In the present study, the Morrison double-moment cloud microphysics scheme including mass and droplet number concentration of water and ice clouds is implemented into the Chinese mesoscale version of the Global/Regional Assimilation and Prediction System (GRAPES_Meso). Sensitivity experiments of different cloud condensation nuclei (CCN) values are conducted to study the impacts of CCN on cloud microphysical processes and radiation processes in East China. The model evaluation shows that the simulated cloud liquid water path (CLWP) is consistent with that of the National Center for Environment Prediction (NCEP) reanalysis, and the cloud optical depth (COD) and effective radius of cloud water (Rc) are in agreement with those of the Moderate Resolution Imaging Spectroradiometer (MODIS) datasets both in regional distribution and magnitudes. These comparisons illustrate the effectiveness of the Morrison scheme for the cloud processes in East China. For the study period of 8 to 12 October 2017, the sensitivity experiments show that with initial CCN number concentration (CCN₀) increasing from 10 to 3000 cm⁻³, the maximum value of daily average Rc decreases by about 63%, which leads to a decrease of cloud-rain conversion rate. Moreover, the maximum value of daily average mixing ratio of cloud water (q_c) increases by 133%, the maximum value of daily average mixing ratio of rain (q_r) decreases by 44%, and the maximum value of daily average CLWP and COD increase by 100% and 150%, respectively. This results in about 65% increasing of the maximum value of daily average cloud downward shortwave radiative forcing (CDSRF) when CCN₀ increases from 10 cm⁻³ to 3000 cm⁻³. The study indicates the important impacts of CCN on cloud properties and radiation effects.

Low visibility (VIS) involves haze, fog, rain, snow, or their combination, while VIS prediction commonly treats aerosol and hydrometeor extinction separately. Observations and the coupled chemistry–weather model are used to quantify their contributions to low VIS in four subregions over eastern China in November 2024. Haze, fog, rain, and snow can all cause low VIS (≤10 km). For 3 < VIS ≤ 10 km and 1 < VIS ≤ 3 km, haze is the most frequent weather record in Harbin and Beijing, whereas fog prevails in Shanghai and Wuhan; for VIS ≤ 1 km, fog dominates in four cities. For 3 < VIS ≤ 10 km, aerosols contribute 87.1–97.3% of the model-estimated total extinction and hydrometeors 2.7–12.8%; for 1 < VIS ≤ 3 km, aerosols contribute 33.9–68.1% and hydrometeors 31.9–66.1%; for VIS ≤ 1 km, hydrometeors contribute 71.6–87.4%, mainly from fog, and fog and snow dominate Northeast China, while aerosols contribute 12.3–28.4%. Jointly considering aerosol and hydrometeor extinction generally produces the lowest root-mean-square error across all VIS grades and the highest threat scores for all VIS levels, especially for VIS ≤ 3 km. These results demonstrate that including both aerosol and hydrometeor extinction is essential for reliable low-VIS prediction.