The Impact of Radiative Transfer at Reduced Spectral Resolution in Large-Eddy Simulations of Convective Clouds

Many radiative transfer schemes approximate the spectral integration over ∼10⁵ to ∼10⁶ wavelengths with correlated k-distributions methods that typically require only 10¹–10² spectral integration points (g-points). The exact number of g-points is then chosen as an optimal balance between computational costs and accuracy, normally assessed in terms of a number of radiative quantities. How this radiative accuracy propagates to simulation accuracy, however, is not straightforward. In this study, we therefore explore the sensitivity of cloud properties in large-eddy simulations (LES) to the accuracy of radiative fluxes and heating rates. We first generate smaller sets of g-points from existing k-distributions by repeatedly combining adjacent g-points while maintaining the highest possible accuracy on a chosen set of radiative metrics. Next, we perform three sets of LES with varying cloud—radiation coupling pathways, and therefore different requirements for the accuracy of the radiative transfer computations, to investigate how these smaller and thus less accurate k-distributions affect simulation characteristics. The decrease in radiative accuracy with 3–4 times smaller k-distributions results in biases in cloud properties that are relative small compared to their temporal fluctuations. These results show potential for speeding up radiative transfer computations in cloud-resolving models by reducing the resolved spectral detail. However, more statistically converged simulations and a wider set of case studies is required to fully assess the robustness of our results.