A technique for habit classification of cloud particles

A new algorithm was developed to classify populations of binary (black and white) images of cloud particles collected with Particle Measuring Systems (PMS) Optical Array Probes (OAPA). The algorithm classifies images into fou r habit categories: "spheres," "irregulars," "needles," and "dendrites." Th e present algorithm derives the particle habits from an analysis of dimensi onless ratios of simple geometrical measures such as the x and y dimensions . perimeter, and image area. For an ensemble of images containing a mixture of different habits, the distribution of a particular ratio will be a line ar superposition of basis distributions of ratios of the individual habits. The fraction of each habit in the ensemble is found by solving the inverse problem. One of the advantages of the suggested scheme is that it provides recognition analysis of both "complete" and "partial" images, that is, ima ges that are completely or partially contained within the sample area of th e probe. The ability to process "partial" images improves the statistics of the recognition by approximately 50% when compared with retrievals that us e "complete" images only. The details of this algorithm are discussed in th is study.