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不同微物理参数化方案对我国北方一次大范围暴雪天气过程的数值模拟研究

王淑彩 平凡 孟雪峰 李玉鹏

王淑彩, 平凡, 孟雪峰, 等. 2022. 不同微物理参数化方案对我国北方一次大范围暴雪天气过程的数值模拟研究[J]. 大气科学, 46(3): 1−22 doi: 10.3878/j.issn.1006-9895.2107.21064
引用本文: 王淑彩, 平凡, 孟雪峰, 等. 2022. 不同微物理参数化方案对我国北方一次大范围暴雪天气过程的数值模拟研究[J]. 大气科学, 46(3): 1−22 doi: 10.3878/j.issn.1006-9895.2107.21064
WANG Shucai, PING Fan, MENG Xuefeng, et al. 2022. Numerical Simulation of a Large-Scale Snowstorm Process in Northern China Using Different Cloud Microphysical Parameterization Schemes [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 46(3): 1−22 doi: 10.3878/j.issn.1006-9895.2107.21064
Citation: WANG Shucai, PING Fan, MENG Xuefeng, et al. 2022. Numerical Simulation of a Large-Scale Snowstorm Process in Northern China Using Different Cloud Microphysical Parameterization Schemes [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 46(3): 1−22 doi: 10.3878/j.issn.1006-9895.2107.21064

不同微物理参数化方案对我国北方一次大范围暴雪天气过程的数值模拟研究

doi: 10.3878/j.issn.1006-9895.2107.21064
基金项目: 国家重点研究开发项目2018YFC1506801、2018YFF0300102
详细信息
    作者简介:

    王淑彩,女,1996年出生,硕士研究生,主要从事风云卫星资料质控研究。E-mail: 1652010637@qq.com

    通讯作者:

    平凡,E-mail: pingf@mail.iap.ac.cn

  • 中图分类号: P435

Numerical Simulation of a Large-Scale Snowstorm Process in Northern China Using Different Cloud Microphysical Parameterization Schemes

Funds: The National Key Research and Development Program of China (Grants 2018YFC1506801, 2018YFF0300102)
  • 摘要: 本文以ERA5(ECMWF Reanalysis v5)再分析资料为初始场,利用WRF(The Weather Research and Forecasting)模式对2020年4月19~20日的一次大范围暴雪天气过程进行数值模拟研究。模式采用不同云微物理参数化方案进行敏感性试验,并与实测数据(自动站降水数据、雷达基数据)进行对比,分析了此次暴雪天气过程不同阶段的降水、雷达反射率、动热力和水凝物的时空演变和三维细致结构特征。研究表明:Morrison方案更好的模拟出了本次暴雪天气过程,表现在模拟的雷达回波强度、范围及形态更与实况一致,模拟出的降水量的相关系数和均方根误差都优于其他方案;其微物理细致结构表现为强上升运动和低层正涡度的长时间维持,以及7 km以上高层较多的冰晶、中低层较少的霰粒子和雨水粒子。从热动力场上看,bin(SBM fast)方案在600 hPa高度以下存在明显的涡度波列,这主要是因为bin方案将粒子群分档处理,没有捆绑不同粒子类型运动,更能细致描述出不同粒子的下沉拖曳作用。从云微物理特征上看,不同方案模拟的雪、霰、云水以及雨水粒子的比质量都较为接近,而对冰晶比质量的模拟不管在量级还是在分布范围上都存在很大的差异,这种差异决定了不同微物理方案模拟的雷达回波和降水量级和相态的差异。
  • 图  1  2020年4月19日12时至20日12时(协调世界时,下同),24小时累计降水量实况分布(单位:mm)。散点图的颜色代表降水量的量级,红色框为本次试验主要关注的区域

    Figure  1.  Distribution of the 24-h accumulative precipitation from 1200 UTC 19 April to 1200 UTC 20 April 2020 (units: mm). The colors of the scatter plot represent the magnitude of precipitation. Red boxes are areas of major concern in this experiment

    图  2  ERA5再分析资料2020年4月19日06时天气形势图:(a)500 hPa位势高度(蓝色等值线,单位:10 gpm)、温度(填色,单位:K)、风场(箭矢,单位:m s−1)和急流(绿色阴影,风速大于20 m s−1);(b)850 hPa位势高度(蓝色等值线,单位:10 gpm)、风场(箭矢,单位m s−1)和比湿(填色,单位:g kg−1);(c)地面海平面平均气压(蓝色等值线,单位:hPa)、风场(箭矢,单位:m s−1)、温度(色阶,单位:K)和温度露点差(绿色阴影,<2 :单位:K)

