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2020年梅汛期强降水事件的预报误差来源分析

林琳 卢楚翰 周菲凡

林琳, 卢楚翰, 周菲凡. 2022. 2020年梅汛期强降水事件的预报误差来源分析[J]. 气候与环境研究, 27(4): 469−479 doi: 10.3878/j.issn.1006-9585.2021.21144
引用本文: 林琳, 卢楚翰, 周菲凡. 2022. 2020年梅汛期强降水事件的预报误差来源分析[J]. 气候与环境研究, 27(4): 469−479 doi: 10.3878/j.issn.1006-9585.2021.21144
LIN Lin, LU Chuhan, ZHOU Feifan. 2022. Forecast Error Source Analysis of Heavy Rainfall Events in the Meiyu period of 2020 [J]. Climatic and Environmental Research (in Chinese), 27 (4): 469−479 doi: 10.3878/j.issn.1006-9585.2021.21144
Citation: LIN Lin, LU Chuhan, ZHOU Feifan. 2022. Forecast Error Source Analysis of Heavy Rainfall Events in the Meiyu period of 2020 [J]. Climatic and Environmental Research (in Chinese), 27 (4): 469−479 doi: 10.3878/j.issn.1006-9585.2021.21144

2020年梅汛期强降水事件的预报误差来源分析

doi: 10.3878/j.issn.1006-9585.2021.21144
基金项目: 国家重点研发计划项目 2018YFC1507405、2017YFC1501601
详细信息
    作者简介:

    林琳,女,1996年出生,博士研究生,主要从事数值模拟及海气相互作用方面研究。E-mail:20211101017@nuist.edu.cn

    通讯作者:

    周菲凡,E-mail: zhouff04@163.com

  • 中图分类号: P457

Forecast Error Source Analysis of Heavy Rainfall Events in the Meiyu period of 2020

Funds: National Key Research and Development Program of China (Grants 2018YFC1507405 and 2017YFC1501601)
  • 摘要: 基于WRF模式(Weather Research and Forecasting Model)分析2020年超长梅汛期内11次强降水事件的预报误差来源。分别以FNL(Final Global Data Assimilation System)、TIGGE_EC(THORPEX Interactive Grand Global Ensemble from European Centre for Medium-Range Weather Forecasts)作为初始场进行预报,对比预报结果发现,TIGGE_EC初始场的预报结果普遍优于FNL,这说明初始条件的不确定性对预报结果有重要影响。进一步探究初始条件不确定性(初始误差)来源的区域(敏感区)和变量(敏感变量)发现,敏感区集中分布于降水区西侧上游,相对应的敏感变量为水汽场。分别考察动能、有效位能以及比湿能在初始误差总能量中的占比,结果表明,扰动比湿能占比最小,但敏感性试验 表明比湿场扰动对预报效果的影响最大。选取比湿场扰动对预报效果影响最大且WRF_EC具有更好预报效果的6个暴雨事件,通过HYSPLIT后向轨迹模式分别追踪其累计降水量最大值点的水汽来源及路径发现,有6个事件均有向降水区西侧上游延伸的水汽来源通道,进一步表明了敏感区的初始水汽场的准确性对暴雨预报的影响。因此降水区西侧上游的水汽场的误差是这11次梅汛期暴雨过程重要的预报误差来源,对其准确描述可有助于预报效果的提升。
  • 图  1  2020年11次梅汛期暴雨事件TIGGE_EC、WRF_FNL、WRF_EC预报累积降水在大雨以上量级的TS评分

    Figure  1.  TS scores of the heavy rain for TIGGE_EC, WRF_FNL, and WRF_EC forecast cumulative precipitation of the eleven rainstorm events during the Meiyu period in 2020

    图  2  2020年11次梅汛期暴雨事件TIGGE_EC、WRF_FNL、WRF_EC预报累积降水分别与OBS实测累计降水的相关,其中红色阴影为WRF_EC优于WRF_FNL的程度,黑色阴影为WRF_FNL优于WRF_EC的程度

    Figure  2.  Correlation coefficient of the TIGGE_EC, WRF_FNL, and WRF_EC forecast cumulative precipitation, respectively, with OBS (observation) in the eleven rainstorm events during the Meiyu period in 2020. The red shade is the degree to which WRF_EC is better than WRF_FNL, and the black shade is the degree to which WRF_FNL is better than WRF_EC

    图  3  2020年梅汛期暴雨事件2(左)、事件5(中)、事件6(右)的(a、b、c)累积实测降水(单位:mm)及(d、e、f)扰动湿能量(单位:105 J)分布。a–c中红框为选定的降水区,d–f中红框为选定的敏感区

    Figure  3.  (a, b, c)Precipitation (units: mm) distribution and (d, e, f) wet energy (units: 105 J) distribution of rainstorm event 2 (left), event 5 (middle), and event 6 (right) in 2020, respectively, where the red frame in (a)–(c) is the selected precipitation area, and the red frame in (d)–(f) is the selected sensitive area

