Four types (2D-video disdrometer: 2DVD; precipitation occurrence sensor system: POSS; micro-rain radar: MRR; and Joss–Waldvogel disdrometer: JWD) of sixteen instruments were collocated within a square area of 400 m² from 16 April to 8 May 2008 for intercomparison of drop size distribution (DSD) of rain. This unique dataset was used to study the inherent measurement uncertainty due to the diversity of the measuring principles and sampling sizes of the four types of instruments. The DSD intercomparison shows generally good agreement among them, except that the POSS and MRR had higher concentrations of small raindrops (5.2 mm). The measurement uncertainty ( $\mathsf{\sigma }$ ) was obtained quantitatively after considering the zero or non-zero measurement error covariance between two instruments of the same type. The results indicate the measurement uncertainties were found to be neither independent nor identical among the same type of instruments. The MRR is relatively accurate (lower $\mathsf{\sigma }$ ) due to large sampling volumes and accurate measurement of the Doppler power spectrum. The JWD is the least accurate due to the small sampling volumes. The $\mathsf{\sigma }$ decreases rapidly with increasing time-averaging window. The 2DVD shows the best accuracy of R in longer averaging time, but this is not true for Z due to the small sampling volume. The MRR outperformed other instruments for Z for entire averaging time due to its measuring principle.

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.

To evaluate the hygroscopic cloud seeding in reality, this study develops a hybrid microphysics scheme using a Weather Research and Forecasting (WRF) model, WDM6-NCU (WDM6 modified by National Central University), which involves 43 bins of seeded cloud condensation nuclei (CCN) in the WDM6 bulk method scheme. This scheme can describe the size distribution of seeded CCN and explain the process of the CCN imbedding and cloud and raindrop formation in detail. Furthermore, based on the observational CCN size distribution applied in the modelling, a series of tests on cloud seeding were conducted during the seeding periods of 21–22 October 2020 with stratocumulus clouds. The model simulation results reveal that seeding in in-cloud regions with an appropriate CCN size distribution can yield greater rainfall and that spreading the seeding agents over an area of 40–60 km² is the most efficient strategy to create a sufficient precipitation rate. With regard to the microphysical processes, the main process that causes the enhancement of precipitation is the strengthening of the accretion process of raindrops. In addition, hygroscopic particles larger than 0.4 µm primarily contribute to cloud-seeding effects. The study results could be used as references for model development and warm-cloud-seeding operations.

The key factors, namely, the radar data quality, raindrop size distribution (RSD) variability, and the data integration method, which significantly affect radar-based quantitative precipitation estimation (QPE) are investigated using the RCWF (S-band) and NCU C-POL (C-band) dual-polarization radars in northern Taiwan. The radar data quality control (QC) procedures, including the corrections of attenuation, the systematic bias, and the wet-radome effect, have large impact on the QPE accuracy. With the proper QC procedures, the values of normalized root mean square error (NRMSE) decrease about 10~40% for R(Z_HH) and about 5~15% for R(K_DP). The QPE error from the RSD variability is mitigated by applying seasonal coefficients derived from eight-year disdrometer data. Instead of using discrete QPEs (D-QPE) from one radar, the synthetic QPEs are derived via discretely combined QPEs (DC-QPE) from S- and C-band radars. The improvements in DC-QPE compared to D-QPE are about 1.5–7.0% and 3.5–8.5% in R(K_DP) and R(K_DP, Z_DR), respectively. A novel algorithm, Lagrangian-evolution adjustment (LEA), is proposed to compensate D-QPE from a single radar. The LEA-QPE shows 1–4% improvements in R(K_DP, Z_DR) at the C-band radar, which has a larger scanning temporal gap (up to 10 min). The synthetic LEA-QPEs by combining two radars have outperformed both D-QPEs and DC-QPEs.

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.

A new method is developed to derive the bulk density and bulk water fraction of a population of particles from collocated measurements from the Micro-Rain Radar (MRR) and Particle Size and Velocity disdrometer (Parsivel). A rigorous particle-scattering simulation, namely the T-matrix method, is applied to Parsivel's particle size distribution data to calculate the reflectivity (Z_HH). The possible combinations of the particle's ice, air, and water are derived to compare them with the MRR-measured Z_HH. The combination of the minimum water fraction and maximum ice fraction subsequently determines the bulk density (ρ_bulk). The proposed method is applied to the data collected from the International Collaborative Experiments for Pyeongchang 2018 Olympic and Paralympic winter games (ICE-POP 2018) projects and its pre-campaign. The estimated ρ_bulk was examined independently by a comparison of the liquid-equivalent snowfall rate (SR) of collocated Pluvio devices. The bias values are adequately low (SR: −0.25–0.06 mm h⁻¹). The retrieved bulk density also shows good consistency with collocated Precipitation Imaging Package (PIP) retrievals. The results indicate the capability of the proposed algorithm to derive reliable ρ_bulk, leveraging the compact and easily deployable designs of MRR and Parsivel. The derived bulk density of the two warm–low cases (28 February and 7 March 2018) shares a similar transition as the systems were decaying. The higher bulk density and bulk water fraction were found in the coastal sites (BKC and GWU have a median value of ρ_bulk and are 0.05 to 0.12 g cm⁻³), typically accompanied by higher liquid-water constituents (mean values of the top 5 % bulk water fraction are 0.07 to 0.45) than the inland sites (YPO and MHS have a median value of ρ_bulk and are 0.06 to 0.10, and mean values of the top 5 % bulk water fraction are 0.001 to 0.008) during such synoptic conditions.