A Numerical Seeding Simulation of Convective Precipitation in Zhejiang, China

Lou Xiaofeng; Fu Yu; Sun Jing

doi:10.11898/1001-7313.20190603

Vol.30, NO.6, 2019

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Article Navigation > Journal of Applied Meteorological Science > 2019 > 30(6): 665-676

Lou Xiaofeng, Fu Yu, Sun Jing. A numerical seeding simulation of convective precipitation in Zhejiang, China. J Appl Meteor Sci, 2019, 30(6): 665-676. DOI: 10.11898/1001-7313.20190603.

Citation:

Lou Xiaofeng, Fu Yu, Sun Jing. A numerical seeding simulation of convective precipitation in Zhejiang, China. J Appl Meteor Sci, 2019, 30(6): 665-676. DOI: 10.11898/1001-7313.20190603.

Lou Xiaofeng, Fu Yu, Sun Jing. A numerical seeding simulation of convective precipitation in Zhejiang, China. J Appl Meteor Sci, 2019, 30(6): 665-676. DOI: 10.11898/1001-7313.20190603.

Citation:

Lou Xiaofeng, Fu Yu, Sun Jing. A numerical seeding simulation of convective precipitation in Zhejiang, China. J Appl Meteor Sci, 2019, 30(6): 665-676. DOI: 10.11898/1001-7313.20190603.

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A Numerical Seeding Simulation of Convective Precipitation in Zhejiang, China

DOI: 10.11898/1001-7313.20190603

Lou Xiaofeng^{1,2
,
,},
Fu Yu³,
Sun Jing¹

1.
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081
2.
Key Laboratory for Cloud Physics of China Meteorological Administration, Beijing 100081
3.
Dalian Weather Modification Office of Liaoning Province, Dalian 116001

Received Date: 2019-07-15
Rev Recd Date: 2019-10-30
Publish Date: 2019-11-30

Abstract

Abstract

To change precipitation amount and distribution through artificial cloud seeding is one target of weather modification, especially for some important events. Cloud numerical simulations are important ways in research of weather modification activities. A 3-D convective model is used to do simulation for a convective rainfall case in Zhejiang on 1 September 2016. The 3-D convective model calculates 27 microphysical processes, which includes condensation, deposition, evaporation, collection, ice nucleation, ice multiplication, melting and freezing, auto conversion of cloud to rain, ice to graupel and graupel to hail. AgI seeding parameterization is based on cloud chamber results of ice forming processes by AgI which can be identified as deposition, contact freezing, condensation freezing and immersion freezing nucleation. Salt seeding scheme considers the micro-physical process between the salt particle and liquid and ice particles. Using the salt powder and AgI seeding scheme, a series of seeding simulations are designed with salt powder seeding, AgI seeding, and both of them, on seeding height levels, seeding rates, starting seeding times and the size of salt powder, to simulate seeding effects of warm cloud seeding, cold cloud seeding, and mixed cloud seeding schemes.Results show that salt powder seeding is mainly manifested by seeding effects at first rain-increasing then rain-reducing. The seeding mechanism is characterized by salt-dissolved droplets growth through colliding with cloud droplets, collected by raindrops, both of which fall to ground to increase precipitation. The rain enhancement effect is better when seeding in the ascending flow region with 12.5/L of salt powder amount of 30 μm particle size, the precipitation can be increased by 17.8%. AgI seeding is carried out, which basically shows an effect of increasing rainfall after rain reduction. The more silver iodide seeded, the greater the amount of rain reduction will be. For different seeding effects of salt powder and AgI, seeding effects are influenced by their amount of these two seeding agents. With 12.5/L of salt powder of 30 μm particle size, along with 100/L AgI agent, the precipitation can be increased by 19%. These results can be used to guide the field seeding experiment of weather modification with hygroscopic seeding agent and glycogenic seeding agent.
- convective cloud model;
- salt powder seeding;
- AgI seeding;
- combined seeding

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References(43)

Figures and Tables

图 1 2016年9月1日模拟回波垂直剖面图(黑线表示温度，单位：℃)

Fig. 1 Vertical section of simulated echo on 1 Sep 2016 (black line denotes temperature, unit:℃)

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图 2 模拟的流场、温度和云中水成物垂直分布(a)模拟第15分钟的云水(红线，单位：g·kg^-1)、流场(白色箭头)、垂直速度(填色)、温度(黑线，单位：℃), (b)模拟第15 min的冰晶(蓝线，单位：g·kg^-1)、雨水(绿线，单位：g·kg^-1)、温度(黑线，单位：℃), (c)模拟第40分钟的云水(红线，单位：g·kg^-1)、流场(白色箭头)、垂直速度(填色)、温度(黑线，单位：℃), (d)模拟第40分钟的冰晶(蓝线，单位：g·kg^-1)、雨水(绿线，单位：g·kg^-1)、霰(填色)、温度(黑线，单位：℃)

