Accounting for subgrid‐scale cloud variability in a multi‐layer 1d solar radiative transfer algorithm

A multi‐layer, 1D solar radiative transfer algorithm that accounts for subgrid‐scale cloud variability is presented. This algorithm is efficient and suitable for use in large‐scale models such as global climate and weather prediction models. While it is built on the same principles as standard multi‐layer 1D codes, there are two major differences. First, it is assumed that for all cloudy layers all the time, frequency distributions of optical depth τ are described by gamma probability density functions pr(τ) and characterized by mean optical depth τ and a variance‐related parameter v. Albedos and transmittances for individual layers are estimated by integrals over all τ of the plane‐parallel, homogeneous two‐stream approximation equations weighted by pr(τ). Thus, the model is referred to as the gamma‐weighted two‐stream approximation. Second, in an attempt to counteract the use of horizontally homogeneous fluxes, a method was devised that often reduces layer values of τ.