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).