In this study, we investigate the most severe East Asian dust storm in the past decade that occurred on 14–16 March 2021 based on the Weather Research and Forecasting model coupled with chemistry (WRF-Chem) and a variety of site measurements and satellite retrievals. The dust emissions from the Gobi Desert, especially over Mongolia on March 14, are the dominant sources of this intense dust event. The maximal hourly accumulated dust emissions over Mongolian and Chinese areas reached 1490.18 kt at 07:00 UTC on 14 March and 821.70 kt at 2:00 UTC on 15 March, respectively. During this dust event, the accumulated dust emissions in coarse modes (i.e., bin 4 and bin 5) account for 64.1% of the total dust emission mass, and the accumulated dust emissions in fine modes (i.e., bin 1) are the least, accounting for 7.6% of the total dust emission mass. Because the coarse mode bins of dust dominate the emissions, the downwind transported coarse mode particles can affect the North China Plain, while the fine particles can only affect the desert source and its surrounding regions such as the Gansu and Ningxia provinces. Due to the dust emissions and the dust transport path, the high AOD areas are located in the Gobi Desert and Northwest China and the vertical spatial distributions of aerosol extinction coefficients have the same characteristics. We also found the model drawback of overestimating simulated wind speeds, which leads to the overestimations of dust emissions and concentrations, indicating the urgency of improving the simulated wind field.

Although the Weather Research and Forecasting model with solar extensions (WRF-Solar) is tailed for solar energy applications, its official version lacks the consideration of the online aerosol-radiation process. To overcome this limitation, we have coupled the aerosol module online with the radiation module, then assimilated the high-resolution aerosol optical depth (AOD) from the Himawari-8 next-generation geostationary satellite using a three-dimensional variational (3DVAR) AOD data assimilation system to optimize the irradiance predictions with the better aerosol–radiation interaction. The results show that data assimilation can significantly eliminate the AOD underestimations and reasonably reproduce the AOD temporal distributions, improving 51.63% for biases and 61.29% for correlation coefficients. Compared with the original WRF-Solar version, coupled online with an advanced aerosol module minifies the bias value of global horizontal irradiance (GHI) up to 44.52%, and AOD data assimilation contributes to a further reduction of 17.43%.

As part of the Blue-Sky Protection Campaign, we develop the Chinese Blue Days Index based on meteorology data from 385 stations in China during 1980–2014. This index is defined as the days with no rain, low cloud cover ≤75th percentile, and visibility ≥15 km at 2 pm. The spatio-temporal variations and possible driving factors of Chinese Blue Days (CBD) are further investigated, revealing a steadily rising rate of 1.6 day (d)/10 year (y) for the nationally averaged CBD during 1980–2014. At regional scales, the CBD exhibit an increasing trend >4 d/10 y in western China and a decreasing trend <−2 d/10 y in southeastern China, northwestern Xinjiang, and Qinghai. The minimum/maximum trends (−7.5/9.5 d/10 y) appear in Yangtze–Huai River Valley (YHRV)/southwestern China (SWC). The interannual variations in CBD are highly related to wind speed and windless days in YHRV but are closely associated with wind speed, rainless days and relative humidity in SWC, suggesting that the two regions are governed by different meteorological factors. Moreover, a dynamic adjustment method called partial least squares is used to remove the atmospheric circulation-related CBD trend. The residual CBD contributions for the total trend in summer and winter are 43.62% and 35.84% in YHRV and are 14.25% and 60.38% in SWC. The result indicates that considerable parts of the CBD trend are due to the change of atmospheric circulation in the two regions.

The online coupling of aerosols and clouds and its effect on surface global horizontal irradiance (GHI) has not yet been thoroughly investigated in the Weather Research and Forecasting Model with Solar extensions (WRF-Solar), despite its potential significance for solar energy applications. This study addresses this critical gap by implementing a computationally efficient, coupled aerosol–cloud scheme and evaluating its impacts on GHI predictability. Simulations with online aerosol–cloud coupling are systematically compared to uncoupled simulations during March 2021, a period marked by two distinct pollution episodes over north China. The online coupling enhances aerosol optical depth (AOD) simulations, increasing the correlation coefficient from 0.19 to 0.51 while reducing the absolute bias from 0.54 to 0.48 and root mean square error from 0.82 to 0.72, compared to uncoupled simulations. Enhanced cloud microphysics (droplet concentration, water path) yields better cloud optical depth estimates, reducing all-sky GHI bias by 14.5% (63.5 W/m² for the uncoupled scenario and 54.3 W/m² for the coupled scenario) through improved aerosol–cloud–meteorology interactions. Notably, the simultaneous spatiotemporal improvement of both AOD and GHI suggests enhanced internal consistency in aerosol–cloud–radiation interactions, which is crucial for operational solar irradiance forecasting in pollution-prone regions. The results also highlight the practical value of incorporating online aerosol coupling in solar forecasting models.