3D Radiative Transfer through Cumulus Clouds, Comparing Modeling to Observations

Clouds modify surface solar irradiance fields by scattering and absorbing radiation. The spatial distribution of surface irradiance drives horizontal heterogeneity in the surface heat and moisture fluxes, which may affect the further development of boundary layer clouds. Additionally, spatial variability of solar irradiance affects the electricity production by solar energy. However, accurately capturing the spatial distribution of surface solar irradiance in cloud-resolving models is not straightforward. Radiative transfer in the atmosphere is generally solved using 1D two-stream approximations, which are computationally efficient but neglect the horizontal energy transport by radiation. Alternatively, realistic surface solar irradiance fields can be computed using Monte Carlo ray tracing methods, which are highly accurate but computationally expensive and therefore often only used for a limited number of cloud fields. Moreover, ray traced irradiance fields are only as accurate as the input cloud fields and therefore still require validation by observations, which are generally only available as single point measurements at a relatively coarse temporal resolution. In this study, we make use of a dense spatial grid of radiation measurements that was set-up during the FESSTVaL measurement campaign in Germany, and aim to provide a unique validation of the spatial variability in surface irradiance fields produced by a suite of radiative transfer approximations. First, we run large-eddy simulations of three different days during the campaign, two days with shallow and one day with deep convection, to generate high-resolution cloud fields that can be validated using LIDAR observations. Radiative fluxes are then computed for each cloud field using the two-stream approximation, Monte Carlo ray tracing, and a number of approximations for three dimensional radiative transfer such as tilted columns and the TenStream solver. The comparison of these various radiative transfer techniques against observations may contribute to a better understanding of the spatial variability of surface solar irradiance under cumulus clouds. Moreover, this can give a better insight into which radiative transfer approximations can capture the surface irradiance variability to sufficient accuracy for solar energy applications.