Data and image fusion for geometrical cloud characterization

Clouds have a strong influence on the Earth`s climate and therefore on climate change. An important step in improving the accuracy of models that predict global climate change, general circulation models, is improving the parameterization of clouds and cloud-radiation interactions. Improvements in the next generation models will likely include the effect of cloud geometry on the cloud-radiation parameterizations. We have developed and report here methods for characterizing the geometrical features and three-dimensional properties of clouds that could be of significant value in developing these new parameterizations. We developed and report here a means of generating and imaging synthetic clouds which we used to test our characterization algorithms; a method for using Taylor`s hypotheses to infer spatial averages from temporal averages of cloud properties; a computer method for automatically classifying cloud types in an image; and a method for producing numerical three-dimensional renderings of cloud fields based on the fusion of ground-based and satellite images together with meteorological data