基于多雷达观测和数值模拟的中天山北坡夏季降水过程研究

刘恩弘; 杨璟; 银燕; 景晓琴; 杨军; 李斌; 杨玲; 佘勇; 袁亮; 林彦君; 姜冬昕

doi:10.3878/j.issn.1006-9895.2204.21196

基于多雷达观测和数值模拟的中天山北坡夏季降水过程研究

doi: 10.3878/j.issn.1006-9895.2204.21196

刘恩弘^{1, 2,},
杨璟^1, ,,
银燕¹,
景晓琴¹,
杨军¹,
李斌³,
杨玲⁴,
佘勇⁴,
袁亮⁵,
林彦君¹,
姜冬昕¹

1.
中国气象局气溶胶与云降水重点开放实验室/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
2.
中国气象局云雾物理环境重点开放实验室, 北京100081
3.
新疆维吾尔自治区人工影响天气办公室, 新疆830002
4.
成都信息工程大学电子工程学院, 成都610225
5.
成都平原城市气象与环境四川省野外科学观测研究站, 成都610225

基金项目: 科技部重点研发项目2017YFC1501402，西北人工影响天气工程项目ZQC-R18211，中国气象局西北区域人影建设研究试验项目RYSY201902，国家自然科学基金项目41905124、42005064，江苏省自然科学基金项目BK20190777、BK20190778

详细信息

作者简介:
刘恩弘，男，1996年出生，硕士研究生，主要从事云降水物理研究。E-mail: 81438548@qq.com

通讯作者:
杨璟，E-mail: jing.yang@nuist.edu.cn

中图分类号: P401
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- 被引次数: 0
出版历程
- 收稿日期: 2021-10-20
- 录用日期: 2022-06-29
- 网络出版日期: 2022-08-31

A Case Study on Summer Precipitation Process in the Central Tianshan Area Using Multi-radar Observation and Model Simulation

Enhong LIU^{1, 2
,},
Jing YANG^{1
, ,},
Yan YIN¹,
Xiaoqin JING¹,
Jun YANG¹,
Bin LI³,
Ling YANG⁴,
Yong SHE⁴,
Liang YUAN⁵,
Yanjun LIN¹,
Dongxin JIANG¹

1.
Key Laboratory for Aerosol–Cloud Precipitation of China Meteorological Administration, Nanjing University of Information Science & Technology /Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing 210044
2.
Key Laboratory of Cloud and Fog Physical Environment of China Meteorological Administration, Beijing, 100081
3.
Weather Modification Office of Xinjiang Uygur Autonomous Region, Xinjiang, 830002
4.
College of Electronic Engineering of Chengdu University of Information Technology, Chengdu, 610225
5.
Chengdu Plain Urban Meteorology and Environment Scientific Observation and Research Station of Sichuan Province, Chengdu, 610225

Funds: National Key Research and Development Program of China (Grant 2017YFC1501402), Capacity Building Project of Weather Modification in Northwest China (Grants ZQC-R18211, RYSY201902), National Natural Science Foundation of China (Grants 41905124, 42005064), Natural Science Foundation of Jiangsu Province, China (Grants BK20190777, BK20190778)

摘要

摘要: 基于微雨雷达、Ka波段云雷达、C波段天气雷达和微波辐射计等仪器的观测资料对2019年7月27日中天山地区一次局地对流云降水过程的精细结构及演变过程进行分析，并结合WRF高分辨率数值模式模拟结果研究了热力不稳定结构及风切变层对云发展的影响。结果表明：此次降水过程中天山北坡区域受到地形热力强迫，形成爬坡气流，并与翻越天山山脉的偏南气流在局部形成对流；雷达观测发现，由于天山山区受到高空西风的控制，局地产生的对流云团不足以突破中天山北坡上空的风速较大的西南气流或偏西气流，低层的偏北气流被高层气流夹带而转向形成风切变层。降水发生后，低层对流云团被限制在风切变层以下，云顶平整且高度较低，风切变层对对流云团存在明显的抑制作用。通过分析模拟结果，此次降水过程中风切变层对中天山北坡降水云的发展及热力不稳定变化影响十分重要，高层西南风对相当位温的平流输送使得风切变层上空更倾向于热力不稳定，同时使其下方更倾向于热力稳定从而抑制低层对流而促进高层对流的发展。当低层对流云团强度不足以突破其上空因垂直风切变导致的稳定层结，对流便会被局限于垂直风切变层以下，使得降水强度减弱。
- 中天山 /
- 多雷达观测 /
- 风切变 /
- 位势散度
Abstract: The fine vertical structure and evolution of orographic precipitation in the middle Tianshan area was analyzed using measurements taken from a micro rain radar, Ka-band cloud radar, and microwave radiometer. In addition, a high-resolution simulation is conducted to analyze the thermal instability and wind shear layer influence on cloud generation. The observations reveal that the precipitation was generated owing to the convergence between the southwesterly wind flying across the mountain ridge and the northerly wind generated by the thermal forcing in the terrain. Because the observed convective updraft was not strong enough, the low-level northerly flow turned southward as it approached the high-level southwesterly wind, resulting in strong wind shear. Following the precipitation, the low-level convective clouds were constrained to remain below the wind shear layer, and the cloud tops were generally flat and low, indicating that the wind shear layer significantly inhibited the convection. The model simulation suggests that the influence of wind shear on developing precipitation clouds and the change in the thermal instability on the northern slope of the Central Tianshan Mountains during this precipitation is crucial. The advective transport of equivalent potential temperature, caused by the action of upper-level southerly winds, was responsible for the layer above the wind shear layer becoming thermally unstable and the layer underneath it becoming thermally stable, thereby suppressing low-level convection and promoting upper-level convection. If low-level convective updrafts were not strong enough to break through the stable laminar junction caused by vertical wind shear, convection would be constrained to remain below the vertical wind shear layer, preventing intense precipitation.
- Central Tianshan /
- Multi-radar observation /
- Wind shear /
- Potential divergence