    Figure  2.  (a) Geopotential height (blue contour, units: 10 gpm), temperature (shaded, units: K), wind (vector, units: m s−1), and wind jet (green shadow, wind speed over 20 m s−1) at 500 hPa; (b) geopotential height (blue contour, units: 10 gpm), wind (vector, units: m s−1), specific humidity (shaded, units: g kg−1) at 850 hPa; (c) mean sea level pressure (blue contour, units: hPa), wind (vector, units: m s−1), temperature (shaded, units: K) and temperature dew point difference (the green shadow is less than 2, units: K) on 1200 UTC 19 April 2020 based on the ERA5 reanalysis data

    图  3  模拟区域

    Figure  3.  Model domain for the numerical simulation

    图  4  2022年4月19日(a1)15:00、(a2)17:00、(a3)20:00实测和(b1、c1、d1、e1、f1)14:00、(b2、c2、d2、e2、f2)16:00、(b3、c3、d3、e3、f3)21:00(b1–b3)Thompson方案、(c1–c3)Morrison方案、(d1–d3)WDM6方案、(e1–e3)NSSL方案及(f1–f3)bin方案模拟的雷达组合反射率(单位:dBZ)。红色对角线和方框分别表示下文剖面位置和区域平均的范围

    Figure  4.  Observed (a1) 1500 UTC, (a2) 1700 UTC, (a3) 2000 UTC and simulated radar composite reflectivity (units: dBZ); (b1) 1400 UTC 19 April 2020, (b2) 1600 UTC 19 April 2020, (b3) 2100 UTC 19 April 2020) correspond to Thompson (the time of other schemes is the same below), (c1, c2, c3) is Morrison, (d1, d2, d3) is WDM6, (e1, e2, e3) is NSSL, and (f1, f2, f3) is bin. Red diagonal lines and red boxes indicate the slice lines of cross-sections and the averaging area in the following analysis, respectively

    图  5  实测和微物理参数化方案模拟的雷达反射率垂直剖面(单位:dBZ)和风场(箭矢:单位:m s−1)(其垂直速度风矢扩大了50倍,下同),时间和剖面的位置如图4所示

    Figure  5.  Observed and simulated vertical cross-section of the radar composite reflectivity (units: dBZ) and winds (vector, units: m s−1; vertical vector speed is magnified by 50, the same below). The analysis time and locations of cross-sections are denoted in Fig. 4

    图  6  观测和模拟的6小时累计降水量(单位:mm):(a1、b1、c1、d1、e1、f1)2020年4月19日10:00~16:00;(a2、b2、c2、d2、e2、f2)2020年4月19日20:00至20日02:00;(a3、b3、c3、d3、e3、f3)2020年4月20日06:00~12:00

    Figure  6.  Observed and simulated of the 6-h accumulated rainfall (units: mm) during (a1, b1, c1, d1, e1, f1) 1000–1600 UTC 19, (a2, b2, c2, d2, e2, f2) 2000 UTC 19–0200 UTC 20, and (a3, b3, c3, d3, e3, f3) 0600 UTC–1200 UTC 20

    图  7  2020年4月19日06时至20日06时实况与不同微物理参数化模拟的24小时累计降水量(单位:mm)

    Figure  7.  Observed and simulated of the 24 h accumulated rainfall during 0600 UTC 19 April 2020–0600 UTC 20 April 2020 for simulation (units: mm).

    图  8  东北258个地面自动站与不同微物理参数化模拟的平均1小时累计降水量时间演变(单位:mm)

    Figure  8.  Temporal evolution of the average 1-hour accumulated rainfall at 258 automatic ground stations in Northeast China simulated by different microphysical parameterizations (units: mm)

    图  9  五种方案模拟的垂直速度 (填色,单位:m s−1)、风场(箭矢,单位:m s−1),时间和剖面位置同图4

    Figure  9.  Vertical cross-sections of the vertical velocity (shaded, units: m s−1) and winds (vectors, units: m s−1) simulated by different microphysical parameterizations. The analysis time and locations of cross-sections are denoted in Fig. 4

    图  10  微物理参数化方案模拟的涡度场(填色,单位:s-1)和风场(箭矢,单位:m s-1)垂直剖面,时间和剖面位置同图4

    Figure  10.  Vertical cross-sections of the vorticity field (shaded, units: s−1) and wind (vector, units: m s-1) simulation by different microphysical parameterizations. The analysis time and the locations of cross-sections are denoted in Fig. 4