Fig. 2 Vertical subsections of simulated vector, temperature and water substances (a)cloud water content (red line, unit:g·kg^-1), vector (white arrow), vertical speed (the shaded), temperature (black line, unit:℃) at the 15th minute, (b)ice (the blue line, unit:g·kg^-1), rain water (green line, unit:g·kg^-1), temperature (black line, unit:℃) at the 15th minute, (c)cloud water content (red line, unit:g·kg^-1), vector (white arrow), vertical speed (the shaded), temperature (black line, unit:℃) at the 40th minute, (d)ice (blue line, unit:g·kg^-1), rain water (green line, unit:g·kg^-1), graupel (the shaded), temperature (black line, unit:℃) at the 40th minute

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图 3 雨水的源项(a)和降雨量(b)随时间分布

Fig. 3 Time series of rainwater source terms(a) and precipitation(b)

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图 4 催化部位和拟降水分布(a)模拟区域(黑色方框)和模拟云水(红线，单位：g·kg^-1)、垂直速度(填色)、温度(绿线，单位：℃)分布，(b)不同盐粉粒径和小催化剂量下2 min降雨随时间分布，(c)不同盐粉粒径和大催化剂量下2 min降雨随时间分布

Fig. 4 Seeding location and precipitation distribution (a)seeding area (black box) and subsections of simulated cloud water (red line, unit:g·kg^-1), vertical speed (the shaded), temperature (green line, unit:℃), (b)time series of precipitation within 2 min with different salt particle size and small seeding rates, (c)time series of precipitation within 2 min with different salt particle size and big seeding rates

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图 5 模拟第32分钟时水成物、盐溶滴分布和主要微物理过程(a)模拟云水(红线，单位：g·kg^-1)、雨水(蓝线，单位：g·kg^-1)、霰(绿线，单位：g·kg^-1)、盐溶滴(填色)垂直剖面，(b)模拟云水(红线，单位：g·kg^-1)、雨水(蓝线，单位：g·kg^-1)、霰(绿线，单位：g·kg^-1)、盐溶滴(填色)水平分布，(c)盐粉与其他水成物之间的微物理过程随时间变化

Fig. 5 Subsection of simulated water substances, salt resolved water and main microphysical processes at the 32nd minute (a)the vertical section of simulated cloud water (red line, unit:g·kg^-1), rainwater (blue line, unit:g·kg^-1), graupel (green line, unit:g·kg^-1), salt resolved water (the shaded), (b)the horizontal distribution of simulated cloud water (red line, unit:g·kg^-1), rainwater (blue line, unit:g·kg^-1), graupel (green line, unit:g·kg^-1), salt resolved water (the shaded), (c)time series of microphysical processes between salt resolved drops and other water substances

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图 6 催化部位和模拟降雨分布(a)催化区域(黑色方框)和模拟云水(红线，单位：g·kg^-1)、垂直速度(填色)、温度(绿线，单位：℃)分布，(b)不同催化高度下2 min降雨量随时间分布

Fig. 6 Seeding location and precipitation distribution (a)seeding area (black box) and subsections of simulated cloud water (red line, unit:g·kg^-1), vertical speed (the shaded), temperature (green line, unit:℃), (b)time series of precipitation within 2 min at different seeding level (unit:kt)

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图 7 不同催化开始时间2 min累积降雨量随时间变化

Fig. 7 Precipitation within 2 min with different seeding start time (unit:kt)

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图 8 催化部位和模拟降雨分布(a)碘化银催化区域(黑色方框)和模拟云水(红线，单位：g·kg^-1)、垂直速度(填色)、温度(绿线，单位：℃)，(b)不同催化剂量下2 min降雨量随时间分布

Fig. 8 Seeding location and precipitation distribution (a)seeding area (black box) and subsections of simulated cloud water (red line, unit:g·kg^-1), vertical speed (the shaded), temperature (green line, unit:℃), (b)time series of precipitation within 2 min with different seeding rates

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图 9 自然云和催化云的云中水成物随时间分布

Fig. 9 Time series of unseeded and seeded water substances

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图 10 催化部位和模拟降雨分布(a)催化区域(黑色方框)和模拟云水(红线，单位：g·kg^-1)、垂直速度(填色)、温度(绿线，单位：℃)，(b)不同催化方案下2 min降雨量随时间分布

Fig. 10 Seeding location and precipitation distribution (a)seeding area (black box) and subsections of simulated cloud water (red line, unit:g·kg^-1), vertical speed (the shaded), temperature (green line, unit:℃), (b)time series of rainfall within 2 min with different seeding schemes

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