An ongoing challenge for cloud condensation nuclei (CCN) measurements is the inclusion of unactivated particles, which affects droplet activation parameterizations and aerosol indirect effects in models. For the first time, a reciprocal relationship between the critical diameter and critical supersaturation of activated droplets from the κ‐Köhler theory is derived to accurately count unactivated particles in CCN measurements. We conducted 4‐day continuous observations to simultaneously measure the number concentration of CCN (N_CCN) and aerosol. The results show that as supersaturation (SS) decreases from 0.186% at 25 °C, the proportion of the unactivated particles in the CCN measurements increases, reaching 88% at SS of 0.07%. Owing to the N_CCN overestimation caused by N_CCN‐SS parameterizations with uncorrected N_CCN, there is significant overestimation of aerosol indirect effects, especially under low SS conditions. After removing unactivated particles, N_CCN‐SS parameterizations are adjusted accordingly, which can improve simulations of aerosol‐cloud‐radiation interactions in models.

Both the similar and different effects of freezing drizzle, sleet and snow on microphysical properties of supercooled fog were analyzed for fourteen events during a comprehensive wire icing, fog, and precipitation observation experiment conducted at Enshi radar station (30°17′N, 109°16′E; 1722 m a.s.l.) on a hilltop in Shibanling, Hubei, China. Liquid precipitation is in a relatively stable form in mountainous areas. Short-term precipitation (1–3 h) is dominant with temperature below 0 °C. The wet scavenging effect of freezing drizzle on small fog droplets with a size range less than 6–12 μm is weak but is stronger for fog droplets with a larger size, which is opposite to the effects of solid precipitation, broadening the fog droplet spectra significantly. As the fog droplet diameter increases, the distributions of droplet spectra change from leptokurtosis to platykurtosis and from positive skewness to negative skewness. Occurrence of freezing drizzle would improve the positive correlation of N-r in dissipation and oscillation periods, resulting in the N-r relationship having a weak negative correlation in the maturity period, and resulting in the transition of the N-L and N-r relationships into positive correlations in the development period. Meanwhile, the emergence of solid precipitation particles would result in negative values for the correlation coefficients of N-L and N-r. The change in relationships among the microphysical properties was caused by the occurrence of different phase precipitation, showing the influence on the main microphysical mechanisms of supercooled fog.

Research on three types of wire icing evolution mechanism is of great significance for ice disaster recognition and prediction. Comprehensive field observations of wire icing were conducted in the winters of 2015/2016 and 2016/2017 at the Lushan Mountain Meteorological Bureau Observatory (elevation 1164.5 m), Jiangxi Province, China, and the ice thickness, weather phenomena, meteorological elements, and fog spectrum were measured. Four icing cases were recorded, in which high ice-producing conditions, such as freezing rain, snow and supercooled fog, occurred. The icing growth and shedding mechanisms in three types of weather (freezing rain, snow, and supercooled fog) were analyzed and compared. Considering mixed ice-producing conditions, the ice thickness was simulated by integrating freezing rain, snow, and supercooled fog icing models, with inputs such as the precipitation rate and wind speed. The average measured icing growth rates in freezing rain, snow, and supercooled fog were 0.4, 0.3, and 0.2 mm h⁻¹, respectively. The correlations between the icing growth rate and both the temperature and the wind speed were stronger in the snow and supercooled fog than in freezing rain. With the decreasing temperature, the icing growth rate increased faster with snow, while that in supercooled fog increased faster as the wind speed increased. In freezing rain, snow, and supercooled fog, the icing growth rates were all positively correlated with the ice thickness, with correlation coefficients of 0.55, 0.67, and 0.79, respectively. Ice was shed when the temperature remained below 0 °C, the wind speed fell to 2.7 m s⁻¹, and the fog liquid water content fell to 0.036 g m⁻³ in supercooled fog, and when the solar radiation increased and the temperature exceeded 0 °C in freezing rain. The average sticking efficiency of the wire to snow was 0.03; its variation range was 0.01–0.10. The integrated model can simulate the changes in actual ice thickness well.

The precipitation and raindrop size distribution (RSD) characteristics of the four seasons and different rain types were studied using a PARSIVEL² raindrop disdrometer set in the southwest mountain areas of China from 2019 to 2021. The seasonal precipitation in the southwest mountain areas was mainly stratiform rain. The peaks of the RSD were about 1–2 orders of magnitude higher than those in the plains. The convective rain in spring and autumn was very close to the ocean-like convective mass. The local shape–slope (μ–Λ), radar reflectivity–rain rate (Z–R), and kinetic energy–rain rate (KE–R) relationships were further derived, and the diversity of these relationships was mainly due to the variability of the RSDs. In addition, the differences in the RSD characteristics between the top and the foot of the mountain during a typical precipitation process in the summer of 2020 were further compared. It was found that the number density of the small particles at the top of the mountain was higher than that at the foot of the mountain due to the broken large raindrops caused by the high wind speed, while the high evaporation rate, strong convective available potential energy (CPAE), and water vapor content at the foot of the mountain could strengthen the RSD, making the number density of the large raindrops at the foot of the mountain higher than that at the top.

This study utilizes six years of hourly meteorological data from seven observation stations in the Beibu Gulf—Qinzhou (QZ), Fangcheng (FC), Beihai (BH), Fangchenggang (FCG), Dongxing (DX), Weizhou Island (WZ), and Hepu (HP)—over the period from 2016 to 2021. It examines the diurnal variations of sea fog occurrence and compares the performance of three machine learning (ML) models—Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Categorical Boosting (CatBoost)—in predicting visibility associated with sea fog in the Beibu Gulf. The results show that sea fog occurs more frequently during the nighttime than during the daytime, primarily due to day-night differences in air temperature, specific humidity, wind speed, and wind direction. To predict visibility associated with sea fog, these variables, along with temperature-dew point differences ($T_{\mathrm{a}}-T_{\mathrm{d}}$), pressure (p), month, day, hour, and wind components, were used as feature variables in the three ML models. Although all the models performed satisfactorily in predicting visibility, XGBoost demonstrated the best performance among them, with its predicted visibility values closely matching the observed low visibility in the Beibu Gulf. However, the performance of these models varies by station, suggesting that additional feature variables, such as geographical or topographical variables, may be needed for training the models and improving their accuracy.