杨晓,黄兴友,杨军,李培仁,李盈盈,杨敏,刘燕斐,张帅,闫文辉. 2019. 毫米波雷达云回波的自动分类技术研究[J]. 气象学报, 77(3):541-551, doi:10.11676/qxxb2019.046
毫米波雷达云回波的自动分类技术研究
A study on auto-classification of cloud types based on millimeter-wavelength cloud radar observations
投稿时间:2018-08-21  修订日期:2019-02-25
DOI:10.11676/qxxb2019.046
中文关键词:  毫米波雷达  多参数阈值法  云观测  云分类  自动化
英文关键词:Millimeter-wavelength radar  Multi-parameter threshold method  Cloud observation  Cloud classification  Automation
基金项目:国家自然科学基金项目(41475034、41475035)。
作者单位E-mail
杨晓 南京信息工程大学, 南京, 210044
山西省人工降雨防雹办公室, 太原, 030032 
 
黄兴友 南京信息工程大学, 南京, 210044
南京信息工程大学气象灾害预报预警与评估协同创新中心, 南京, 210044 
hxyradar@126.com 
杨军 南京信息工程大学, 南京, 210044  
李培仁 山西省人工降雨防雹办公室, 太原, 030032  
李盈盈 南京信息工程大学, 南京, 210044  
杨敏 南京信息工程大学, 南京, 210044  
刘燕斐 南京信息工程大学, 南京, 210044  
张帅 南京信息工程大学, 南京, 210044  
闫文辉 南京信息工程大学, 南京, 210044  
摘要点击次数: 54
全文下载次数: 110
中文摘要:
      毫米波雷达在云探测方面比厘米波天气雷达和激光雷达具有显著优势,可获得更多的云粒子信息,是研究云特性的主要遥感探测设备。为了开展对毫米波雷达探测的云回波进行自动分类的研究,利用161次云回波的个例数据,统计得到了卷云、高层云、高积云、层云、层积云和积云6类云型的特征量和其他参量的数值范围,利用分级的多参数阈值判别方法,达到了自动分类的目标,通过与人工分类的初步验证,两种分类结果的一致性达到84%,其中,层云和积云的识别一致较低的原因在于样本数据有限,仅有6次层云和8次积云的个例样本数据。通过更多样本的处理,提取的特征参量更可靠,自动分类的准确率会得到提高,以便将基于毫米波雷达的云分类技术应用于将来的云观测自动化业务。
英文摘要:
      The millimeter-wavelength cloud radar has obvious advantages over weather radar and lidar because it can provide more information on cloud particle. It becomes an effective instrument in the detection and study of cloud characteristics. This work is focused on automatic classification of cloud echoes detected by the millimeter-wavelength cloud radar. Based on 161 samples of cloud echoes, the value ranges of characteristic quantities and other parameters are obtained for six types of cloud, including cirrus, altostratus, altocumulus, stratus, stratocumulus and cumulus. Automatic classification of clouds has been realized by using the multi-parameters threshold discrimination method with these value ranges in a hierarchical order. The automatic classification results are evaluated by comparing with that of manual classification, which shows a 84% consistency between the two methods. The automatic classification method cannot well identify stratus and cumulus clouds due to the limited number of samples (6 stratus samples and 8 cumulus samples). With more samples, more reliable information of the characteristic quantities for various types of clouds will be obtained, and the accuracy of automatic classification definitely will be improved. The cloud classification technique developed in this work based on millimeter-wavelength cloud radar observations is highly expected to promote the operation of automatic cloud observations in the near future.
HTML   查看全文   查看/发表评论  下载PDF阅读器
分享按钮