Considering the Impacts of Horizontal Heterogeneity in Satellite-Based Large-Scale Statistics of Cloud Optical Properties

This presentation explores a new approach to improve satellite measurements of cloud optical thickness and droplet size by considering the radiative impacts of horizontal heterogeneity in boundary layer cumulus clouds. In contrast to the usual bottom-up approach that retrieves cloud properties for individual pixels and compiles large-scale statistics subsequently, the proposed top-down approach first determines the effect of 3D heterogeneity on large-scale cloud statistics and then distributes the overall effects to individual pixels. The potential of this approach was explored by applying a regression-based scheme to a simulated dataset containing over 3000 scenes generated through large eddy simulations. The results show that the new approach can greatly reduce the errors in widely used bispectral retrievals that assume horizontal homogeneity. Errors in large scale mean values and cloud variability were typically reduced by factors of 2 to 4 for 1 km resolution retrievals—and the reductions remained significant even for 4 km resolution. The calculations also revealed that heterogeneity-caused droplet size retrieval biases are often opposite over vegetation and over ocean. Ultimately, the proposed approach shows potential to improve the accuracy of both old and new satellite datasets.