This study introduces a recent field experiment investigating multiscale terrain–circulation–precipitation interactions. When a synoptic‐scale northeasterly wind prevails under the active East Asian winter monsoon, stratocumulus cloud decks with severe rainfall exceeding 100 mm·day−1 frequently occur in the northeastern plain area and adjacent mountains in Yilan, Taiwan. The Yilan Experiment of Severe Rainfall (YESR2020) is a field campaign from November 20, 2020, to November 24, 2020, to survey the physical processes leading to severe wintertime rainfall. The three‐dimensional structure of the wind field and the atmospheric environment can be identified through high temporal and spatial resolution sounding observations, which is empowered by the novel Storm Tracker mini‐radiosonde. During YESR2020, the continuously collected meteorological data of two northeasterly episodes captured the variability of local‐scale wind patterns and the features of the severe rainfall induced by stratocumulus. A preliminary analysis indicated that a local‐scale convergence line could appear over the plain area of Yilan under the northeasterly environmental condition. The precipitation hotspot was located in the mountain region of southern Yilan, where the local winds signified turbulence features. Moreover, the severe rainfall of the two northeasterly episodes spotlighted shallow cumulus under stratus with pure warm rain processes. The results of YESR2020 inspire the arrangement of future field observations to explore detailed mechanisms of heavily precipitating stratocumulus over complex topography.

Abstract. 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.

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 dr ), and a specific differential phase (K 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 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 dp (Z, Z dr ) relationship outperformed that of K dp (Z). The K dp (Z, Z dr ) relationship with seasonal coefficients and systematic bias-corrected Z 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 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 nr . Moreover, a pronounced wind intensity dependency of the WRE could be observed in the quadratic polynomial fitting equation.