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华东区域中尺度集合预报系统的改进及2020年梅雨期降水试验

谭燕 黄伟 杨玉华 张旭 陈葆德

谭燕, 黄伟, 杨玉华, 等. 2022. 华东区域中尺度集合预报系统的改进及2020年梅雨期降水试验[J]. 大气科学, 46(6): 1437−1453 doi: 10.3878/j.issn.1006-9895.2203.21097
引用本文: 谭燕, 黄伟, 杨玉华, 等. 2022. 华东区域中尺度集合预报系统的改进及2020年梅雨期降水试验[J]. 大气科学, 46(6): 1437−1453 doi: 10.3878/j.issn.1006-9895.2203.21097
TAN Yan, HUANG Wei, YANG Yuhua, et al. 2022. Improvement in the Mesoscale Ensemble Forecast System in East China and A Precipitation Experiment in the 2020 Meiyu Season [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 46(6): 1437−1453 doi: 10.3878/j.issn.1006-9895.2203.21097
Citation: TAN Yan, HUANG Wei, YANG Yuhua, et al. 2022. Improvement in the Mesoscale Ensemble Forecast System in East China and A Precipitation Experiment in the 2020 Meiyu Season [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 46(6): 1437−1453 doi: 10.3878/j.issn.1006-9895.2203.21097

华东区域中尺度集合预报系统的改进及2020年梅雨期降水试验

doi: 10.3878/j.issn.1006-9895.2203.21097
基金项目: 国家重点研发计划项目2017YFC1501902,上海市科委项目19DZ1201500,国家自然科学基金项目41975133,华东区域气象科技协同创新基金项目QYHZ201801
详细信息
    作者简介:

    谭燕,女,1980年出生,副研究员,主要从事集合预报研究。E-mail: tany@typhoon.org.cn

    通讯作者:

    黄伟,E-mail: huangw@typhoon.org.cn

  • 中图分类号: P456.7

Improvement in the Mesoscale Ensemble Forecast System in East China and A Precipitation Experiment in the 2020 Meiyu Season

Funds: National Key Research and Development Program of China (Grant 2017YFC1501902), Social Development Projects of STCSM (Grant 19DZ1201500),National Natural Science Foundation of China (Grant 41975133), East China Regional Meteorological Science and Technology Collaborative Innovation Fund (Grant QYHZ201801)
  • 摘要: 考虑区域模式预报中不确定性的各种来源,分别引入初始场误差、侧边界误差和模式误差构建新一代华东区域中尺度集合预报系统,并对2020年梅雨期降水开展为期一个月的集合预报试验。通过不同时空尺度典型个例的分析可以看出,所选取的随机物理倾向扰动方案中的参数具备一定的通用性,且在参数调优中加强随机过程的影响,系统中低层的风场和湿度场有明显的反馈,集合系统的离散度得到较大改善,对预报的影响大小依次为:格点方差、随机扰动场的去相关空间和随机扰动场的去相关时间。一个月的梅雨期降水评估结果显示:集合系统升级后对各时次各量级的降水TS(Threat Score)评分均有所提升,但仍然存在着降水强度偏大的问题;从概率预报的角度来看,系统升级后,对中到大雨预报的准确率和可信度提升明显,对强降水事件的描述更准确;形势场的检验结果表明,系统的预报偏差问题得到了部分程度地改善,对大气中低层风场、湿度场和地面变量的预报效果较好。相比原华东区域中尺度集合预报系统,升级后的系统,其整体优势可概括为:预报误差减小、集合离散度明显增加,降水预报的能力在各时段各量级均有提升,其中物理过程的不确定性对于捕捉强降水事件有明显的影响,使得系统的预报可信度增加。
  • 图  1  第一代中尺度集合预报系统(SWARMS-ENV1,简称V1,黑虚线)与扩展区域后第二代中尺度集合预报系统(SWARMS-ENV2,简称V2)的预报范围

    Figure  1.  Horizontal domain of SWARMS-ENV1 (V1) model (black and dashed) and the expanded domain of SWARMS-ENV2 model (V2)

    图  2  2019年11月23~25日冬季个例(左列)和2020年6月15~17日夏季个例(右列)的各组敏感试验(a、b)850 hPa温度、(c、d)850 hPa纬向风速和(e、f)2 m温度离群值随时间的变化

    Figure  2.  Time series of the outlier of sensitive experiments for (a, b) 850-hPa temperature, (c, d) 850-hPa zonal wind, and (e, f) 2-m temperature for the winter case from 23 to 25 November 2019 (left column) and for the summer case from 15 to 17 June 2020 (right column)

    图  3  图2,为连续分级概率评分CRPS随时间变化

    Figure  3.  Same as Fig.2, but for CRPS (Continuous Ranked Probability Score)