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图 1 中天山北坡地形及观测仪器所在位置，黄色圆圈表示C波段天气雷达扫描范围（半径75 km），红色实线表示云雷达扫描范围

Figure 1. Topography topography of the northern slope of central Tianshan Mountain and the location of the observation instruments; the yellow circle indicates the C-band weather radar scanning range, and the solid red line indicates the cloud radar scanning range

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图 2 模式模拟区域

Figure 2. Domains for the numerical simulations used in this study

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图 3 2019年7月（a、b）26日20:00、（c、d）27日14:00 500 hPa（左列）和700 hPa（右列）ERA5再分析资料位势高度场（实线，单位：gpm）及风场分布，棕色实线为槽线，红色三角为观测站点所在位置

Figure 3. Distributions of geopotential height field (solid line, units: gpm) and wind field from ERA5 reanalysis data at 500 hPa (left column) and 700 hPa (right column) at (a, b) 2000 BJT (Beijing time) on July 26 and1400 BJT on July 27,2019. The dark red curves indicate the trough and the red triangle is the location of the station

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图 4 2019年7月27日17:00（a）ERA5再分析资料800 hPa流场分布和（b）沿观测站点东西剖面WRF模拟的风场垂直分布，红线为图4b剖面位置。2019年7月27日（c）16:45～17:00和（d）17:00～17:15 FY-4A卫星可见光通道云图，红色三角为观测站点位置

Figure 4. (a) Distributions of ERA5 reanalysis data 800 hPa flow field and (b) the vertical distributions of wind simulated using WRF field along the east–west profile of the station at 1700 BJT July, 2019. FY-4A satellite true color at (c) 1645 BJT–1700 BJT and (d) 1700 BJT–1715 BJT, the red line is the position of the profile in fig.4b and the red triangle is the position of the station

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图 5 2019年7月27日06:00～17:00微波辐射计观测（a）温度（单位：°C）、（b）相对湿度和（c）水汽密度（单位：g m⁻³）廓线分布

Figure 5. Distributions of (a) temperature profile (units: °C), (b) relative humidity profile, and (c) water vapor concentration profile (units: g m⁻³) from microwave radiometer observations from 0600 BJT to 1700 BJT on July 27, 2019

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图 6 2019年7月27日（a、b）16:04和（c、d）17:03 C波段雷达第三层扫描雷达反射率因子（左列）及多普勒径向速度（右列）分布红色圆点为C波段天气雷达位置，红色三角为Ka波段云雷达位置，仰角为2.4°

Figure 6. Distributions of reflectivity factors (left column) and radial velocity (right column) at (a, b) 1604 BJT, (c, d) 1703 BJT July 27, 2019, the red dot is the C-band weather radar position, and the red triangle is the Ka-band cloud radar position, elevation angle=2.4°

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图 7 2019年7月27日15:00～22:00 Ka波段云雷达垂直观测高度时间序列：（a）雷达反射率因子；（b）多普勒径向速度；（c）速度谱宽

Figure 7. Time evolution of Ka-band cloud radar (a) reflectivity factors, (b) doppler radial velocity, (c) width spectrum and time evolution of MRR from 1500 BJT to 2200 BJT on July 27, 2019

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图 8 2019年7月27日15:00～22:00微雨雷达垂直观测高度时间序列：（a）反射率因子；（b）下落末速度

