To accurately estimate outgoing top-of-atmosphere (TOA) shortwave (SW) fluxes from measurements of broadband radiances, angular distribution models (ADMs) are necessary. ADMs rely on radiance-predicting models that are trained on hemispherically-resolved CERES TOA radiance observations. The estimation of SW fluxes is particularly challenging for cloudy skies due to clouds' anisotropy, which substantially varies with their optical properties for any given sun-object-observer geometry. The aim of this study is to investigate the influence of micro- and macrophysical properties of liquid clouds on SW fluxes estimated by ADMs that are based on a semi-physical model and compare to operational ADMs. We hypothesize that a microphysically aware ADM performs better in observation angles influenced by single-scattering features. The semi-physical approach relies on a parameterized asymmetry parameter gΔ, which depends on the cloud effective radius and, after adjustments during training of the model, explicitly varies with sun–observer geometry. We link these adjustments to single scattering features, such as the shift of the cloud bow and glory with varying cloud droplet sizes. For the investigation, 125 3D cloud scenes are constructed based on observational data and theoretical assumptions. Using a Monte Carlo model, the TOA broadband SW radiances and fluxes of the semi-synthetic cloud scenes are simulated for different scenarios with varying viewing angles ( θv) along the principal plane and solar angles ( θs). Based on the resulting 20 000 scenarios, the sensitivity and accuracy of the two SW radiance-to-irradiance conversion approaches to cloud droplet size, spatial distribution of liquid water path, and mean optical thickness are quantified. The study emphasizes that explicitly including the liquid droplet effective radius in ADM generation can improve the accuracy of shortwave flux estimates. Particularly for viewing geometries that exhibit single scattering phenomena, such as cloud glory and cloud bow, flux estimates can benefit from microphysically aware ADMs. For the analyzed scenarios, we found that the errors of instantaneous TOA SW flux estimates could be reduced by up to 25 W m −2 . For scenes with very large or small droplets, the median error was reduced by up to 7 W m −2 .
