大气科学  2017, Vol. 41 Issue (6): 1246-1263 PDF

1 成都信息工程大学大气科学学院高原大气与环境四川省重点实验室, 成都 610225
2 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
3 中国科学院大气物理研究所中国科学院云降水物理与强风暴重点实验室, 北京 100029
4 中国科学院大学, 北京 100049

Research on the Evolution Characteristics of Hydrometeors in a Thunderstorm Cell with X-Band Dual-Polarimetric Radar
LI Xiaomin1, ZHOU Yunjun1,2, XIAO Hui3,4, WU Wei1, ZHAI Li1
1 Plateau Atmosphere and Environment Key Laboratory of Sichuan Province, College of Atmospheric Science, Chengdu University of Information Technology, Chengdu 610225
2 Meteorological Disaster Forecasting and Evaluating Collaborative Innovation Center, Nanjing University of Information Science and Technology, Nanjing 210044
3 Key Laboratory of Cloud-Precipitation Physics and Severe Storms, Institute of Atmospheric Physics, Chinese Academy of Sciences(IAP/CAS), Beijing 100029
4 University of Chinese Academy of Sciences, Beijing 100049
Abstract: In order to understand the distribution and evolution of hydrometeors in thunderstorm cells, a fuzzy logic algorithm is applied to analyze the evolution characteristics of hydrometeors in a typical thunderstorm cell that occurred in Beijing by utilizing the dual-polarization radar parameters and environmental temperature. Furthermore, a wavelet de-noising method and a self-consistent method with constraints (SCWC) are combined to preprocess the data. The results are as follows. (1) According to the macroscopic characteristics of the thunderstorm cell, the process is divided into three stages, i.e. the development stage, the mature stage and the dissipation stage. The average heights of the cell are 11, 12 and 10 km, and the reflectivity can reach 40-45 dBZ, greater than 50 dBZ and 40-45dBZ in the three stages, respectively. Moreover, the percentages of graupels are 2%, 12% and 1% in the three stages. (2) The main microphysical processes and evolving characteristics in each stage are as follows. In the development stage, the main microphysical process below the freezing level is warm-cloud process with 5% of drizzle (DR) and 24% of rain (RA). A small amount of liquid hydrometeors rises to the layer above the freezing level, reacting with dry crystal (DC) and generating 1% of graupels, which actually is a weak cold cloud process. During the mature stage, there is an enhanced warm-cloud process with DR decreasing by a percentage of 2 and RA increasing by a percentage of 2. More liquid hydrometeors can rise to the layer above the freezing level and there is an enhanced cold cloud process with 4% of RA and 5% of DC converting to 7% of graupels. At the last stage, the liquid hydrometeors below 0℃ layer cannot rise to the layer above 0℃, which leads to weaker warm and cold cloud processes. DR increases by a percentage of 1 below the freezing level while DC increases by a percentage of 2, and graupels above the freezing level reduces by a percentage of 5. (3) Based on the above results and dynamic characteristic, a microphysical model of the thunderstorm cell's evolution process is established. This study is important for understanding the structure and microphysical processes of thunderstorm cells as well as improving the forecast of thunderstorm weather.
Key words: X-band dual-polarimetric radar      Fuzzy logic algorithm      Hydrometeors      Thunderstorm cell
1 引言

2 数据来源

 图 1 雷达位置示意图（*：雷达位置） Figure 1 Schematic diagram of location of Radar (*)

3 研究方法

 图 2 雷达资料处理流程图 Figure 2 Flowchart of radar data preprocessing
3.1 数据预处理

 图 3 天气雷达数据质量控制效果图：（a）退折叠前；（b）退折叠后；（c）小波去噪；（d）衰减订正前；（e）衰减订正后 Figure 3 The effects of quality control of radar data: (a) Before phase shift back fold; (b) after phase shift back fold; (c) wavelet denoising; (d) before attenuation correction; (e) after attenuation correction

 ${\varphi _{DP}} = {\phi _{{\rm{DP}}}} + \delta ,$ (1)