    图  4  模拟区域内地形(单位:m)分布。红框为2020年11次梅汛期暴雨事件敏感区的集中分布位置

    Figure  4.  Distribution of terrain (units: m) in the simulation area. The red box is the concentrated distribution position of the sensitive area of eleven rainstorm events during the Meiyu period in 2020

    图  5  2020年9次梅汛期暴雨事件WRF_FNL、WRF_EC、 F-sens预报累积降水量分别与OBS实测累积降水量的相关

    Figure  5.  Correlation coefficients of WRF_FNL, WRF_EC, and F-sens forecast cumulative precipitation, respectively, with OBS in the nine rainstorm events during the Meiyu period in 2020

    图  6  2020年梅汛期暴雨事件3敏感区内湿能量最大值点的扰动动能、扰动有效位能以及扰动比湿能(单位:J)分别沿(a、b、c)纬向、(d、e、f)经向的剖面:(a、d)扰动动能;(b、e)扰动有效位能;(c、f)扰动比湿能

    Figure  6.  Cross-sectional view of the disturbance’s kinetic energy, effective potential energy, and specific humidity energy (units: J) at the maximum moisture energy point in the sensitive area of rainstorm event 3 in 2020 along the (a, b, c) longitudinal and (d, e, f) latitude directions: (a, d) Perturbation kinetic energy; (b, e) perturbation effective potential energy; (c, f) perturbation specific humidity energy

    图  7  2020年梅汛期暴雨事件4、5、7、8、9、11对应累计降水量最大点空气块的后向轨迹聚类的西侧水汽来源路径及此路径占总路径的百分比,分别对应红、紫、浅蓝、深蓝、橙、绿色曲线及同色百分比,事件4、7、8、9的水汽来源路径高度为700 hPa,事件5、11的水汽来源路径所处高度为850 hPa

    Figure  7.  Trajectory clustering of the air block in cases 4, 5, 7, 8, 9, and 11 in 2020 at the largest accumulated precipitation points. The upstream water vapor source path on the west side and the percentage of this path within the total path correspond to the red, purple, light blue, dark blue, orange, and green curves and percentages of the same color, respectively. The height of the water vapor source path for events 4, 7, 8, and 9 is 700 hPa, and that for events 5 and 11 is 850 hPa

    表  1  2020年11次梅汛期暴雨事件的降水时次、区域实测累积总降水量以及单个格点的实测累积最大降水量

    Table  1.   Characteristics of eleven heavy precipitation events during the Meiyu period in 2020, including the precipitation time, cumulative precipitation, and maximum cumulative precipitation in a grid

    事件降水时次区域实测累计降水量/mm实测单点最大累计降水量/mm
    12020-06-10 00:00至 2020-06-11 00:0048930.7257.4
    22020-06-15 00:00至2020-06-15 12:0050681.7122.4
    32020-06-16 00:00至2020-06-16 12:0035901.686.3
    42020-06-18 00:00至2020-06-19 12:0073567.0186.0
    52020-06-20 00:00至2020-06-22 12:00171801.8256.4
    62020-06-27 12:00至2020-06-28 12:00120342.8174.1
    72020-06-29 12:00至2020-06-30 12:0082577.6169.1
    82020-07-02 12:00至2020-07-04 12:00159129.7220.7
    92020-07-05 00:00至2020-07-08 12:00391424.1618.0
    102020-07-15 00:00至2020-07-16 12:0088529.3196.7
    112020-07-17 00:00至2020-07-19 00:00267466.6448.0
    下载: 导出CSV

    表  2  2020年梅汛期11次暴雨事件敏感性实验预报累积降水量与OBS实测累积降水量的相关。表中为各事件分别替换扰动动能、扰动有效位能、扰动比湿能,其中黄色、蓝色、绿色阴影分别为替换扰动有效位能、扰动动能、扰动比湿能中的气象要素场后预报效果最好的事件

    Table  2.   Correlation coefficients between the precipitation of OBS and the sensitivity experiment forecast after replacing the disturbance’s kinetic energy, effective potential energy, and specific humidity. Yellow, blue, and green shades indicate the best forecast effect after replacing the meteorological element field in terms of the disturbance’s effective potential energy, kinetic energy, and specific humidity, respectively

    事件预报与实测累积降水相关系数
    替换扰动
    动能项后
    替换扰动有效
    位能项后
    替换扰动比
    湿能项后
    10.23610.24810.2295
    20.29680.34120.2365
    30.13790.03670.0603
    100.56270.36300.4346
    40.32830.17480.3590
    50.46060.45130.5890
    60.42970.47390.5860
    70.17300.02750.1816
    80.63110.43000.6548
    90.71900.63290.7921
    110.42540.36190.5626
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-09-02
  • 网络出版日期:  2021-11-05
  • 刊出日期:  2022-08-01

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