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雷达资料同化对一次飑线过程的模拟影响

邹玮 沈晗 袁慧玲

邹玮, 沈晗, 袁慧玲. 2022. 雷达资料同化对一次飑线过程的模拟影响[J]. 大气科学, 46(6): 1281−1299 doi: 10.3878/j.issn.1006-9895.2105.20191
引用本文: 邹玮, 沈晗, 袁慧玲. 2022. 雷达资料同化对一次飑线过程的模拟影响[J]. 大气科学, 46(6): 1281−1299 doi: 10.3878/j.issn.1006-9895.2105.20191
ZOU Wei, SHEN Han, YUAN Huiling. 2022. Simulation Impact of Radar Data Assimilation on a Squall Line Process [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 46(6): 1281−1299 doi: 10.3878/j.issn.1006-9895.2105.20191
Citation: ZOU Wei, SHEN Han, YUAN Huiling. 2022. Simulation Impact of Radar Data Assimilation on a Squall Line Process [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 46(6): 1281−1299 doi: 10.3878/j.issn.1006-9895.2105.20191

雷达资料同化对一次飑线过程的模拟影响

doi: 10.3878/j.issn.1006-9895.2105.20191
基金项目: 国家重点研发计划项目2018YFC1507405、2017YFC1502703
详细信息
    作者简介:

    邹玮,女,1996年出生,硕士研究生,主要从事灾害性天气预报及资料同化方面研究。E-mail: were_mercy@163.com

    通讯作者:

    袁慧玲,E-mail: yuanhl@nju.edu.cn

  • 中图分类号: P456.7

Simulation Impact of Radar Data Assimilation on a Squall Line Process

Funds: National Key Research and Development Program (Grants 2018YFC1507405, 2017YFC1502703)
  • 摘要: 雷达资料同化能够改善强对流天气的预报,但是不同的模式方案配置会得到不同的结果。本文针对中国南部2018年3月4日一次飑线过程,以全球预报模式GFS分析场和预报场为背景场,采用中尺度区域气象预报模式ARPS 3DVAR系统同化多普勒雷达径向速度,用云分析处理反射率数据,考虑同化间隔、频次、云分析中不同参数调整,采用1 h同化窗口,设计不同同化方案,最后用WRF模式进行预报,研究雷达资料同化对飑线系统触发及发展机制的影响。结果表明,同化间隔过短时,由于模式热动力变量没有平衡产生虚假回波,同化间隔过长时,系统触发和发展的特征普遍偏弱;采用12 min间隔同化得到了最好的初始场,并且同化频次越高得到的降水预报结果越好。此外,ARPS云分析能大大改善初始场,减少模式自调整时间,其中湿度调整、温度调整、雨水调整及水汽调整对系统动力过程和水凝物初始场分布都有较大的影响,而垂直速度相关参数调整影响较小。
  • 图  1  2018年3月4日17时(协调世界时,下同)中国南部13部新一代天气雷达组合反射率因子(单位:dBZ)。红色实线表示垂直于飑线移动方向的垂直剖面位置

    Figure  1.  Composite radar reflectivity (units: dBZ) of 13 new-generation weather radars in southern China at 1700 UTC 4 March 2018. The red solid line indicates a cross section perpendicular to the moving direction of the squall line

    图  2  2018年3月4日12时GFS分析场(a)500 hPa、(b)850 hPa风场(风向杆,单位:m s−1)和位势高度(填色,单位:gpm),(c)850 hPa水汽通量散度(填色,单位:g s−1 cm−2 hPa−1,负值表示水汽辐合)、水汽通量(箭头,单位:g s−1 cm−1 hPa−1),(d)地面对流有效位能(单位:J kg−1

    Figure  2.  Wind (barbs, units: m s−1) and geopotential height (shadings, units: gpm) at (a) 500 hPa and (b) 850 hPa, (c) moisture flux divergence (shadings, units: g s−1 cm−2 hPa−1, negative values represent horizontal mass convergence) and its corresponding moisture flux (arrows, units: g s−1 cm−1 hPa−1), (d) convective available potential energy (CAPE) (units: J kg−1) at the surface from the GFS (Global Forecast System) analysis at 1200 UTC 4 March 2018

