Some Techniques and Uses of 2D-C Habit Classification Software for Snow Particles
Some Techniques and Uses of 2D-C Habit Classification Software for Snow Particles
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雪颗粒2D-C习惯分类软件的一些技术和用途
DOI：
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发表时间：
1987
期刊：
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通讯作者：
E. Holroyd
中科院分区：
文献类型：
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作者：
E. Holroyd
设计了一种技术，利用2D-C光学阵列探头图像的可观察属性（尺寸、线性度、面积、周长和图像密度）将不对称冰粒分为九个习惯类。浓度的计算要求每个被接受的粒子的中心出现在探针的视野范围内。一旦估计了大小和习性，就可以给每个粒子分配一个一般的质量和终端速度，以计算其对冰水含量和降水速率的贡献。举例说明习惯分类器在分析风暴、阵雨、地形云和种子云的结构时的价值。尽管这些技术对大多数自然降雪都很有效，但也给出了一些不完美的例子，以提醒分析人员查看图像，并了解分类器将如何处理它们。
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.