Figure 8. Time evolution of micro rain radar (a) reflectivity factors and (b) terminal fall velocity from 1500 BJT to 2200 BJT on July 27, 2019

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图 9 2019年7月27日（a、b、c）16:36、（d、e、f）16:46、（g、h、i）16:56、（j、k、l）17:06和（m、n、o）17:17 Ka波段云雷达RHI扫描雷达反射率因子（左列）、多普勒径向速度（中间列）及速度谱宽（右列）的垂直结构

Figure 9. Ka-band cloud radar RHI scan reflectivity factors (left colu), Doppler radial velocity (middle column) and width spectrum (right column) vertical structure at (a, b, c) 1636 BJT, (d, e, f) 1646 BJT, (g, h, i) 1656 BJT, (j, k, l) 1706 BJT, and (m, n, o) 1717 BJT on July 27, 2019

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图 10 2019年7月27日（a）16:26、（b）16:36、（c）16:46、（d）16:56和（e）17:06 Ka波段云雷达反射率因子概率分布（其中黑色实线为雷达反射率因子中位数线），以及（f）雷达反射率因子中位数廓线

Figure 10. Distributions of Ka-band cloud radar reflectivity factors contoured frequency using altitude diagrams at (a) 1626 BJT, (b) 1636 BJT, (c) 1646 BJT, (d) 1656 BJT, and (e) 1706 BJT on July 27, 2019, where the solid black line is the median radar reflectivity factor line, and (f) median radar reflectivity factor profiles

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图 11 2019年7月27日（a）16:00、（b）16:30、（c）17:00和（d）17:30沿图4a红线相当位温（等值线，单位：K）、水汽混合比（彩色阴影，单位：g kg⁻¹）以及风场（灰色箭头）的南北方向剖面。横坐标原点为云雷达所在位置

Figure 11. North–south profiles for equivalent potential temperature (contours, units: K), water vapor mixing ratio (colored shading, units: g kg⁻¹), and wind field (gray arrows) along the red line of Fig. 4a at (a) 1600 BJT, (b) 1630 BJT, (c) 1700 BJT, and (d) 1730 BJT on July 27, 2019. 0 on the x-axis indicates the location of the cloud radar

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图 12 2019年7月27日（a、b、c）16:00、（d、e、f）16:30和（g、h、i）17:00沿图4a红线位势散度m$ \mathrm{m} $（左列，彩色阴影）、$ m $的分量$ {m}_{bc} $（中间列，彩色阴影）、$ m $的分量$ {m}_{bt} $（右列，彩色阴影、）的南北方向剖面，单位：10⁻⁶ K m⁻¹ s⁻¹，灰色阴影为地形

Figure 12. North–south profiles of potential dispersion m (left column, colored shading), component $ {m}_{bc} $ (middle column, colored shading), and component $ {m}_{bt} $ (right column, colored shading) along the red line of Fig. 4a at (a, b, c)1600 BJT, (d, e, f) 1630 BJT, and (g, h, i) at 1700 BJT on July 27, 2019, units: 10⁻⁶ K m⁻¹ s⁻¹,the grey shading is terrain

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表 1 观测仪器主要指标和性能

Table 1. Main indicators and performance of the observation instruments

名称	频率/波长	时间分辨率	空间分辨率	探测产品
C波段天气雷达	5.47 GHz/5.48 cm	6 min	150 m	反射率因子、径向速度、速度谱宽
Ka波段云雷达	35 GHz/8.59 mm	5 min	150 m	反射率因子、径向速度、速度谱宽
微雨雷达	24.23 GHz/1.238 cm	30 s	100 m	反射率因子、径向速度、速度谱宽
微波辐射计	—	2 min	50 m、100 m和250 m	温度、湿度、水汽

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表 2 模式参数设置

Table 2. Configuration of the model

	三层嵌套网格
	D01	D02	D03
水平空间分辨率（格点数）	9 km （600×500）	3 km （511×400）	1 km （697×535）
垂直层数	33层
积云参数化方案	Grell-Freitas方案
微物理方案	Thompson方案
长波辐射方案	RRTMG方案
短波辐射方案	RRTMG方案
陆面过程方案	Noah方案
近地面层方案	Monin-Obukhov(Janjic Eta)方案
边界层方案	MYJ方案

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A1 参数化组合试验方案

试验名称	边界层方案	云微物理方案
MThompson	MYJ方案	Thompson方案
MLin	MYJ方案	Lin方案
MWSM6	MYJ方案	WSM6方案
BThompson	BL方案	Thompson方案
BLin	BL方案	Lin方案
BWSM6	BL方案	WSM6方案

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