The effects of radiation heating and cooling on cumulus cloud development have been the focus of considerable attention for many years. However, it is still not clear how radiation impacts cloud droplet growth. Since cloud inhomogeneity has a great influence on radiation transmission, we coupled the 3D atmospheric radiative transfer model using the spherical harmonic discrete ordinate method with WRF-LES, which can improve the simulation accuracy of the inhomogeneous effect of clouds on radiation compared with that of the 1D radiation method. The shortwave and longwave radiation fluxes for upward and downward directions were simulated with different solar zenith angles. The comparison of 1D and 3D radiative solvers for deep convective cloud cases shows that the 3D radiative solver provides an accurate structure of solar and thermal radiation characteristics and the spatial distribution field. The solar radiation heating is likely to increase perpendicular to the solar incidence direction. For longwave radiation, the cooling effect on the cloud top and the heating effect on the cloud base are both more intense in the 3D radiation model. This study focuses on 3D cloud-radiative interactions in an inhomogeneous cloud field in a large eddy simulation, and the results suggest that compared with the widely used 1D radiative solver in WRF, the 3D radiation model can provide a precise description of the radiation field in an inhomogeneous atmosphere.

Aerosol optical depth (AOD), an important indicator of atmospheric aerosol load, characterizes the impacts of aerosol on radiation balance and atmospheric turbidity. The nesting Elterman model and a spatiotemporal linear mixed-effects (ST-LME) model, which is referred to as the ST-Elterman retrieval model (ST-ERM), was employed to improve the temporal resolution of AOD prediction. This model produces daily AOD in the Southern Central Hebei Plain (SCHP) region, China. Results show that the ST-ERM can effectively capture the variability of correlations between daily AOD and meteorological variables. After being validated against the daily Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOD, the correlation coefficient between daily retrieved AOD from ST-ERM and MAIAC observations in 2017 reached 0.823. The validated Nash–Sutcliffe efficiency (E_f) of daily MAIAC AOD and ST-ERM-retrieved AOD is greater than or equal to 0.50 at 72 of the 95 stations in 2017. The relative error (E_r) is less than 14% at all the stations except for Shijiazhuang (17.5%), Fengfeng (17.8%), and Raoyang (30.1%) stations. The ST-ERM significantly outperforms the conventional meteorology–AOD prediction approaches, such as the revised Elterman retrieval model (R-ERM). Thus, the ST-ERM shows great potential for daily AOD estimation in study regions with missingness of data.