The three‐dimensional structure of cumulus clouds over the ocean: 1. Structural analysis

Abstract

Thermal channel (channel 6, 10.4–12.5 μm) images of five Landsat thematic mapper cumulus scenes over the ocean are examined. These images are thresholded using the standard International Satellite Cloud Climatology Project thermal threshold algorithm. The individual clouds in the cloud fields are segmented to obtain their structural statistics which include size distribution, orientation angle, horizontal aspect ratio, and perimeter‐to‐area (PtA) relationship. It is found that the cloud size distributions exhibit a double power law with the smaller clouds having a smaller absolute exponent. The cloud orientation angles, horizontal aspect ratios, and PtA exponents are found in good agreement with earlier studies. A technique also is developed to recognize individual cells within a cloud so that statistics of cloud cellular structure can be obtained. Cell structural statistics are computed for each cloud. Further examination reveals that unicellular clouds are generally smaller (≤ 1 km) and have smaller PtA exponents, while multicellular clouds are larger (≥ 1 km) and have larger PtA exponents. Cell structural statistics are similar to those of the smaller clouds. Each cell is approximated as a quadric surface using a linear least squares fit. Most cells are found to have the shape of a hyperboloid of one sheet. However, about 15% of the cells are best modeled by a hyperboloid of two sheets. Contrary to intuition, less than 1% of the clouds are found to be ellipsoidal. The number of cells in a cloud is found to increase slightly faster than linearly with increasing cloud size. The mean nearest neighbor distance between cells in a cloud, however, appears to increase linearly with increasing cloud size and to reach a maximum when the cloud effective diameter is about 10 km; then it decreases with increasing cloud size. Sensitivity studies of threshold and lapse rate show that neither has a significant impact upon the results. A goodness‐of‐fit ratio is used to provide a quantitative measure of the individual cloud results. Significantly improved results are obtained after applying a smoothing operator, suggesting that eliminating subresolution scale variations with higher spatial resolution may yield even better shape analyses.