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基于探空云识别方法的云垂直结构分布特征

李琦 蔡淼 周毓荃 唐雅慧 欧建军

李琦, 蔡淼, 周毓荃, 等. 2021. 基于探空云识别方法的云垂直结构分布特征[J]. 大气科学, 45(6): 1161−1172 doi: 10.3878/j.issn.1006-9895.2105.19246
引用本文: 李琦, 蔡淼, 周毓荃, 等. 2021. 基于探空云识别方法的云垂直结构分布特征[J]. 大气科学, 45(6): 1161−1172 doi: 10.3878/j.issn.1006-9895.2105.19246
LI Qi, CAI Miao, ZHOU Yuquan, et al. 2021. Characteristics of Cloud Vertical Distribution Based on Cloud Identification by Radiosonde [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 45(6): 1161−1172 doi: 10.3878/j.issn.1006-9895.2105.19246
Citation: LI Qi, CAI Miao, ZHOU Yuquan, et al. 2021. Characteristics of Cloud Vertical Distribution Based on Cloud Identification by Radiosonde [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 45(6): 1161−1172 doi: 10.3878/j.issn.1006-9895.2105.19246

基于探空云识别方法的云垂直结构分布特征

doi: 10.3878/j.issn.1006-9895.2105.19246
基金项目: 国家重点研发计划项目2016YFA0601701
详细信息
    作者简介:

    李琦,男,1990年出生,硕士研究生,主要从事云降水物理与人工影响天气的研究。E-mail: airforce1_q@163.com

    通讯作者:

    周毓荃,E-mail: zhouyq05@163.com

  • 中图分类号: P41, P426.5

Characteristics of Cloud Vertical Distribution Based on Cloud Identification by Radiosonde

Funds: National Key Research and Development Program of China (Grant 2016YFA0601701)
  • 摘要: 云的垂直结构特征作为云重要的宏观特征之一,直接决定了云的类型,进而通过发射和吸收辐射的方式影响着地气系统的能量收支平衡,因此对云垂直结构特征的研究一直都是云物理研究的一个重要方向。作为观测云垂直结构特征的一种方式,探空气球通过获取沿路径方向高分辨率的廓线信息,采用一定反演方法从而能够较为准确的识别云的垂直结构。本文即利用我国业务布网探空站的观测资料,采用相对湿度阈值法识别云垂直结构,并同激光云高仪、“风云四号”静止卫星和毫米波云雷达对识别的云结构特征量进行了一致性检验。在此基础上,统计分析了2015~2017年单层、两层和三层云的垂直结构分布特征、日变化和季节变化特征以及全国区域分布特征,结果表明:(1)整体分布上,单层云在垂直方向上出现的高度范围介于多层云的高度范围内,并且随着云层数的增加,云在垂直方向上更为伸展,即高层云越高,低层云越低;(2)在日变化中,中午单层和多层云中最低层云的云底高度均高于早晨,而夜间单层和多层云中最高层云的云顶高度则高于早晨和中午,同时中间层云厚的变化要小于最上层和最下层云厚的变化;(3)在季节变化中,夏季云量较其他季节更多,云体发展也更为深厚,表明温暖的大气条件更有利于云的形成和发展;(4)我国云垂直结构分布特征具有明显的纬向变化趋势,从以青藏高原为中心的西南地区的云底较高云体较薄的云,逐步过渡到以东南沿海地区为中心的云底较低云体较为深厚的云,表明不同地形和气候带的差异与不同云类型的分布直接相关。
  • 图  1  全国L波段业务探空站布网情况

    Figure  1.  Distribution of L-band operational radiosonde sites in China

    图  2  激光云高仪同探空识别云底高度(HCB,单位:km)散点图(N为样本数,R为相关系数)

    Figure  2.  Comparison of radiosonde and ceilometer cloud base heights (HCB, units: km; N: samples, R: correlation)