X波段雷达的波长仅为3 cm，相较于S、C波段雷达，云和降水粒子等对其能量的吸收和散射不可忽略，衰减影响严重。为了使该雷达观测值更靠近真实值，参考Park et al.（2005）的改进自适应约束算法对ZH进行衰减订正，其原理是：根据雨区（r1rr2，连续15个距离库以上的ZH大于等于20 dBZ）的衰减积分与该路径上的差分传播相移变化总量相一致的约束条件来求取衰减率AH[公式（2）]，再据此得到订正后的ZHcor(r) [公式（3）]：

 $\begin{array}{l} {A_{\rm{H}}}(r) = \frac{{{{[{Z_{\rm{H}}}(r)]}^b}}}{{I({r_1},{r_2}) + ({{10}^{0.1b\alpha \Delta {\varphi _{{\rm{DP}}}}}} - 1)I(r,{r_2})}} \times \\ \quad \quad \quad \quad \quad \quad \quad ({10^{0.1b\alpha \Delta {\varphi _{{\rm{DP}}}}}} - 1), \end{array}$ (2)
 ${Z_{{\rm{Hcor}}}}(r) = {Z_{\rm{H}}}(r) + 2\mathop \smallint \limits_0^r {A_{\rm{H}}}(s){\rm{d}}s,$ (3)

 $I({r_1},{r_2}) = 0.46b\mathop \smallint \limits_{{r_1}}^{{r_2}} {[{Z_{\rm{H}}}(s)]^b}{\rm{d}}s,$ (4a)
 $I(r{\rm{,}}{r_2}) = 0.46b\mathop \smallint \limits_r^{{r_2}} {[{Z_{\rm{H}}}(s)]^b}{\rm{d}}s,$ (4b)
 ${\varphi _{{\rm{DP}}}} = {\varphi _{{\rm{DP}}}}({r_2}) - {\varphi _{{\rm{DP}}}}({r_1}),$ (4c)

3.2 模糊逻辑算法

 $T\left( {x{\rm{, }}{X_1}{\rm{, }}{X_2}{\rm{, }}{X_3}{\rm{, }}{X_4}} \right) = \left\{ {\begin{array}{*{20}{c}} {0{\rm{, }}x ＜ {X_1}}\\ {\frac{{x - {X_1}}}{{{X_2} - {X_1}}},{\rm{ }}{X_1} \le x ＜ {X_2}}\\ {1{\rm{, }}{X_2} \le {\rm{ }}x ＜ {X_3}}\\ {\frac{{{X_4} - x}}{{{X_4} - {X_3}}},{\rm{ }}{X_3} \le x ＜ {X_4}}\\ {0{\rm{, }}x \ge {X_4}} \end{array}} \right.$ (5)

 图 4 2015年6月26日20:00环境温度廓线（方形实心点从右至左分别代表 5 ℃、0 ℃、－10 ℃、－15 ℃和－25 ℃层） Figure 4 Temperature profile at 2000 BT (Beijing Time) 26 June 2015 (squares symbols from right to left denote 5℃, 0℃, －10℃, －15℃, and －25℃)
4 典型雷暴单体分析 4.1 环流背景及天气过程分析 4.1.1 环流背景

 图 5 2015年6月（a）25日、（b）26日20:00的500 hPa环流形势以及26日（c）19:00、（d）20:00和（e）21:00京津冀地区卫星云图。（a、b）中阴影表示温度场，单位：℃；黑色实线代表等高线，单位：dagpm；黑色方框代表研究区域。（c、d、e）中阴影表示亮温，单位：℃ Figure 5 (a, b) Circulations at 500 hPa at 2000 BT (a) 25 and (b) 26 June 2015(shadings indicate the temperature field, units: ℃; black solid isolines represent the geopotential height, units: dagpm; black box represents the research area); (c, d, e) satellite cloud images in Beijing-Tianjin-Hebei area at (c) 1900 BT, (d) 2000 BT, and (e) 2100 BT on 26 June 2015 [shadings indicate Black-Body Temperature (TBB), units: ℃]
4.1.2 天气过程

 图 6 不同反射率因子值（单位：dBZ）百分比随时间变化 Figure 6 Temporal variations of percentages of various reflectivity factors (units: dBZ)