    图  11  不同方案模拟的扰动位温(填色,单位:K)和总水凝物混合比(等值线,单位:g kg−1)的垂直剖面,红色实线为0°C等温线,时间和剖面位置如图4

    Figure  11.  Vertical cross-sections of the potential temperature perturbation (shaded, units: K) and the total mixing ratio (contours, units: g kg−1). Red solid lines represent the 0°C isotherms. The analysis time and locations of cross-sections are denoted in Fig. 4

    图  12  不同微物理参数化方案模拟的(a1、a2)最大最小垂直速度、(b1、b2)涡度和(c)平均扰动位温时间演变

    Figure  12.  Temporal evolution of the (a1, a2) max and min vertical velocity, (b1, b2) vorticity, and (c) potential temperature (PT) perturbation simulated by different microphysical parameterizations.

    图  13  区域平均的水凝物混合比垂直廓线(单位:g kg−1):(a1、a2、a3)冰晶;(b1、b2、b3)雪;(c1、c2、c3)霰;(d1、d2、d3)云水;(e1、e2、e3)雨水,时间和位置同图4

    Figure  13.  Vertical profiles of the regionally average mixing ratio (units: g kg−1) of (a1, a2, a3) ice, (b1, b2, b3) snow, (c1, c2, c3) graupel, (d1, d2, d3) cloud water, and (e1, e2, e3) rainwater. The analysis time and the locations of cross-sections are denoted in Fig. 4

    图  14  区域平均的水凝物数浓度垂直廓线(单位:105 kg−1):(a1、a2、a3)冰晶;(b1、b2、b3)雪;(c1、c2、c3)霰;(d1、d2、d3)云水;(e1、e2、e3)雨水。时间和位置如图4

    Figure  14.  Vertical profiles of the regionally average number concentration of (a1, a2, a3) ice, (b1, b2, b3) snow, (c1, c2, c3) graupel, (d1, d2, d3) cloud water, and (e1, e2, e3) rainwater (units: 105 kg-1). The analysis time and locations of cross-sections are denoted in Fig. 4

    图  15  不同微物理参数化方案模拟的区域平均水凝物混合比时间—高度剖面分布:(a1、b1、c1、d1、e1)云水(qc,填色)和冰晶(qi,等值线);(a2、b2、c2、d2、e2)雨水(qr,填色)和雪粒子(qs,等值线);(a3、b3、c3、d3、e3)霰粒子(qg,等值线),(单位:g kg-1,红色实线为0°C线)

    Figure  15.  Time–height cross-sections of the (a1, b1, c1, d1, e1) regionally average mixing ratio of cloud water (qc, shaded) and cloud ice (qi, contours), (a2, b2, c2, d2, e2) rainwater (qr, shaded) and snow (qs, contours), (a3, b3, c3, d3, e3) graupel (qg, contours) of different microphysical parameterization schemes (units: g kg−1, red solid lines represent the 0°C isotherms)

    表  1  模式设计

    Table  1.   Mode parameterization scheme setting

    方案模式设置
    水平分辨率3 km
    水平格点数500×530
    中心经纬度40°N, 116°E
    垂直层数51
    模式顶气压50 hPa
    积云参数化方案/
    边界层方案YSU
    路面过程方案Noah
    长波辐射方案RRTM
    短波辐射方案Dudhia
    云微物理方案Thompson(8)、Morrison(10)、WDM6(16)、
    NSSL(18)、SBM fast (bin, 30)
    下载: 导出CSV

    表  2  微物理参数化方案的混合比和数浓度

    Table  2.   Mixing ratio and number concentration of the five microphysical parameterization schemes

    微物理
    方案编号
    方案名称混合比数浓度
    8ThompsonQc Qr Qi Qs QgNi Nr
    10Morrison 2-momQc Qr Qi Qs QgNr Ni Ns Ng
    16WDM6Qc Qr Qi Qs QgNn Nc Nr
    18NSSL 2-mom+CCNQc Qr Qi Qs Qg QhNr Ni Ns Ng Nh Nn Vg
    30HUJI fast(bin)Qc Qr Qs Qg QiNc Nr Ns Ni Ng Nn
    下载: 导出CSV

    表  3  1小时累计降水量统计评估

    Table  3.   One-hour accumulated rainfall statistical assessment

    方案ThompsonMorrisonWDM6NSSLbin
    相关系数(CORR)0.95770.95910.95100.95670.9410
    均方根误差(RMSE/mm)0.06400.06850.07490.06750.0816
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-04-14
  • 录用日期:  2021-08-13
  • 网络出版日期:  2021-09-09

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