The current study summarizes the raindrop size distributions (RSDs) characteristic of the North Indian Ocean (NIO) tropical cyclones (TCs) measured with ground-based disdrometers installed at the coastal (Thiruvananthapuram, 8.5335°N, 76.9047°E) and inland (Kadapa, 14.4742°N, 78.7098°E) stations in south India. The NIO TCs observed at the coastal station showed more mid- and large-size drops (>1 mm) than the inland station. On the other hand, for both inland and coastal stations, small and mid-size drops (<3 mm) primarily contributed to the total number concentration and rainfall rate. The RSDs of the NIO TCs segregated into precipitation types (stratiform and convective) demonstrated the presence of more mid- and large-size drops at the coastal station. The RSD relations of the NIO TCs, which are used in rain retrieval algorithms of remote sensing (global precipitation measurement) radars, exhibited contrasts between the coastal and inland station. Further, the NIO TCs’ rainfall kinetic energy relations, which are crucial in rainfall erosivity studies, estimated for the coastal station revealed dissimilar characteristics to that of the inland station. The conceivable thermo-dynamical and microphysical processes that are accountable for the disparities in the NIO TCs RSDs measured at the coastal and inland stations are also elucidated in this work.

Tools to identify and classify stratiform and convective rains at various times of the 12 days from June 2015 to March 2016 in Jincheon, Korea, were developed by using a Parsivel disdrometer and S-band polarimetric (S-POL) radar data. Stratiform and convective rains were identified using three different methods (vertical profile of reflectivity (VPR), the method proposed by Bringi et al. (BR03), and a combination of the two (BR03-VPR)) by using a Parsivel disdrometer for its applications to radar as a reference. BR03-VPR exhibits a better classification scheme than the VPR and BR03 methods. The rain types were compared using the drop size distribution (DSD) retrieved from polarimetric variables and reflectivity only. By using the DSD variables, a new convective/stratiform classification line of the log-normalized droplet number concentration ( ${log}_{10}{N}_w)-$ median volume diameter ( $D_0$ ) was derived for this area to classify the rainfall types using DSD variables retrieved from the polarimetric radar. For the radar variables, the method by Steiner et al. (SHY95) was found to be the best method, with 0.00% misclassification of the stratiform rains. For the convective rains, the DSD retrieval method performed better. However, for both stratiform and convective rains, the fuzzy method performed better than the SHY95 and DSD retrieval methods.

This study assessed the long-term radar reflectivity ( $Z$ ) biases of collocated S- and C-band dual-polarization radars. The systematic bias, wet-radome effect (WRE), and attenuation effect were investigated. The algorithm of self-consistency utilizes $Z$ , differential reflectivity ( $Z_{\mathrm{ dr}}$ ), and a specific differential phase ( $K_{\mathrm{ dp}}$ ) to estimate the systematic bias and WRE of both radars. Eleven years of disdrometer data in northern Taiwan were used to obtain the self-consistency and $K_{\mathrm{ dp}}$ -based attenuation correction relation coefficients. Subsequently, a series of sensitivity tests were conducted to examine the influence of these coefficients on bias and attenuation corrections. The $K_{\mathrm{ dp}}(Z,Z_{\mathrm{ dr}})$ relationship outperformed that of $K_{\mathrm{ dp}}(Z)$ . The $K_{\mathrm{ dp}}(Z,Z_{\mathrm{ dr}})$ relationship with seasonal coefficients and systematic bias-corrected $Z_{\mathrm{ dr}}$ constituted the optimal procedure. The corrected $Z$ of collocated radars was in good agreement, lending further validity to the correction schemes. The results demonstrated that the stable systematic bias values of two radars were −1.89 to −1.14 dB and −2.46 to −1.87 dB. During the WRE period, additional underestimations of $Z$ by nearly 4 and 7 dB were recorded for S- and C-band radars, respectively. The mean value of radar reflectivity near radar ( $Z_{\mathrm{ nr}}$ ) was obtained to identify the WRE period. In this study, an innovative quadratic polynomial fitting equation was proposed to investigate the systematic and WRE biases using $Z_{\mathrm{ nr}}$ . Moreover, a pronounced wind intensity dependency of the WRE could be observed in the quadratic polynomial fitting equation.