北京地区一次降雪系统大气水凝物输送特征及降雪微物理机制的数值模拟研究

刘香娥; 何晖; 陈羿辰; 高茜; 王永庆; 杨燕

doi:10.3878/j.issn.1006-9895.2110.20212

北京地区一次降雪系统大气水凝物输送特征及降雪微物理机制的数值模拟研究

doi: 10.3878/j.issn.1006-9895.2110.20212

刘香娥^{1, 2, 3, 4,},
何晖^{1, 2, 3, 4, ,},
陈羿辰^{1, 2, 3, 4},
高茜^{1, 2, 3, 4},
王永庆^{1, 2, 3, 4},
杨燕^{1, 2, 3, 4}

1.
北京市人工影响天气中心, 北京100089
2.
云降水物理研究和云水资源开发北京市重点试验室, 北京 100089
3.
中国气象局北京城市气象研究院, 北京 100089
4.
中国气象局华北云降水野外科学试验基地, 北京 101200

基金项目: 国家重点研发计划项目2016YFA0601704，国家自然科学基金项目42005078、41675138，北京市自然科学基金项目8182024

详细信息

作者简介:
刘香娥，女，1982年生，博士，正高级工程师，从事云降水物理与人工影响天气研究。E-mail:lxe3399@163.com

通讯作者:
何晖，E-mail: hehui@bj.cma.gov.cn

中图分类号: P401
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- 被引次数: 0
出版历程
- 收稿日期: 2020-10-15
- 录用日期: 2021-10-19
- 网络出版日期: 2021-11-17
- 刊出日期: 2022-05-19

Numerical Simulation Studies of Atmospheric Hydrometeor Transportation Characteristics and Snowfall Microphysical Mechanism during a Snowfall System in Beijing

Xiang’ e LIU^{1, 2, 3, 4
,},
Hui HE^{1, 2, 3, 4
, ,},
Yichen CHEN^{1, 2, 3, 4},
Qian GAO^{1, 2, 3, 4},
Yongqing WANG^{1, 2, 3, 4},
Yan YANG^{1, 2, 3, 4}

1.
Beijing Weather Modification Center, Beijing 100089
2.
Key laboratory of Beijing for Cloud, Precipitation and atmospheric water resources, Beijing 100089
3.
Institute of Urban Meteorology, China Meteorological Administration, Beijing 100089
4.
Field Experiment Base of Cloud and Precipitation Research in North China, China Meteorological Administration, Beijing 101200

Funds: National Key Research and Development Program of China (Grant 2016YFA0601704), National Natural Science Foundation of China (Grants 42005078, 41675138)，Beijing Natural Science Foundation (Grant 8182024)

摘要

摘要: 北京冬季降雪云系存在丰富的可开发利用的云水资源。出于人工增雪研究和充分开发云水资源的需要，文中对北京2019年11月29日发生的年度首场降雪进行了观测，对其资料做了分析和中尺度数值模拟，研究了降雪过程的宏观特征、水凝物输送及降雪的微物理机制。结果表明：影响本次北京降雪的是稳定性层状冷云云系，水凝物主要从北京区域的西边界和南边界输送到区域内，而从东边界和北边界流出，具有西向和南向分量的湿气流是降雪云系水物质的输送通道。降雪云中的水凝物基本全为冰晶和雪，有少量的云水，整层云系都含有非常丰富的水汽并且贯穿整个降雪时段。在冰面过饱和环境中，水汽凝华（Prds）是雪的主要增长过程；其次是云冰增长成雪（Prci）和云冰聚合成雪（Prai）的过程。
- 云水资源 /
- 冬季降雪 /
- 数值模拟
Abstract: There are abundant cloud water resources for development and utilization in the snowfall cloud system in winter in Beijing. For the needs of artificial snow enhancement research and full development of cloud water resources, the first annual snowfall in Beijing was observed on November 29, 2019. Data are analyzed, and numerical simulation is carried out. The macro-observation characteristics of the snowfall process are studied, and the atmospheric hydrometeor transportation characteristics and microphysical mechanism of the snowfall are also analyzed through the simulation results. The results show that the stable stratified cold cloud system affects the snowfall in Beijing. Water vapor and water condensate are mainly transported into the region from the western and southern boundaries of the Beijing area and flow out from the eastern and northern boundaries. The cloud hydrometeor transport channel for the snowfall cloud system accompanies a westward and southward component of the moist airflow. The water condensate in the snowfall cloud comprises ice crystals, snow, and a small amount of cloud water. The entire layer of the cloud system contains rich water vapor and runs through the entire snowfall period. In an ice-saturated environment, deposition of snow (Prds) are the main source of the snow, followed by the automatic conversion of cloud ice to snow (Prci) and the accretion of cloud ice by snow (Prai).
- Cloud water resources /
- Snowfall in winter /
- Numerical simulation

HTML全文

图 1 2019年11月29日08:00至30日08:00（a）实况和（b）模拟24 h地面降水量（单位：mm）分布。黑点为闫家坪站位置

Figure 1. Distributions of (a) observed and (b) simulated 24-hour cumulative rainfall (units: mm) from 0800 BT (Beijing time) November 29 to 0800 BT November 30, 2019. The black dot is the location of Yanjiaping Station

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图 2 2019年11月29日（a）15:00和（b）22:00 FY-2G卫星云顶亮温图