    图  3  激光云高仪同探空识别云底高度之差(ΔH)频数分布图

    Figure  3.  Frequency distribution of cloud base height differences [ΔHHCB(ceilometer)− HCB(radiosonde)]

    图  4  FY-4A卫星同探空识别云顶高度(HCT,单位:km)散点图(N为样本数,R为相关系数)

    Figure  4.  Comparison of FY-4A satellite and ceilometer cloud top heights (HCT, units: km; N: samples, R: correlation)

    图  5  不同类型云FY-4A卫星同探空识别云顶高度(HCT,单位:km)比较(N为样本数)

    Figure  5.  Comparison of FY-4A and radiosonde cloud top heights in different cloud types (HCT, units: km; N: samples)

    图  6  激光云高仪同探空识别云底高度之差绝对值随高度变化结果

    Figure  6.  Absolute difference in cloud base height between ceilometer and radiosonde with heights

    图  7  2015~2017年单层、两层和三层云垂直方向的平均分布特征(HtHb 是指平均云顶高度和云底高度)

    Figure  7.  Mean locations of one-, two-, and three-layer clouds from 2015 to 2017 (Ht and Hb represent the mean cloud top height and cloud base height)

    图  8  2015~2017年单层、两层和三层云垂直结构的日变化特征

    Figure  8.  Diurnal variations of one-, two-, and three-layer clouds from 2015 to 2017

    图  9  2015~2017年单层、两层和三层云最高层云顶高度和最低层云底高度的季节变化特征

    Figure  9.  Seasonal variations of the uppermost layer of the cloud top height and the lowest layer of the cloud base height in one-, two-, and three-layer clouds from 2015 to 2017

    图  10  2015~2017年单层、两层和三层云最低层云底高度的区域分布特征

    Figure  10.  Spatial distribution of the lowest layer of the cloud base height in one-, two-, and three-layer clouds in China from 2015 to 2017

    图  11  单层、两层和三层云累积云体厚度的区域分布特征

    Figure  11.  Spatial distribution of the integral cloud thickness of one-, two-, and three-layer clouds in China

    表  1  云底高度检验的样本分类标准及其统计结果

    Table  1.   Sample classification standards of cloud base heights and statistical results

    简称定义样本数占总样本比例
    NORM探空和激光云高仪均识别有云27133.9%
    NONE探空和激光云高仪均识别无云30037.5%
    DIFF探空和激光云高仪中一方识别有云,另一方识别无云18022.5%
    RAIN探空识别云边界为探测起始高度,且地面统计有降水496.1%
    下载: 导出CSV

    表  2  云顶高度检验的样本分类标准和统计结果

    Table  2.   Sample classification standards of cloud top heights and statistical results

    简称定义样本数占总样本比率
    NORM探空和FY-4A卫星云顶高度均识别有云51742.4%
    NONE探空和FY-4A卫星云顶高度中一类识别有云,另一类识别无云43435.6%
    DIFF探空和FY-4A卫星云顶高度均识别无云16713.7%
    RAIN探空识别云边界为探测起始高度,且地面统计有降水1018.3%
    下载: 导出CSV

    表  3  Ka波段云雷达识别不同云层数时对应的探空识别结果

    Table  3.   Corresponding results identified by the radiosonde when different cloud layers were retrieved by the Ka-band cloud radar

    云层数云雷达反演样本数探空识别样本数探空识别正确率
    无云 716085%
    单层云1076662%
    两层云654062%
    三层云221882%
    下载: 导出CSV

    表  4  北京南郊站2015~2017年探空识别不同层云样本分布

    Table  4.   Statistics of different cloud layers identified by radiosonde at the Beijing Meteorological Observatory (2015–2017)

    云层数无云单层云两层云三层云四层云五层云六层及以上云
    样本数10324202721581296776
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-12-09
  • 录用日期:  2021-07-13
  • 网络出版日期:  2021-06-03
  • 刊出日期:  2021-11-25

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