WorldSciNet

The use of fractal-dimension calculations, for quantitative classification of various objects, is well established in many areas of the physical and life sciences. Such fractal-dimension calculations are useful in that they furnish some measure of geometrical complexity that is not available through "traditional" approaches. In the present context, we focus attention on the application of such calculations to cloud microphysical data. In particular, we consider data from in-situ, two-dimensional particle imagery. Whereas previous efforts have typically characterized the imaged hydrometeoric fields by dimensional and statistical measures, distinguishing between the various hydrometeoric types should also be possible with fractal dimension based analyses. Although typical data sets may include thousands of individual images, each of these single images is typically quite small in size, and has intensity values that span only a few levels. Thus the need for computationally efficient algorithms, that can process these individual images, presents interesting challenges for fractal-dimension calculations. Here we report on our preliminary findings regarding the capacity and information dimension of various synthetic images.