气溶胶对北京地区云和降水影响的模拟研究

doi:10.11821/dlyj201610010

摘要/Abstract

摘要：

利用中尺度气象模式WRF中一个含有云凝结核CCN活化过程的双参数云微物理方案（NSSL 2-mom+ccn）,研究不同气溶胶导致的初始CCN浓度对云物理和降水过程的影响。完整地模拟了北京市2015年7月16-17日一次典型的从小雨到暴雨连续降水过程,包括降水主要集中的时间段,雨带的西北—东南走向,降水初期普降小雨、后期转变为中到大雨甚至局部地区出现暴雨,24小时最大累积降水量可以达到65 mm,模拟结果与实测符合较好。模拟得到的区域平均累积降水量随着气溶胶的增加而减少,与其他研究根据地面降水与能见度资料统计分析的结论相一致。比较详细地给出了气溶胶对云的微物理结构、水成物时空分布以及微物理过程的转化率的影响。

关键词: 气溶胶, 云, 降水, 数值模拟, 北京

Abstract:

In this paper, the influence of initial CCN concentration on the physical and precipitation processes of cloud was numerically studied by using mesoscale model WRF. A two parameter cloud micro physics scheme (2-mom+ccn NSSL) containing CCN activation process of cloud condensation nuclei is utilized to investigate the impacts of different aerosol backgrounds on the cloud microphysical processes and precipitation. A typical precipitation event near Beijing (July 16-17, 2015), which was formatted as a drizzle case and then developed into heavy precipitation, was simulated in order to examine the sensitivity of aerosol pollution. Controlled experiment captured the characters of this rainfall case very well. Rainfall rate peak in the time series and its varying tendency and moving direction of precipitation strength center were modeled with reliable accuracy. The modeled average precipitation rate decreased with rising aerosol background concentration, which is consistent with studies of some other authors. Structure of cloud system, temple and spatial variation of hydrometeor categories and strength of their sink and source terms are compared and analyzed. Our numerical simulation can completely simulate the whole process of the precipitation, including main precipitation-concentrated period of time, the rain belt in the northwest-southeast direction, precipitation at the beginning of the heavy rain, the late shift to rain or even local rainstorm. Simulation results show that the maximum cumulative precipitation of 24 hours can reach 65 mm, which is in good agreement with the measured data, and the effect of aerosol on precipitation efficiency is different in different precipitation stages. We found that when the initial CCN concentration is higher, the maximum precipitation efficiency occurs in the light rain stage, and the precipitation efficiency is reduced in the rainstorm period. There was no significant change in the precipitation range of the three initial CCN concentrations, but the location of the strong precipitation center shifted from the southwest to the northeast of Beijing. Our simulations also show that the regional mean accumulated precipitation decreases with increasing aerosol, which is consistent with the conclusions of other authors based on the statistical analysis of the ground precipitation and visibility data. Effect of aerosol on cloud micro physical structure, hydrometeor spatial and temporal distribution and conversion of microphysical processes were also given in detail. The results show that in both of early or late precipitation, the background aerosol cloud water content in the air were increased. Our research work is preliminary, but it can be used to carry out a more detailed study of the coupling chemistry model WRF-Chem. Due to the fact that the influence of aerosol on cloud and precipitation is very complex, the relevant observational data is still lacking. Therefore, the research on this aspect is still continuing to carry out extensive and in-depth study in the future.

Key words: numerical simulation, aerosol, cloud, precipitation, Beijing

师宇, 楼小凤, 王广河. 气溶胶对北京地区云和降水影响的模拟研究[J]. 地理研究, 2016, 35(10): 1912-1924.

Yu SHI, Xiaofeng LOU, Guanghe WANG. Numerical simulation of the effect of aerosol on cloud and precipitation in Beijing[J]. GEOGRAPHICAL RESEARCH, 2016, 35(10): 1912-1924.

图/表 9

表1

不同水成物的基本参数"

水成物种类	$ν$	$μ$	$α$	密度（kg/m^-3）
云滴	0	1	$3 ν + 2$	1000
雨滴	-0.8	1	$3 ν + 2$	1000
冰晶	0	1	$3 ν + 2$	900
雪	-0.8	1	$3 ν + 2$	100
霰	-2/3	1/3	0	300~900
雹	-2/3	1/3	1	500~900

表1

图1

图2

图3

图4

图5

图6

表2

表3

参考文献 19

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雨滴的主要源项	物理意义	16日12时微物理过程转化率最大值				17日05时微物理过程转化率最大值
雨滴的主要源项	物理意义	正常		中度污染	重度污染	正常	中度污染	重度污染
		（1500）	（5000）	（8000）		（1500）	（5000）	（8000）
qracw	雨滴碰并云滴	0.065	0.064	0.054		0.163	0.115	0.036
qraci	雨滴收集冰晶粒子	8.23×10^-4	7.06×10^-4	2.97×10^-4		1.48×10^-3	3.3×10^-4	4.15×10^-5
qrcnw	云雨的自动转化	2.83×10^-3	1.398×10^-3	2.86×10^-3		2.75×10^-2	1.61×10^-2	9.23×10^-3
qhmlr	霰的融化	0.186	0.24	0.24		0.114	0.152	0.067
qsmlr	雪的融化	0.113	0.109	0.10		0.061	0.055	0.034

霰的主要源项	物理意义	16日12时微物理过程转化率最大值				17日05时微物理过程转化率最大值
霰的主要源项	物理意义	正常		中度污染	重度污染	正常	中度污染	重度污染
		（1500）	（5000）	（8000）		（1500）	（5000）	（8000）
qhaci	霰收集冰晶	2.106×10^-3	1.509×10^-3	1.19×10^-3		2.89×10^-4	7.643×10^-4	2.652×10^-4
qhcni	冰晶向霰的转化	1.526×10^-3	9.386×10^-4	6.799×10^-4		4.695×10^-5	1.296×10^-4	7.452×10^-5
qhcns	雪向霰的转化	3.376×10^-2	4.986×10^-2	5.4×10^-2		1.282×10^-2	1.428×10^-2	1.038×10^-2
qhacs	霰碰并雪	6.102×10^-2	6.401×10^-2	6.246×10^-2		2.681×10^-2	3.524×10^-2	1.297×10^-2
qhacw	霰碰并过冷云滴	0.107	0.146	0.144		0.062	0.129	0.06
qhacr	霰碰并过冷雨滴	0.023	0.015	0.017		0.057	0.031	0.065
qhdpv	霰的凝华增长	5.669×10^-3	6.762×10^-3	7.119×10^-3		3.082×10^-3	4.644×10^-3	1.925×10^-3
qrfrz	雨滴的冻结	1.014×10^-7	5.339×10^-8	3.879×10^-8		2.626×10^-4	1.206×10^-5	2.921×10^-10