Figure 2. TBB (Black Body Temperature) images from FY-2G satellite on top of cloud at (a) 1500 BT and (b) 2200 BT November 29, 2019

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图 3 2019年11月29～30日闫家坪站气象要素的时间演变。柱状图：降水量（单位：mm）；蓝线：气温（单位：°C）；绿线：相对湿度

Figure 3. Time evolution of various elements at the Yanjiaping meteorological station. Columnars: precipitation (units: mm); blue line: temperature (units: °C); green line: relative humidity

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图 4 2019年11月29～30日闫家坪站（a）观测和（b）模拟的云雷达回波的时间—高度演变

Figure 4. Time–height evolution of the echo intensity observed by a cloud radar from November 29 to November 30, 2019

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图 5 2019年11月29～30日闫家坪站半小时降水量观测（蓝色柱状图）与模拟（绿色实线）对比

Figure 5. Simulated (green line) and observed (blue columnars) 30-min accumulated precipitation (units: mm) at the Yanjiaping station from November 29 to November 30, 2019

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图 6 2019年11月29日17:00（a）850 hPa、（b）700 hPa水汽通量（填色，单位：g s kg⁻¹）和风场（黑色箭头）分布

Figure 6. Distributions of water vapor flux (shaded) and wind field (black arrows) at (a) 850 hPa and (b) 700 hPa at 1700 BT on November 29, 2019

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图 7 2019年11月29～30日模拟云系两相水凝物总质量时间演变

Figure 7. Time evolution of the total hydrometeor content in two phase in the simulated cloud system from November 29 to November 30, 2019

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图 8 2019年11月30日00:00穿过北京区域（39.3°～41.5°N，115.3°～117.5°E）各边界单位截面积的（a）云水（Q_c）、（b）雪（Q_s）、（c）冰晶（Q_i）、（d）雨水（Q_r）、（e）霰（Q_g）的通量及总量随高度分布以及（f）29～30日总水凝物通量（带标记的线）和总量（虚线）的垂直积分时间演变

Figure 8. Vertical distribution of the fluxes of (a) cloud water (Q_c), (b) snow (Q_s), (c) ice (Q_i), (d) rain (Q_r), (c) graupel (Q_g and the total flux across each boundary of the Beijing region (39.3°–41.5°N, 115.3°–117.5°E) at 0000 BT November 30, 2019. (f) The time series of the vertical integration of the fluxes (VIF; solid lines with symbols) and the vertically integrated flux convergences (VIFC; dashed line) of all condensates from November 29 to November 30, 2019

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图 9 2019年11月29日（a）16:00和（b）23:00北京区域模拟的小时降雨量（单位：mm）分布

Figure 9. Distributions of simulated hourly rainfall (units: mm) in Beijing region at 1600 BT and 2300 BT 29 November 2019

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图 10 2019年11月（a）29日16:00沿40.3°N和（b）30日00:00沿40°N水凝物质量浓度的纬向剖面以及（c）区域（39.3°～41.5°N，115.3°～117.5°E）内水凝物质量浓度总量垂直廓线。（a、b）中彩色阴影：雪；蓝色实线：冰晶，单位：g kg⁻¹；黑色虚线：等温线，单位：°C。（c）中黑实线：雪质量浓度总量；黑虚线：冰晶质量浓度总量，单位：g m⁻³）

Figure 10. Vertical sections of water hydrometeor mixing ratio (a) along 40.3°N at 1600 BT 29 and (b) along 40°N at 0000 BT 30 November 2019, and (c) vertical profiles of area accumulation of the total water content of hydrometeors over the region (39.3°–41.5°N, 115.3°–117.5°E). (a, b) Shaded: snow mixing ratio; blue lines: ice crystal mixing ratio, units: g kg⁻¹; black dotted line: isotherm, units: °C. (c) Black solid line: total snow mixing ratio; black dotted lines: total ice mixing ratio, units: g m⁻³

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图 11 2019年11月29～30日闫家坪站（a）水汽、云水、雨水质量浓度和（b）冰晶、雪、霰质量浓度随时间和高度的分布，单位：g kg⁻¹

Figure 11. Distribution of (a) water vapor, cloud, rain mixing ratio and (b) ice, snow graupel mixing ratio (units: g kg⁻¹) with time and height in Yanjiaping station from November 29 to November 30, 2019

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图 12 2019年11月29～30日闫家坪站（a）冰面过饱和度（彩色阴影）和水汽凝华成雪（Prds，黑线）、雪淞附云滴（Psacws，绿色线）、云冰聚合成雪（Prai，紫色线）过程的转换率以及（b）云冰自动转换为雪（Prci，红色线）、雪晶升华（Eprds，紫色线）过程的转换率随时间和高度的分布。转换率单位：10⁻⁸ kg kg⁻¹ s⁻¹

Figure 12. Distribution of the supersaturation with respect to ice (shaded), (a) conversion rate (units: 10⁻⁸ kg kg⁻¹ s⁻¹) of the deposition of snow (Prds , black line), droplet accretion by snow (Psacws, green line), accretion cloud ice by snow (Prai, purple line) and (b) conversion rate of the auto conversion cloud ice to snow (Prci,red line), sublimation of snow (Eprds, grey line) with time and height in Yanjiaping station from November 29 to November 30, 2019.

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