Assessing the impact of 3d cloud structures on broadband fluxes derived from synthetic satellite radiances

The Earth’s energy budget heavily depends on the cloud radiative effect (CRE). A common approach to determine the top-of-atmosphere broadband radiative fluxes in cloudy conditions is their calculation from derived cloud properties, primarily optical thickness (COT) and effective radius (CER), which can be retrieved from passive satellite observations. In many operational retrieval algorithms simplifying 1d assumptions such as the independent pixel approximation are applied. However, accounting for 3d cloud effects is important for correctly determining the CRE and thus the Earth’s energy budget.

In our project we use the 3d Monte Carlo radiative transfer model MYSTIC in order to compare synthetic satellite radiances based on 1d or full 3d calculations. We present results for three case studies with different cloud types for which we derive COT and CER from these synthetic satellite radiances and then derive the corresponding broadband fluxes. Evaluating the derived cloud properties and broadband fluxes allows for estimating for which cloud types and viewing/illumination conditions 3d effects are more relevant and thus pose more problems for satellite-based estimates of the CRE when following the given approach. This will help us in developing strategies to better account for 3d effects and thus to potentially improve the determination of the CRE using satellite data. Our preliminary results indicate that the differences between 1d and 3d radiances and thus cloud properties and broadband fluxes are larger for cumulus clouds than for low, stratiform clouds, i.e., 3d effects are more relevant for these cases.

This work is part of the Research Unit named C3SAR (Cloud 3D Structure and Radiation, www.c3sar.de) funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), in which cloud modelling, radiative transfer as well as ground and satellite observations are complementary components for assessing the role of 3d cloud variability in estimating the Earth’s energy budget.