2016年北京“7·20”特大暴雨降水物理过程模拟诊断研究

陆婷婷; 崔晓鹏

doi:10.3878/j.issn.1006-9895.2104.20232

2016年北京“7·20”特大暴雨降水物理过程模拟诊断研究

doi: 10.3878/j.issn.1006-9895.2104.20232

陆婷婷^{1, 4, 5,},
崔晓鹏^{1, 2, 3, 4, ,}

1.
中国科学院大气物理研究所云降水物理与强风暴重点实验室，北京 100029
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.
中国气象局沈阳大气环境研究所，沈阳 110166
4.
中国科学院大学，北京 100049
5.
宁波市气象台，宁波 315012

基金项目: 国家自然科学基金面上项目42075009，中国气象局沈阳大气环境研究所基本科研业务费重点项目2020SYIAEZD4

详细信息

作者简介:
陆婷婷，女，1993年出生，博士研究生，主要从事暴雨相关研究。E-mail: lutingting@mail.iap.ac.cn

通讯作者:
崔晓鹏，E-mail: xpcui@mail.iap.ac.cn

中图分类号: P401
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- 被引次数: 0
出版历程
- 收稿日期: 2020-11-25
- 录用日期: 2021-08-13
- 网络出版日期: 2021-09-09
- 刊出日期: 2022-03-16

Simulation and Diagnosis of the Physical Process of the “7·20” Heavy Rainfall in Beijing in 2016

Tingting LU^{1, 4, 5
,},
Xiaopeng CUI^{1, 2, 3, 4
, ,}

1.
Key Laboratory of Cloud–Precipitation Physics and Severe Storms, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029
2.
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing 210044
3.
The Institute of Atmospheric Environment, China Meteorological Administration, Shenyang 110166
4.
University of Chinese Academy of Sciences, Beijing 100049
5.
Ningbo Meteorological Observatory, Ningbo 315012

Funds: National Natural Science Foundation of China (Grant 42075009), the Key Project of Shenyang Institute of Atmospheric Environment, China Meteorological Administration (Grant 2020SYIAEZD4)

摘要

摘要: 利用WRF模式，结合三维降水诊断方程，对2016年北京“7·20”特大暴雨过程主降水时段的强降水物理过程开展了高分辨率模拟诊断分析。结果显示：降水峰值时刻前，强盛水汽辐合支撑强降水，同时加湿大气，后期，水汽辐合显著减弱，降水造成局地大气中水汽含量明显减少；降水峰值时刻前，水汽辐合、凝结和液相水凝物辐合共同助力强降水云系快速发展，后期，动力辐合作用减弱以及水凝物持续消耗和辐散，导致水凝物含量显著减少，降水系统逐步瓦解；主降水时段，垂直上升运动强度和垂直扩展范围逐步增大，并在降水峰值时刻达最大，之后减弱收缩；上升运动峰值高度从初期位于零度层上逐步降到零度层附近，进而回落到零度层之下，伴随“弱—强—弱”的降水强度变化；上升运动控制下，水凝物含量变化明显，但不同水凝物变化幅度不一，霰粒子和雨滴增幅最显著，并于降水峰值时刻含量达最大，随后减小，其他水凝物由于微物理转化和动力辐散等过程，导致其含量的变化幅度弱于上述两者。本文研究同时指出，不同微物理参数化方案对“7·20”特大暴雨强降水物理过程的可能影响以及不同强度降水物理过程的差异，值得深入研究。
- “7·20”特大暴雨 /
- 降水物理过程 /
- 三维降水诊断方程
Abstract: Using a WRF model and a three-dimensional precipitation diagnostic equation, a high-resolution simulation and diagnosis analysis of the physical process of the heavy precipitation during the main precipitation period of the heavy rains in Beijing was carried out on July 20 2016. Results show that before the peak of precipitation, strong water vapor convergence supports strong precipitation while humidifying the atmosphere. In the later stage, the water vapor convergence is significantly weakened, and the precipitation causes an obvious reduction in the water vapor content in the local atmosphere. Before the peak time of the precipitation, the water vapor convergence, condensation, and liquid-phase condensate convergence jointly contribute to the rapid development of the heavy precipitation cloud system. In the later stage, the weak dynamic convergence effect and the continuous consumption and divergence of the water condensate lead to the significant decrease of the water condensate content, thus resulting in the gradual disintegration of the precipitation system. During the main precipitation period, the intensity and range of the vertical upward motion gradually increased and reached the maximum peak of precipitation, after which it weakened and contracted. The peak height of the ascending motion is located at the zero level in the initial stage and then decreases to the lower part of the zero level, accompanied by a “weak-strong-weak” precipitation intensity change. Under the control of ascending motion, the change range of the water condensate is obvious, but the change range of a different water condensate is different. Graupel particles and raindrops increase most significantly; the contents reach the maximum at the peak of precipitation then decrease. The variation range of other water condensates is weaker than the above two due to the process of microphysical transformation and dynamic divergence. This paper also points out that the possible influence of different microphysical parameterization schemes on the physical process of heavy rain happened on July 20, and differences of physical processes of precipitation with different intensities are worthy of further study.
- “7·20” torrential rain /
- Precipitation processes /
- Three dimensional precipitation equation

