李旭岗,苏婧,王晨,胡晓宇,汪美华,葛觐铭. 2020. SACOL站冰云粒子下降末速度的反演及其时空分布特征的研究[J]. 气象学报, (0):-, doi:10.11676/qxxb2020.054
SACOL站冰云粒子下降末速度的反演及其时空分布特征的研究
Retrival of the terminal fall velocity of the ice cloud particles and its spatial-temporal distribution at the sacol
投稿时间:2020-01-17  修订日期:2020-03-26
DOI:10.11676/qxxb2020.054
中文关键词:  云雷达(KAZR),雷达反射率,下降末速度,聚类分析,微物理过程
英文关键词:Ka-Band Zenith Radar (KAZR)  Radar reflectivity  Terminal fall velocity  Cluster analysis  Microphysical processes
基金项目:国家自然科学基金
作者单位E-mail
李旭岗 兰州大学半干旱气候变化教育部重点实验室 lixg17@lzu.edu.cn 
苏婧 兰州大学半干旱气候变化教育部重点实验室 jsu@lzu.edu.cn 
王晨 兰州大学半干旱气候变化教育部重点实验室 wangch2018@lzu.edu.cn 
胡晓宇 兰州大学半干旱气候变化教育部重点实验室 huxy2012@lzu.edu.cn 
汪美华 兰州大学半干旱气候变化教育部重点实验室 wangmh16@lzu.edu.cn 
葛觐铭 兰州大学半干旱气候变化教育部重点实验室 gejm@lzu.edu.cn 
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中文摘要:
      冰云是影响气候变化最为重要的因子之一,其生命周期的变化在很大程度上决定了冰云的气候辐射效应。冰云粒子下降末速度是影响冰云生命周期的关键参数。本文利用兰州大学半干旱气候与环境监测站Ka波段毫米波云雷达2013年8月到2015年7月连续观测数据,反演了冰云粒子的下降末速度,并根据雷达反射率因子Z与的关系计算了拟合因子a、b的值;在此基础上应用聚类分析方法,对比分析了四种不同特性冰云Z、和拟合因子a、b的时空分布特征,进而尝试通过参数垂直分布特征识别研究云中不同位置上云微物理过程的变化。结果表明:冰云粒子下降末速度的分布与雷达反射率因子有着很好的对应,最大频率都出现在距离地面约7km附近,且具有显著的季节变化,粒子下降末速度在暖季较冷季可增大25%,峰值出现在6月和9月;云层较厚且持续时间长的第一、三类冰云,其雷达反射率因子、粒子下降末速度及拟合系数a和b的平均值都显著大于云层较薄且持续时间短的第二、四类云。垂直方向上,Z和拟合因子b从云顶到云底随着高度的降低呈现先增大后减小的趋势,体现了云粒子在云顶区域成核和水汽沉积,随着粒子在下落过程中碰并增长,云滴粒子逐渐增大,水汽的沉积和粒子的聚合起主要作用,最后在云底部分,云粒子蒸发升华减小消亡的过程。由此表明中纬度干旱半干旱地区冰云是从云顶到云底自上而下的形成过程。
英文摘要:
      Ice cloud is one of the most important factors in the climate system, and the characterization of its life cycle in climate models has always been a prominent issue. The terminal fall velocity of ice cloud particles is the key parameter affecting the life cycle of ice cloud. Ka-Band Zenith Radar (KAZR), which has been deployed at the Semi-Arid Climate and Environment Observatory of Lanzhou University (SACOL) since July 2013, has been continuously operated for six years. By using continuous observations of the Ka-band millimeter-wave cloud radar from August 2013 to July 2015, we retrieved the particle terminal fall velocity and calculated the values of the coefficients a and b based on the relationship between the radar reflectance factor Z and . Then, we further divide the ice clouds into four categories by clustering analysis, and discuss the spatiotemporal distribution of the reflectivity, the terminal fall velocity, coefficients a and b of the four different types of ice cloud. Furthermore, we try to study the change of microphysical processes in different positions in the cloud through the parameter vertical distribution feature recognition. The results show that the distribution of corresponds well to Z, and the maximum frequency appears about 7km near the ground. In addition, they all show significant seasonal changes and the terminal fall velocity can be increased by 25% in the warm season compared to the cold season. The average values of radar reflectivity factor, particle terminal fall velocity and fitting coefficient a and b of the first and third types of ice clouds with thicker clouds and longer duration are significantly larger than those of the second and fourth types of clouds with thinner clouds and shorter duration. The parameters of all types of ice clouds are relatively consistent in seasonal changes, all peaking in June and September. In the vertical direction, Z, and fitting coefficient b from the top of the cloud to the bottom of the cloud show a tendency to increase first and then decrease with the decrease of the height of the cloud, which reflects the microphysical processes at different locations in the cloud. In the top part of the cloud, thenucleation of particles and the deposition of water vapor in the cloud are dominant. With the decrease of cloud height, the deposition of water vapor and the aggregation of particles play a major role, the cloud particles gradually increase. In the cloud layer close to the cloud bottom, the cloud particles evaporate and sublimate, and the particle size decreases. This indicates that the formation of ice clouds in arid and semi-arid areas in the middle latitude of China is a top-down process from the top to the bottom of the cloud. While the distribution characteristics of fitting coefficient b in the vertical direction also indicate that this parameter can be tried for the identification of different physical processes within the cloud.
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