A Technique for Habit Classification of Cloud Particles

A. Korolev Meteorological Service of Canada, Downsview, Ontario, Canada

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B. Sussman Meteorological Service of Canada, Downsview, Ontario, Canada

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Abstract

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 four habit categories: “spheres,” “irregulars,” “needles,” and “dendrites.” The present algorithm derives the particle habits from an analysis of dimensionless 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 linear 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, images that are completely or partially contained within the sample area of the probe. The ability to process “partial” images improves the statistics of the recognition by approximately 50% when compared with retrievals that use “complete” images only. The details of this algorithm are discussed in this study.

Corresponding author address: Dr. Alexei V. Korolev, Cloud Physics Research Division, Meteorological Service of Canada, 4905 Dufferin Street, Downsview, ON M3H 5T4, Canada.

Email: alexei.korolev@ec.gc.ca

Abstract

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 four habit categories: “spheres,” “irregulars,” “needles,” and “dendrites.” The present algorithm derives the particle habits from an analysis of dimensionless 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 linear 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, images that are completely or partially contained within the sample area of the probe. The ability to process “partial” images improves the statistics of the recognition by approximately 50% when compared with retrievals that use “complete” images only. The details of this algorithm are discussed in this study.

Corresponding author address: Dr. Alexei V. Korolev, Cloud Physics Research Division, Meteorological Service of Canada, 4905 Dufferin Street, Downsview, ON M3H 5T4, Canada.

Email: alexei.korolev@ec.gc.ca

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