黄敏松. 2020. 改进的Holroyd云粒子形状识别方法及其应用[J]. 气象学报, (0):-, doi:10.11676/qxxb2020.018
改进的Holroyd云粒子形状识别方法及其应用
Improved Holroyd cloud particle habit identification method and its application Acta Meteorologica Sinica
投稿时间:2019-09-06  修订日期:2019-11-28
DOI:10.11676/qxxb2020.018
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, Precipitus stratiform cloud, Cloud and precipitation microphysics
基金项目:国家自然科学基金项目41575131、41775166和41705142、气象灾害教育部重点实验室(南京信息工程大学)开放课题KLME201907、南京信息工程大学江苏省气象探测与信息处理重点实验室与江苏省气象传感网技术工程中心开放课题 KDXS1803.
作者单位E-mail
黄敏松 中国科学院大气物理研究所 mission@mail.iap.ac.cn 
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中文摘要:
      云降水粒子形状是云微物理过程的重要方面,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测量仪(CIP)所测云粒子的形状信息,本文提出了一种改进的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状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,最终可将云粒子形状分为微小状、线形状、聚合状、霰、球形状、板状、不规则状和枝状。利用实测数据对原始的Holroyd方法和改进的Holroyd方法进行识别效果上的对比验证。经对比发现,改进的Holroyd方法在云粒子形状识别的准确性方面比原始的Holroyd方法有了较大的提高。将所提方法应用于山西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本文所提方法非常有助于云微物理分析。
英文摘要:
      The habit of cloud and precipitation particle is an important aspect of cloud microphysical process. And the accurate information of particle shape is the premise for many cloud microphysical parameters calculation. At present, the airborne cloud particle imaging probe (CIP) based on the photodiode array is one of the most widely used instruments for the cloud and precipitation particle''s shape measurement at home and abroad. However, using the information of the particle shape measured by this probe requires additional automatic particle habit identification method. In the research history of cloud particle shape automatic recognition algorithm, Holroyd had ever proposed a very representative method in 1987. However, the proposed method exists a serious defect in the particle habit classification. That it used the same set of threshold value to classify the particle''s habit without considering the integrity of the particle shape, which limits its identification accuracy. In view of the shortcoming of the proposed method, an improved Holroyd cloud particle habit identification method is presented, which uses different sets of threshold value to identify the particle''s shape according to whether it is a complete particle or a partial particle. Using the probe''s image data from the field campaign the identified accuracy of these two methods was 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 was then applied in an airborne observation data from a precipitation stratiform cloud in Shanxi Taiyuan area to analyze the cloud particle habit occurrence frequency, cloud particle growth mechanism, ice particle number concentration and the ice water content in the vertical distribution in the different precipitation phase. The properties of ice crystal acquired in the precipitation stratiform cloud suggests the proficiency of the presented cloud habit classification method.
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