    图  4  2020年6月7日至7月7日(a)0~24 h、(b)24~48 h、(c)48~72 h、(d)72~96 h和(e)96~120 h累计降水的TS评分箱线图(横坐标为降水量级,蓝色为V1,红色为V2)

    Figure  4.  Boxplot of Treat Score for 24-h accumulated precipitation from June 7 to July 7, 2020: (a) 0–24 h; (b) 24–48 h; (c) 48–72 h; (d) 72–96 h; (e) 96–120 h. The abscissa is the precipitation level, blue is V1, and red is V2

    图  5  图4,但为Bias评分

    Figure  5.  Same as Fig. 4, but for Bias score

    图  6  2020年6月7日至7月7日24 h累计降水的BSS(Brier Skill Score)评分。横坐标为降水量级,蓝色为V1,红色为V2;图中f120、f96、f72、f48、f24表示预报时效分别为120 h、96 h、72 h、48 h以及24 h

    Figure  6.  BSS (Brier Skill Score) for 24-h accumulated precipitation from June 7 to July 7, 2020. The abscissa is the precipitation level, blue is V1, and red is V2; f120, f96, f72, f48, f24 mean 120-h, 96-h, 72-h, 48-h, and 24-h forecast

    图  7  2020年6月7日至7月7日新旧系统120小时预报时效(a)0.1 mm、(b)10.0 mm和(c)25.0 mm不同降水量级24 h累计降水的相对作用特征(ROC)曲线(蓝色为V1,红色为V2)

    Figure  7.  ROC (Relative Operating Characteristic) diagram for 24-h accumulated precipitation of (a) 0.1 mm, (b) 10.0 mm, and (c) 25.0 mm for 120-h forecast period from June 7 to July 7, 2020. Blue is V1, and red is V2

    图  8  2020年6月7日至7月7日新旧系统预报的(a)500 hPa位势高度、(b)700 hPa相对湿度、(c)850 hPa温度、(d)850 hPa纬向风、(e)2 m温度和(f)10 m纬向风集合平均均方根误差(RMSE)与集合离散度(SPD)随时间变化

    Figure  8.  Time series of the RMSE (root mean square error) and the ensemble mean and the ensemble spread (SPD) from June 7 to July 7, 2020: (a) 500-hPa geopotential height; (b) 700-hPa relative humidity; (c) 850-hPa temperature; (d) 850-hPa zonal wind ; (e) 2-m temperature; (f) 10-m zonal wind. blue is V1, and red is V2

    图  9  2020年6月7日至7月7日新旧系统预报的120 h的(a)500 hPa位势高度、(b)700 hPa相对湿度、(c)850 hPa温度、(d)850 hPa纬向风、(e)2 m温度和(f)10 m纬向风的Talagrand分布

    Figure  9.  Talagrand diagram for (a) 500-hPa geopotential height; (b) 700-hPa relative humidity; (c) 850-hPa temperature; (d) 850-hPa zonal wind; (e) 2-m temperature and (f) 10-m zonal wind for 96–120-h forecast period from June 7 to July 7, 2020. Blue is V1, and red is V2

    图  10  图8,为CRPS

    Figure  10.  Same as Fig.8, but for CRPS

    图  11  (a)2020年6月15日00:00至16日00:00 24 h累计降水量(单位:mm)分布;2020年6月13日00:00起报(b)V1、(c)V2系统预报大于50 mm的降水概率分布

    Figure  11.  (a) Distributions of 24-h accumulated precipitation (units: mm) from 0000 UTC 15 to 0000 UTC 16 June 2020, and the probability more than 50 mm of (b) V1 and (c) V2 for 96–120-h forecast period

    表  1  华东区域中尺度集合预报系统

    Table  1.   The setup of regional ensemble forecast system

    版本分辨率格点数
    (纬向×经向)
    预报
    时效
    成员数控制预报初值控制预报侧边界初值扰动侧边界扰动模式扰动
    V115 km121×121120 h21ADAS分析场GFS
    预报场
    增长模繁殖法多物理参数化组合
    V29 km760×600120 h21ADAS分析场GEFS 控制预报的
    预报场
    动力降尺度NCEP
    全球集合预报初值
    NCEP全球集合
    预报驱动
    随机物理倾向
    扰动方案
    下载: 导出CSV

    表  2  随机物理倾向扰动方案(SPPT)的优化试验

    Table  2.   Sensitive experiment of stochastic parameters

    试验名称扰动方案
    格点方差去相关时间/s去相关空间/km
    REF0.521600150
    A250.2521600150
    A750.7521600150
    T3h0.510800150
    T9h0.543200150
    L750.52160075
    L3000.521600300
    PHY0.7543200300
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-06-10
  • 录用日期:  2022-06-22
  • 网络出版日期:  2022-06-24
  • 刊出日期:  2022-11-24

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