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图 1 2016年7月20日00时（北京时，下同）至21日08时（a）实况和（b）模拟（分辨率为1.33 km）的累积降水量（彩色阴影，单位：mm）。紫色实线为200 m地形等高线，（a）中灰色圆点为累积降水量大于300 mm的观测站点，黑色圆点为累积降水量最大的观测站（东山村站，累积降水量为401.3 mm）；（b）中灰色方框所示区域为文中降水物理过程分析区域

Figure 1. Distribution of the (a) observed and (b) simulated (with a resolution of 1.33 km) cumulated precipitation from 0000 BST 20 to 0800 BST (Beijing Standard Time) on July 21, 2016 (shaded, units: mm). A thick purple line denotes the 200-m terrain elevation. Gray and black dots in (a) represent stations with a cumulated precipitation of more than 300 mm and a maximum cumulated precipitation (Dongshancun station, 401.3 mm), respectively. The gray box in (b) indicates the analysis area of the precipitation physical process

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图 2 2016年7月19日08时至21日02时逐6小时的500 hPa位势高度场（黑色实线，单位：gpm）和850 hPa大于12 m s⁻¹的风场（风向杆）：（a）19日08时；（b）19日14时；（c）19日20时；（d）20日02时；（e）20日08时；（f）20日14时；（g）20日20时；（h）21日02时

Figure 2. 500-hPa geopotential height (height, units: gpm), 850-hPa wind field (wind bar, >12 m s⁻¹) at (a) 0800 BST 19, (b) 1400 BST 19, (c) 2000 BST 19, (d) 0200 BST 20, (e) 0800 BST 20, (f) 1400 BST 20, (g) 2000 BST 20, (h) 0200 BST 21 July 2016

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图 3 数值模拟区域

Figure 3. Model domain configuration

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图 4 2016年7月19日20时至21日02时的逐6小时500 hPa位势高度（蓝色实线，单位：gpm，蓝色粗实线为5880gpm等高线）、850 hPa风矢量（风向杆）和大于等于12 m s⁻¹的风速（彩色阴影）：（a1, b1, c1, d1, e1, f1）ERA-interim再分析数据；（a2–f2）D01区域（分辨率为12 km）模拟结果：（a1，a2）19日20时；（b1，b2）20日02时；（c1，c2）20日08时；（d1，d2）20日14日；（e1，e2）20日20时；（f1，f2）21日02时

Figure 4. 500-hPa geopotential height (blue counter, units: gpm, thick lines indicate 5880 gpm), 850-hPa wind field (vector) and 850-hPa wind field (shaded, ≥12 m s⁻¹) from (a1, b1, c1, d1, e1, f1) ERA-interim reanalysis data and (a2, b2, c2, d2, e2, f2) numerical simulation data with 12 km horizontal resolution at (a1, a2) 2000 BST 19, (b1, b2) 0200 BST 20, (c1, c2) 0800 BST 20, (d1, d2) 1400 BST 20, (e1, e2) 2000 BST 20, (f1, f2)0200 BST 21 July 2016.

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图 5 2016年7月20日00时至20日23时华北区域雷达组合反射率（彩色阴影，单位：dBZ）

Figure 5. Combined radar reflectivity in North China (shaded, units: dBZ) from 0000 BST July 20 to 2300 BST July 20, 2016

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图 6 同图5，但为模拟结果

Figure 6. Same as Fig.5, but for the simulated radar reflectivity

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图 7 2016年7月20日00时至20日23时北京地区逐小时实况降水量分布（彩色阴影，单位：mm）。灰色实线为200 m地形等高线

Figure 7. Distribution of hourly rainfall observations in Beijing (shaded, units: mm) from 0000 BST July 20 to 2300 BST July 20, 2016. A thick gray line denotes the 200-m terrain elevation

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图 8 同图7，但为模拟结果

Figure 8. Same as Fig.7, but for the simulated rainfall

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图 9 2016年7月20日00时至21日08时区域（39.5°～40.9°N，115.6°～117.3°E）平均的实况（实线）和模拟（虚线）降水率（单位：mm h⁻¹）的时间序列

Figure 9. Time series of the area-averaged (39.5°–40.9°N, 115.6°–117.3°E) observation (solid line) and model (dashed line) precipitation rate (units: mm h⁻¹) from 0000 BST July 20 to 0800 BST July 21, 2016

