A novel approach for introducing cloud spatial structure into cloud radiative transfer parameterizations

Subgrid-scale variability is one of the main reasons why parameterizations are needed in large-scale models. Although some parameterizations started to address the issue of subgrid variability by introducing a subgrid probability distribution function for relevant quantities, the spatial structure has been typically ignored and thus the subgrid-scale interactions cannot be accounted for physically. Here we present a new statistical-physics-like approach whereby the spatial autocorrelation function can be used to physically capture the net effects of subgrid cloud interaction with radiation. The new approach is able to faithfully reproduce the Monte Carlo 3D simulation results with several orders less computational cost, allowing for more realistic representation of cloud radiation interactions in large-scale models

Autocorrelation · Cloud computing · Meteorology · Monte Carlo method · Physics · Radiative transfer · Representation (politics · Scale (ratio · Spatial analysis · Statistical physics · Statistics · Atmospheric aerosols and clouds · Atmospheric chemistry and aerosols · Computer Science · Mathematics · Urban Heat Island Mitigation