论文

毫米波雷达反演层状云液态水路径研究

  • 姚志刚 ,
  • 杨超 ,
  • 赵增亮 ,
  • 王磊
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  • 北京应用气象研究所, 北京 100029

收稿日期: 2016-07-15

  网络出版日期: 2018-02-28

基金资助

国家自然科学基金项目(41575031, 41375024);中国博士后基金项目(2015M580124);部级重点课题(QX2015040311A12005);国家重大专项课题(GFZX04021201)

Study of the Stratiform Cloud Liquid Water Path Retrieval from the Millimeter Wave Radar Data

  • YAO Zhigang ,
  • YANG Chao ,
  • ZHAO Zengliang ,
  • WANG Lei
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  • Beijing Institute of Applied Meteorology, Beijing 100029, China

Received date: 2016-07-15

  Online published: 2018-02-28

摘要

为了从毫米波雷达观测数据准确反演云中液态水路径,利用中国区域飞机探测资料得到的云粒子谱参数,基于2008年寿县ARM-AMF地基毫米波云雷达观测,针对层状云采用不同的云粒子谱参数假定,由物理迭代法和经验关系法反演云中液态水路径,并与地基微波辐射计的云水产品进行对比,开展了基于地基毫米波雷达的层状云液态水路径反演算法的对比分析。结果表明,反演结果与谱参数的选取以及云的特征密切相关,但物理迭代法总体上优于传统的经验关系法且前者对谱参数假定的依赖性相对较弱;基于中国区域的飞机探测资料得到的谱参数能够得到更优的反演结果;云中可能存在的大粒子是云雷达液态水路径反演高估的可能原因之一。最后,提出了基于云特征的谱参数选择方案,显著改进了云中液态水路径的反演结果。

本文引用格式

姚志刚 , 杨超 , 赵增亮 , 王磊 . 毫米波雷达反演层状云液态水路径研究[J]. 高原气象, 2018 , 37(1) : 223 -233 . DOI: 10.7522/j.issn.1000-0534.2016.00127

Abstract

Clouds play an important role in the weather prediction and global climate changes. Cloud liquid water path (LWP) is one of the important cloud microphysical parameters. LWP can be extracted from the millimeter wave radar. To investigate the dependence of the LWP retrieval on retrieval methods and cloud particle spectrum assumptions and improve the LWP retrieval, based on the cloud particle spectrum parameters from the aircraft detection data over China, the retrieval experiments are carried out by using the ground-based millimeter wave radar data in Shouxian from AMF-ARM Mobile Facility in 2008. Based on different cloud particle spectrum parameter assumptions, a physical iteration method and a statistical regression method are respectively used to retrieve the stratiform cloud LWP. And the results are evaluated with the products from the collocated microwave radiometer products. It is shown that although the results are strongly dependent on the spectrum assumption of the cloud particles, the physical iteration method less depends on the cloud spectrum assumption than the statistical regression method. Overall, the retrieval results from the physical iteration method agree better with the radiometer based LWP than those from the statistical regression method. Particularly, the spectrum of the clouds from the aircraft based measurements over China favor the LWP retrievals. The further analysis indicates that the ignorance of the large size particles could lead to the overestimation of the LWP retrievals, which increases with the increase of the radar reflectivity. Finally, a spectrum parameter selection scheme based on different cloud characteristics is proposed. The results show that the new scheme can significantly improve the agreement of the LWP retrievals with the microwave radiometer results.

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