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A new method is developed to derive the hydrometer's bulk density and bulk water fraction from collocated measurements from Micro-Rain Radar (MRR) and Particle Size and Velocity disdrometer (Parsivel). Rigorous particle scattering simulation, namely the T-matrix method, is applied to particle size distribution data of Parsivel to calculate the reflectivity (ZHH). The possible combinations of the particle's ice, air, and water are derived to compare them with the MRR-measured ZHH. The combination of minimum water fraction subsequently determines the bulk density (ρbulk). 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 was examined by self-evaluation of reflectivity weighted fall velocity (Vz) of MRR and independent comparison of the liquid-equivalent snowfall rate (SR) of collocated Pluvio. The bias values are adequately low (Vz: -0.27~0.14 m s-1, SR: 0.52~0.74 mm hr-1). The correlation coefficient of calculated SR from estimated ρbulk and observed SR from Pluvio can be up to 0.74. The results indicate the capability to derive reliable ρbulk through the proposed method, leveraging the compact and easily deployable designs of MRR and Parsivel. The derived bulk density of the two warm-low cases (28 February and 07 March 2018) shares a similar transition as the systems were decaying. The particles with higher bulk density and bulk water fraction were found in the coastal sites (BKC and GWU: mean ρbulk values are 0.12 to 0.25 g cm-3), typically accompanied by higher liquid-water constituents (mean values of the top 5 % bulk water fraction are 0.33 to 0.50) than the inland sites (YPO and MHS: mean ρbulk values are 0.09 to 0.08 and mean values of the top 5 % bulk water fraction are 0.001 to 0.029) during such synoptic conditions. [ABSTRACT FROM AUTHOR]

This study selected three heavy-rainfall events of different types in Taiwan's Mei-yu season for high-resolution simulations at a grid size of 1 km and assessed the model's capability to reproduce their morphology and characteristics. The three cases include a pre-frontal squall line, a mesoscale convective system (MCS) embedded in southwesterly flow, and a local convection near the front in southern Taiwan during the South-West Monsoon Experiment (SoWMEX) in 2008, chosen mainly because of the availability of the S-band polarimetric (S-Pol) radar observations, and especially the particle identification results. The simulations using the Cloud-Resolving Storm Simulator (CReSS) could reproduce all three corresponding rainfall systems at roughly the correct time and location, including their kinematic structures such as system-relative flows with minor differences, although the cells appeared to be coarser and wider than the S-Pol observations. The double-moment cold-rain microphysics scheme of the model could also capture the general distributions of hydrometeors, such as heavy rainfall below the updraft core with lighter rainfall farther away below the melting level, and graupel and mixed-phase particles in the upper part of the updraft with snow and ice crystals in stratiform areas between updrafts above the melting level. Near the melting level, the coexistence of rain and snow corresponds to wet snow in the observations. Differences in cloud characteristics in the events are also reflected in the model results to some extent. Overall, the model's performance in the simulation of hydrometeors exhibits good agreement with the observation and appears reasonable. [ABSTRACT FROM AUTHOR]

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 km 2 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 µ m primarily contribute to cloud-seeding effects. The study results could be used as references for model development and warm-cloud-seeding operations. [ABSTRACT FROM AUTHOR]

To evaluate the hygroscopic cloud seeding in reality, this study develops a hybrid microphysics scheme on WRF model, WDM6–NCU, 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 CCNs and explain the process of the CCN imbedding, 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 was conducted during the seeding periods of 21–22 October, 2020 with stratocumulus clouds. The model simulation results reveal that seeding at 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. [ABSTRACT FROM AUTHOR]

Error averaging effect is one of the advantages of a parallel mechanism when individual errors are relatively large. However, further investigation is necessary to clarify the evidence with mathematical analysis and experiment. In the developed parallel coordinate measuring machine (PCMM), which is based on three pairs of prismatic-universal-universal joints (3-PUU), error averaging mechanism was investigated and is analyzed in this report. Firstly, the error transfer coefficients of various errors in the PCMM were studied based on the established error transfer model. It can be shown how the various original errors in the parallel mechanism are averaged and reduced. Secondly, experimental measurements were carried out, including angular errors and straightness errors of three moving sliders. Lastly, solving the inverse kinematics by numerical method of iteration, it can be seen that the final measuring errors of the moving platform of PCMM can be reduced by the error averaging effect in comparison with the attributed geometric errors of three moving slides. This study reveals the significance of the error averaging effect for a PCMM.

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. [ABSTRACT FROM AUTHOR]