Some Techniques and Uses of 2D-C Habit Classification Software for Snow Particles

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  • 1 Bureau of Reclamation, U.S. Dept. of the Interior, Montrose, CO 81401
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Abstract

A technique has been designed that uses observable properties of images from a 2D-C optical array probe (size, linearity, area, perimeter, and image density) to classify unsymmetrical ice particles into nine habit classes. Concentrations are calculated by requiring that the center of each accepted particle appear to be within the field of view of the probe. Once the size and habit are estimated, a generic mass and terminal velocity can be assigned to each particle to calculate its contribution to ice water content and to precipitation rate. Examples are given to indicate the value of a habit classifier in analyzing the structure of storms, showers, orographic clouds, and seeded clouds. Though the techniques work well for most natural snowfalls, some examples of imperfections are given to remind the analyst to look at the images and to understand how the classifer will treat them.

Abstract

A technique has been designed that uses observable properties of images from a 2D-C optical array probe (size, linearity, area, perimeter, and image density) to classify unsymmetrical ice particles into nine habit classes. Concentrations are calculated by requiring that the center of each accepted particle appear to be within the field of view of the probe. Once the size and habit are estimated, a generic mass and terminal velocity can be assigned to each particle to calculate its contribution to ice water content and to precipitation rate. Examples are given to indicate the value of a habit classifier in analyzing the structure of storms, showers, orographic clouds, and seeded clouds. Though the techniques work well for most natural snowfalls, some examples of imperfections are given to remind the analyst to look at the images and to understand how the classifer will treat them.

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