二维粒子形状分类技术在云微物理特征分析中的应用

Application of 2D Habit Classification in Cloud Microphysics Analysis

本文介绍了一种针对飞机粒子探测系统中云二维图像探头开发的二维粒子形状分类技术。该技术利用粒子形状几何参数特征把云粒子分为8种类型,分别为微小状、线形状、聚合状、霰状、球状、六角形状、不规则状和枝状。同时结合冰水质量关系,给出了探头液水含量和冰水含量的计算方法。最后应用该技术对2006年4月6日一次飞机探测获取的数据进行了云微物理结构分析,聚合状、霰状、六角形状、不规则状的总出现频率为78%,其中霰状粒子的出现频率随着温度的降低而增加。非降水云中的液水含量、液滴粒子浓度、冰晶浓度明显小于降水云,非降水云中液水含量的平均值为0.01 g m–3,冰水含量的平均值0.007 g m–3,冰晶粒子浓度的平均值为11.9 L–1。

A method was introduced to classify two-dimensional particle images collected using optical array probes. Cloud particles were grouped into eight types on the basis of particle morphological parameters, including tiny, linear, aggregate, graupel, sphere, hexagonal, irregular, and dendrite. Mass and habit were assigned to each particle according to liquid water and ice water contents. This method was used to analyze cloud microphysical data obtained by an aircraft on April 6, 2006, in Beijing. The occurrence frequency of the aggregate, graupel, and hexagonal, was 78%. The fraction of graupel increased with an increase in particle size at four temperature intervals between -20~C and 0~C. The liquid water content, liquid particle concentration, and ice particle concentration of the nonprecipitating clouds were considerably smaller than the same values of precipitating clouds. In the detected nonprecipitating clouds, the mean values of the liquid water content, ice water content, and ice particle concentration were 0.01 gm-3, 0.007 gm-3 and 11.9 L-1, respectively.