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图 10 2016年7月20日00时至21日08时区域（39.5°~40.9°N，115.6°~117.3°E）平均的（a）降水率（P_S，黑色实线）、水汽相关过程变率（Q_WV，蓝色实线）和云相关过程变率（Q_CM，红色实线）的时间演变，（b）Q_WV（灰色实线）、Q_WVA(三维水汽通量辐合/辐散率，红色虚线）、Q_WVL（水汽局地变化率的负值，蓝色虚线）、Q_WVD（水汽三维耗散率，橙色虚线）、Q_WVE（地面蒸发率，紫色虚线）(单位：mm h⁻¹）的时间演变，（c）Q_CL（灰色实线）、Q_CLL（液相水凝物局地变率的负值，红色虚线）、Q_CLA（液相水凝物通量辐合/辐散率，蓝色虚线）、Q_CLD（液相水凝物三维耗散率，橙色虚线）的时间演变，（d）Q_CI（灰色实线）、Q_CIL（固相水凝物局地变率的负值，红色虚线）、Q_CIA（固相水凝物通量辐合/辐散率，蓝色虚线）、Q_CID（冰相水凝物三维耗散率，橙色虚线）的时间演变

Figure 10. Temporal evolutions of the area-averaged (39.5°–40.9°N, 115.6°–117.3°E) (a) P_S(black solid line), moisture-related processes (Q_WV: blue solid line), change rates for hydrometeor-related processes (Q_CM, red solid line, units: mm h⁻¹); (b) Q_WV(gray solid line), Q_WVA(3D moisture flux convergence/divergence rate, red dotted line), Q_WVL(negative local change rate of water vapor, blue dotted line), Q_WVD (3D moisture diffusion rate, orange dotted line), Q_WVE (surface evaporation rate, purple dotted line, units: mm h⁻¹); (c) Q_CL(gray solid line), Q_CLL (negative local change rate of liquid-phase hydrometers, red dotted line), Q_CLA (3D flux convergence/divergence rate of liquid-phase hydrometers, blue dotted line), Q_CLD (3D diffusion rate of liquid-phase hydrometers, orange dotted line, units: mm h⁻¹); (d) Q_CI (gray solid line), Q_CIL(negative local change rate of ice-phase hydrometers, red dotted line), Q_CIA (3D flux convergence/divergence rate of ice-phase hydrometers, blue dotted line), Q_CID (3D diffusion rate of ice-phase hydrometers, orange dotted line, units: mm h⁻¹) from 0000 BST July 20 to 0800 BST July 21, 2016

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图 11 2016年7月20日00时至21日03时区域（39.5°～40.9°N，115.6°～117.3°E）平均的云水凝物混合比和垂直速度廓线（Q_g：霰粒子，Q_s：雪粒子，Q_i：冰晶，Q_r：雨滴,Q_c：云水，单位：10⁻³ kg kg⁻¹；ｗ: 垂直速度，单位：m s⁻¹）的逐时分布。图中右上角数值代表时间（例如“0000BST20”代表20日00时）

Figure 11. Area-averaged (39.5°–40.9°N, 115.6°–117.3°E) vertical profiles of hydrometeor mixing ratios (Q_g for graupel, Q_s for snow, Q_i for cloud ice, Q_r for raindrops, Q_c for cloud water, units: 10⁻³ kg kg⁻¹, w for vertical speed, unit: m s⁻¹) from 0000 BST July 20 to 0300 BST July 21, 2016

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表 1 模拟方案设置

Table 1. Model configurations

	D01	D02	D03
分辨率	12 km	4 km	1.33 km
积分时段(UTC)	7月19日00时至21日00时（48 h）	7月19日12时至21日00时（36 h）	7月19日12时至21日00时（36 h）
微物理参数化方案	WSM6	WSM6	WSM6
长波辐射方案	RRTM	RRTM	RRTM
短波辐射方案	Dudhia	Dudhia	Dudhia
近地面层方案	MM5 Monin-Obukhov	MM5 Monin-Obukhov	MM5 Monin-Obukhov
陆面过程方案	unified Noah land-surface model	unified Noah land-surface model	unified Noah land-surface model
边界层参数化方案	YSU	YSU	YSU
积云对流参数化方案	Kain-Fritsch (new Eta)	-	-

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表 2 三维降水诊断方程各项物理含义

Table 2. Physical descriptions of terms in the 3D WRF-based precipitation equation

方程项	物理含义
P_S	地面降水率/降水强度
Q_WVL	水汽局地变化率垂直积分的负值
Q_WVA	水汽三维通量辐合/辐散率垂直积分
Q_WVE	地（海）面蒸发率
Q_WVD	水汽三维耗散率垂直积分
Q_CLL	液相水凝物（云滴和雨滴）局地变化率垂直积分的负值
Q_CLA	液相水凝物（云滴和雨滴）三维通量辐合/辐散率垂直积分
Q_CLD	液相水凝物（云滴和雨滴）三维耗散率垂直积分
Q_CIL	冰相水凝物（云冰、雪、霰等）局地变化率垂直积分的负值
Q_CIA	冰相水凝物（云冰、雪、霰等）三维通量辐合/辐散率垂直积分
Q_CID	冰相水凝物（云冰、雪、霰等）三维耗散率垂直积分

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