    图  3  模式区域设置。填色表示地形高度(单位:m)

    Figure  3.  Model domain configuration. The shadings represent the elevation (units: m)

    图  4  同化试验流程图

    Figure  4.  Flowchart of data assimilation experiments

    图  5  2018年3月4日12时组合反射率(单位:dBZ):(a)中国南部13部新一代多普勒天气雷达;(b)CNTL试验;(c)Exp1Vr试验;(d)Exp1Z试验;(e)Exp1All试验

    Figure  5.  Composite reflectivity (units: dBZ) at 1200 UTC 4 March 2018: (a) 13 new generation weather radars in southern China; (b) CNTL experiment; (c) Exp1Vr experiment; (d) Exp1Z experiment; (e) Exp1All experiment

    图  6  2018年3月4日19时扰动位温(填色,单位:K)、组合反射率(黑色等值线,单位:dBZ)及水平风场的垂直切变(风羽,单位:m s−1)沿(27°N,114°E)–(25°N,116°E)的垂直剖面分布:(a)CNTL试验;(b)Exp1Vr试验;(c)Exp1Z试验;(d)Exp1All试验

    Figure  6.  Cross sections of temperature perturbation (shadings, units: K), composite reflectivity (black contours, units: dBZ), and vertical wind shear (barbs, units: m s−1) of horizontal wind field along (27°N, 114°E)–(25°N, 116°E) at 1900 UTC 4 March 2018: (a) CNTL experiment; (b) Exp1Vr experiment; (c) Exp1Z experiment; (d) Exp1All experiment

    图  7  2018年3月4日12时至3月5日06时第一组试验(表1)不同阈值下每小时累积降水量ETS评分:(a)0.1 mm;(b)2.5 mm;(c)8 mm;(d)16 mm

    Figure  7.  ETS scores of predicted hourly accumulated rainfall at the thresholds of (a) 0.1 mm, (b) 2.5 mm, (c) 8 mm, and (d) 16 mm for the first group of experiments (Table 1) from 1200 UTC 4 March to 0600 UTC 5 March 2018

    图  8  2018年3月4日15时组合反射率(单位:dBZ):(a)中国南部13部新一代多普勒天气雷达;(b)Exp2All1h2t试验;(c)Exp2All30m3t试验;(d)Exp2All12m6t试验;(e)ExpSPstart试验

    Figure  8.  Composite reflectivity (units: dBZ) at 1500 UTC 4 March 2018: (a) 13 new generation weather radars in southern China; (b) Exp2All1h2t experiment; (c) Exp2All30m3t experiment; (d) Exp2All12m6t experiment; (e) ExpSPstart experiment

    图  9  2018年3月4日16时不同同化间隔下绝对涡度(填色,单位:10−5 s−1)及p坐标系下垂直速度(黑色等值线,单位:Pa s−1)沿图1中红色线段(27°N,114°E)–(25°N,116°E)的垂直剖面分布:(a)1 h;(b)30 min;(c)12 min;(d)6 min

    Figure  9.  Cross sections of absolute vorticity (shadings, units: 10−5 s−1) and vertical velocity (black contours, units: Pa s−1) on the p coordinate with different assimilation intervals along red line (27°N, 114°E)–(25°N, 116°E) in Fig. 1 at 1600 UTC 4 March 2018: (a) 1 h; (b) 30 min; (c) 12 min; (d) 6 min

    图  10  图9,但为相当位温(填色,单位:K)、相对湿度(黑色等值线,单位:%)的垂直剖面分布

    Figure  10.  As in Fig. 9, but for cross sections of equivalent potential temperature (shadings, units: K) and relative humidity (black contours, units: %)

    图  11  2018年3月4日第二组试验(表1)初始1 h预报时段(15~16时)850 hPa每分钟最大垂直速度(单位:m s−1)的变化

    Figure  11.  Variation of the maximum vertical velocity (units: m s−1) per minute at 850 hPa during the initial 1 h forecast period (from 1500 UTC to 1600 UTC) for the second group of experiments (Table 1) on 4 March 2018

    图  12  2018年3月4日15时至3月5日06时第三组试验(表1)不同阈值下每小时累积降水量ETS评分:(a)0.1 mm;(b)2.5 mm;(c)8 mm;(d)16 mm

