in terms of dynamics as well as
microphysics (Houze, 1997). Vertical air motions within these two
portions of a cloud system differ significantly; convective parts are
mainly driven by large narrow updrafts (5–10 m s-1 or
more), while stratiform portions are governed by gentler mesoscale
ascents (< 3 m s-1). Thus, microphysical
processes responsible for particle growth within the convective and
stratiform parts are very different. Particles within convective cores
regions mainly grow by riming or accretion (collection of supercooled
liquid water droplets onto the ice particle surface), which leads to
large/dense hydrometeors, whereas in the stratiform region vapour
deposition and aggregation are dominating processes that lead to smaller
and less dense ice hydrometeors (though large aggregates may exist).
The convective-stratiform separation method of Steiner et al. (1995),
which is based on the texture of the radar reflectivity field is adopted
for the present study and is widely used by the radar community. This
method basically checks for two criteria viz. intensity orpeakedness criteria on the horizontal reflectivity field at 3 km
height, to identify a grid point (pixel) as a convective center. Any
grid point with reflectivity at least 40 dBZ (intensity criteria) or
greater than a fluctuating threshold (peakedness criterion) depending on
the area-averaged background reflectivity (Zbgcalculated within a radius of 11km around the grid point), is considered
as a convective center. For each pixel identified as a convective
center, all surrounding pixels within a certain radius of influence are
also included as convective pixels. This radius of influence is
dependent on Zbg. Once all the convective pixels are
identified, the rest of the pixels with non-zero reflectivity values are
assigned as stratiform pixels.
2.3. Φdp data processing and
Kdp calculation
The differential propagation phase (Φdp) is the phase
difference between the horizontal and vertical polarized pulses on
traversing through the atmosphere. The differential propagation phase is
proportional to the water content along a rain path. Since, most of the
hydrometeors in the atmosphere are aligned with their major axis in the
horizontal plane and it’s a range cumulative parameter, the value of
Φdp increases with propagation path. Now, the
unambiguous range of Φdp usually is 180° in the
alternate H/V transmission mode and 360° in the simultaneous H/V
transmission mode. Hence, for a long propagation path in rain,
Φdp values can easily exceed the unambiguous range and
then the Φdp will be wrapped/folded which is usually
manifested as a sudden jump in the range profiles of
Φdp. This issue with Φdp is known as
phase wrapping/folding (Wang & Chandrasekar, 2009; You et al., 2014).
The unfolding of these phases has been done by adding appropriate phase
offset (You et al., 2014). So, even after the quality control steps
mentioned in the previous section, Φdp needs this extra
processing before it can be used in further analysis. In Figure 3a, such
a situation of phase wrapping is observed towards 15 km west of the
radar during a convective event. Then the phase are unfolded nicely and
the unfolded Φdp is shown in Figure 3b.
Specific differential phase (Kdp) is defined as the
slope of range profiles of Φdp (Seliga & Bringi, 1978;
Jameson, 1985; Bringi & Chandrasekar, 2001) and is defined as follows.
\begin{equation}
\text{\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ K}_{\text{dp}}\left(r\right)=\left[\frac{\Phi_{\text{dp}}\left(r+\frac{\Delta r}{2}\right)-\Phi_{\text{dp}}\left(r-\frac{\Delta r}{2}\right)}{2r}\right]\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ (1)\nonumber \\
\end{equation}Kdp is an important parameter for meteorological
applications as it is closely related to rain intensity. More
importantly it’s insensitive to signal attenuation during propagation,
radar calibration, partial beam blockage and the presence of hail (Aydin
et al., 1995; Zrnic & Ryzhkov, 1996). This makes specific differential
phase very useful for precipitation estimation at heavy rain intensity
or during partial beam blockage. Though the estimation of
Kdp seems quite simple, it requires further processing
of Φdp range profiles before calculating the slope.
Φdp is known to be a very noisy parameter particularly
in regions with low rain rates and the process of differentiation
increases this noise even further. To tackle this, we have applied a
low-pass butterworth filter (Parks & Burrus, 1987; Proakis &
Manolakis, 1988) of order 10 with a cut-off scale of 2 km to reduce the
statistical fluctuation but keeping the overall features intact. Similar
filters with similar cut-off scales have been used in previous studies
(Hubbert et al., 1993; Wang & Chandrasekar, 2009). Figure 4a shows the
Φdp Plan Position Indicator (PPI) at 2° elevation angle
during the convective event on 13th May after quality
control and unfolding. Then the previously mentioned filter has been
applied on this Φdp and obtained a smoothed
Φdp (Figure 4b). Small scale fluctuations in the
Φdp field are nicely removed in the filtered
Φdp. With this smoothed Φdp field
Kdp has been estimated using Equation 1 and is shown in
Figure 4c. Another Kdp estimate using slope of the
linear regression line (Balakrishnan & Zrnic, 1990) has also been
calculated. Both the methods gave similar Kdp values.
Kdp field shows high values close to 9°
km-1 at a distance of 5 to 15 km westward from the
radar, indicating presence of heavy precipitation. The blue line in this
plot represents the 281° azimuth. Along this direction original
Φdp (dot-dashed blue curve), filtered
Φdp (solid blue curve), estimated Kdp(red curves) are shown in Figure 4d. The ranges of Kdpvalues obtained here, agrees quite well with previous studies on
convective cases (Wang & Chandrasekar, 2009; Dolan et al., 2013).