On the lumpiness and bumpiness of clouds: Impacts on remote sensing and large scale models

Abstract

The wonderful beauty of clouds comes in large part from the nearly endless variety of shapes and textures that clouds exhibit. When clouds are represented mathematically, however, we almost always replace the variability with its average. This talk addresses the costs of this simplification in two applications. First, I'll explore the shape of cloud tops, show how cloud bumpiness affects the distribution of sunlight reflected back to space, and note some serious implications for the ways cloud properties are measured from satellites. Then I'll describe the internal variability of clouds at spatial scales of tens to hundreds of kilometers, and point out the connection between the cloud lumpiness and the computation of cloud process rates in large scale climate and weather prediction models. In both cases a blending of observations and a general representation of cloud structure allow us to make more robust and accurate calculations.