4.2 水成物粒子分类结果与分析 4.2.1 发展阶段水成物粒子水平和垂直结构的时间演变特征

 图 7 雷暴单体发展阶段雷达反射率因子（左列）及其对应时刻水成物粒子识别分布（右列）。（c、d、g、h）为RHI图，为对应（a、b、e、f）PPI图点划线方向的垂直剖面 Figure 7 Radar reflectivity factor (left column) and the corresponding hydrometeors (right column) distributions at the development stage of the thunderstorm cell. (c, d, g, h) The RHI (Radar Height Indicator) diagrams indicate the vertical cross sections along the dash-dotted lines in (a, b, e, f) those PPI diagrams; DR, RA, DS, DC, WS, DG, WG, SH, LH, RH are the abbreviations of Drizzle Rain, Rain, Dry Snow, Dry Crystal, Wet Snow, Dry Graupel, Wet Graupel, Small Hail, Large Hail, Rain and Hail

 图 8 同图 7，但为雷暴单体成熟阶段 Figure 8 Same as Fig. 7, but for the mature stage of the thunderstorm cell

 图 9 同图 7，但为雷暴单体消散阶段 Figure 9 Same as Fig. 7, but for the dissipation stage of the thunderstorm cell

4.2.2 成熟阶段水成物粒子水平和垂直结构的时间演变特征

 图 10 雷暴单体（a、b）发展、（c、d、e）成熟、（f、g）消散阶段径向速度垂直分布图 Figure 10 Vertical distributions of radial velocity in (a, b) development, (c, d, e) maturation, and (f, g) dissipation stages of thunderstorm cell

4.2.3 消散阶段水成物粒子水平和垂直结构的时间演变特征

4.3 水成物粒子演变模型的建立

 图 11 雷暴单体发展、成熟和消散阶段水成物粒子占比 Figure 11 Percentages of hydrometeors in the development, maturation, and dissipation phases of the thunderstorm cell

 图 12 径向速度垂直分量（左列；方框内为雷暴单体）和基于水成物粒子识别结果的雷暴单体模型（右列，箭头代表粒子运动方向）：（a、b）发展阶段；（c、d）成熟阶段；（e、f）消散阶段 Figure 12 Vertical component of radial velocity (left column; thunderstorm cell is in the box) and the model of thunderstorm cell based on hydrometeors (right column; arrows indicates the moving directions of hydrometeors): (a, b) Development; (c, d) maturation; (e, f) dissipation

5 结论和讨论

（1）雷暴单体经历发展、成熟和消散3个阶段，其中成熟阶段反射率因子大于45 dBZ的库数明显多于发展和消散阶段，其占比为3.6%，远大于发展阶段的0.6%和消散阶段的0.4%。此外，三个阶段单体高度分别可达11、12和10 km，霰粒子占各自阶段单体内所有粒子百分比分别为2%、12%和1%，呈现显著的先增后减趋势，与雷暴单体发展过程回波强度变化对应较好。

（2）各阶段主要微物理过程及演变特征是：发展阶段，单体0℃层以下由暖云过程主导，毛毛雨占5%，雨滴占24%；少量液态粒子上升至0℃层以上与冰晶反应生成1%干霰，冷云过程较弱。成熟阶段，相较发展阶段0℃层以下毛毛雨减少约2个百分点，雨滴增多约2个百分点，粒子碰并加强，暖云过程增强；较多液态粒子上升至0℃层以上，约有4%的雨滴与5%的冰晶通过凇附作用生成7%的霰，冷云过程增强。消散阶段，下层液态粒子难以上升至0℃层以上形成初始冰晶，使暖云及冷云过程都减弱，0℃层以下毛毛雨相较成熟阶段平均增多约1个百分点，粒子碰并减弱；0℃层以上冰晶消耗减少2个百分点，霰生成减少5个百分点。

（3）结合粒子垂直运动速度及水成物粒子分布建立雷暴单体内水成物粒子演变模型：发展阶段，单体0℃层以上后部为正垂直速度推动雷暴单体发展，少量液态粒子跨越至0℃层以上，冷云过程极弱。成熟阶段，单体后部正垂直速度区扩展至0℃层以下，使大量液态粒子上升至0℃层以上，6%的雨滴与5%的冰晶转化为2%的雪和9%的霰，冷云过程增强。消散阶段0℃层附近的负速度区阻碍液态粒子跨越0℃层使暖云及冷云过程都减弱，0℃层以下雨滴减少1个百分点，毛毛雨增多3个百分点，0℃层以上霰粒子减少9个百分点，雨滴与冰晶增加4个百分点。