    Figure  12.  ETS scores of predicted hourly accumulated rainfall at the thresholds of (a) 0.1 mm, (b) 2.5 mm, (c) 8 mm, and (d) 16 mm for the third group of experiments (Table 1) from 1500 UTC 4 March to 0600 UTC 5 March 2018

    图  13  图12,但为每小时累积降水量的FSS评分

    Figure  13.  As in Fig. 12, but for FSS scores of predicted hourly accumulated rainfall

    图  14  2018年3月4日15时Exp2All12m6t试验(a)初始参数配置、(b–k)调整参数后云水混合比(单位:g kg−1)沿图1中红色线段(27°N,114°E)–(25°N,116°E)的垂直剖面分布:(b)温度调整方案Ptopt=0;(c)温度调整方案Ptopt=3;(d)水汽调整方案Qcopt=0;(e)湿度调整方案Qvopt=0;(f)雨水调整方案Qropt=0;(g)雨水调整方案Qropt=1;(h)水成物最大输出混合比Qrlimit=0.0005;(i)雨水/冰向云水的转换比例Frac_qr_2_qc=0.4;(j)垂直速度调整方案Wopt=0;(k)积云内最大垂直速度Wmhr_Cu=0.005

    Figure  14.  Cross sections of the cloud water mixing ratio (units: g kg−1) in (a) the initial parameter configuration, (b–k) after adjusting the parameters of Exp2All12m6t experiment along red line (27°N, 114°E)–(25°N, 116°E) in Fig. 1 at 1500 UTC 4 March 2018: (b) Temperature option Ptopt = 0; (c) temperature option Ptopt = 3; (d) water vapor Qcopt = 0; (e) humidity Qvopt = 0; (f) rainwater Qropt = 0; (g) rainwater Qropt = 1; (h) maximum mixing ratio of hydrometeors Qrlimit = 0.0005; (i) conversion ratio of rain/ice to cloud water Frac_qr_2_qc = 0.4; (j) vertical velocity Wopt = 0; (k) maximum vertical speed in cumulus clouds Wmhr_Cu = 0.005

    图  15  2018年3月4日15时至3月5日06时不同云参数敏感性试验每小时700 hPa最大垂直速度(单位:m s−1

    Figure  15.  Hourly maximum vertical velocity (units: m s−1) at 700 hPa of different sensitivity tests on complex cloud analysis from 1500 UTC 4 March to 0600 UTC 5 March 2018

    表  1  第一组、第二组和第三组同化试验设计

    Table  1.   List of different data assimilation experiments

    试验名称初始同化时刻是否同化Vr是否同化Z间隔/min同化次数
    第一组试验CNTL///
    Exp1All2018年3月4日12时/1
    Exp1Vr2018年3月4日12时/1
    Exp1Z2018年3月4日12时/1
    第二组试验ExpSPstart2018年3月4日15时/1
    Exp2All1h2t2018年3月4日15时602
    Exp2All30m3t2018年3月4日15时303
    Exp2All12m6t2018年3月4日15时126
    第三组试验Exp2All6m11t2018年3月4日15时611
    Exp3All12m2t2018年3月4日15时122
    Exp3All12m4t2018年3月4日15时124
    下载: 导出CSV

    表  2  云分析参数敏感性试验设计

    Table  2.   Design of cloud analysis parameter sensitivity test

    云参数
    试验名称PtoptQcoptQvoptQroptWoptQrlimitWmhr_CuFrac_qr_2_qc
    Exp2All12m6t511210.0050.00050
    Exp4AllPT0011210.0050.00050
    Exp4AllPT3311210.0050.00050
    Exp4AllQC0501210.0050.00050
    Exp4AllQV0510210.0050.00050
    Exp4AllQR0511010.0050.00050
    Exp4AllQR1511110.0050.00050
    Exp4AllW0511200.0050.00050
    Exp4Allqrlmt511210.00050.00050
    Exp4AllwmhrCu511210.0050.0050
    Exp4Allqr2qc511210.0050.00050.4
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-08-07
  • 录用日期:  2022-01-07
  • 网络出版日期:  2022-01-18
  • 刊出日期:  2022-11-24

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