改进的Holroyd云粒子形状识别方法及其应用

An improved Holroyd cloud particle habit identification method and its application

中文关键词:  云粒子形状  形状识别  降水性层状云  云降水微物理

英文关键词:Cloud particle habit  Habit identification  Precipitable stratiform cloud  Cloud and precipitation microphysics

中文摘要:

      云降水粒子形状是影响云微物理过程的重要因素，准确的云粒子形状信息是诸多云微物理参量计算的前提。为获取机载云粒子成像仪（CIP）所测云粒子的形状信息，文中提出了一种改进的Holroyd云粒子形状识别方法，即先对云粒子形状进行预分类，然后针对预分类后的完整粒子和可识别的部分状粒子，分别选出合适的参数及其阈值再进行具体的分类，最终可将云粒子分为微小状、线形状、聚合状、霰、球形、板状、不规则和枝状。利用实测数据对原始的Holroyd方法和改进的Holroyd方法进行识别效果对比验证。结果表明改进的Holroyd方法在云粒子形状识别的准确度方面比原Holroyd方法有较大的提高。将所提方法应用于太原地区一次降水性层状云的云微物理飞机观测资料以分析不同的降水阶段云中冰晶粒子的形状分布、增长机制、冰晶粒子数浓度以及冰水含量的垂直分布特征，所获取的云中冰晶粒子属性表明新提出方法有助于云微物理分析。

英文摘要:

      The habit of cloud and precipitation particles is an important aspect of cloud microphysical process. And accurate information of particle shape is the premise for the calculation of many cloud microphysical parameters. At present, the airborne cloud particle imaging probe (CIP) based on the photodiode array is one of the most widely used instruments for cloud and precipitation particle shape measurement both domestically and abroad. However, the application of the information of particle shapes measured by this probe requires additional automatic particle habit identification method. In the research history of automatic recognition algorithm for cloud particle shapes, Holroyd proposed a very representative method in 1987. However, the proposed method has a serious defect in the particle habit classification, i.e., it uses the same set of threshold values to classify particle habits without considering the integrity of the particle shapes, which limits its identification accuracy. To overcome the shortcoming of the Holroyd method, an improved Holroyd cloud particle habit identification method is proposed in the present study, which uses different sets of threshold values to identify the particle shape according to whether it is a complete particle or a partial particle. Using the probe's image data from a field campaign, the accuracies of these two methods are verified. It is found that the improved algorithm can greatly improve the accuracy of the particle habit classification and its average accuracy rate can reach 80%. The improved method is then applied to airborne observation data of stratiform clouds in Taiyuan area to analyze cloud particle habit occurrence frequency, cloud particle growth mechanism, vertical distributions of ice particle number concentration and ice water content during different precipitation phases. The properties of ice crystals acquired in the stratiform clouds suggest the cloud habit classification method proposed in the present study is helpful for cloud microphysics analysis.