高级检索

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

GRAPES区域集合预报对2019年中国汛期降水预报评估

王婧卓 陈法敬 陈静 刘雪晴 李红祺 邓国 李晓莉 王远哲

王婧卓, 陈法敬, 陈静, 等. 2021. GRAPES区域集合预报对2019年中国汛期降水预报评估[J]. 大气科学, 45(3): 1−19 doi: 10.3878/j.issn.1006-9895.2008.20146
引用本文: 王婧卓, 陈法敬, 陈静, 等. 2021. GRAPES区域集合预报对2019年中国汛期降水预报评估[J]. 大气科学, 45(3): 1−19 doi: 10.3878/j.issn.1006-9895.2008.20146
WANG Jingzhuo, CHEN Fajing, CHEN Jing, et al. 2021. Verification of GRAPES-REPS Model Precipitation Forecasts over China during 2019 Flood Season [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 45(3): 1−19 doi: 10.3878/j.issn.1006-9895.2008.20146
Citation: WANG Jingzhuo, CHEN Fajing, CHEN Jing, et al. 2021. Verification of GRAPES-REPS Model Precipitation Forecasts over China during 2019 Flood Season [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 45(3): 1−19 doi: 10.3878/j.issn.1006-9895.2008.20146

GRAPES区域集合预报对2019年中国汛期降水预报评估

doi: 10.3878/j.issn.1006-9895.2008.20146
基金项目: 国家重点研发计划2018YFC1507405
详细信息
    作者简介:

    王婧卓,女,1992年出生,硕士,主要从事集合预报研究。E-mail: 15510166003@163.com

    通讯作者:

    陈静,E-mail: chenj@cma.gov.cn

  • 中图分类号: P 458

Verification of GRAPES-REPS Model Precipitation Forecasts over China during 2019 Flood Season

Funds: National Key Research and Development Project (Grant 2018YFC1507405)
  • 摘要: 2019年,数值预报中心开发了以GRAPES全球模式为驱动场,集合变换卡尔曼滤波为初值扰动方法,随机物理过程倾向项为模式扰动方法的10 km水平分辨率GRAPES-REPS V3.0区域集合预报模式,并投入业务运行。基于该模式,作者开展了2019年7~9月夏季降水不确定性的集合预报实时试验,并从统计检验和个例分析角度,与GRAPES-REPS V2.0和ECMWF全球集合预报模式进行对比,由此对GRAPES-REPS V3.0区域集合预报模式的降水预报能力给予客观评价,并分析了引起中尺度强降水预报不确定性的物理机制,研究结论可为诊断集合预报模式及改进集合预报方法提供依据。结果表明:(1)GRAPES-REPS V3.0区域集合预报系统的降水ETS评分在所有预报时效和量级内均优于GRAPES-REPS V2.0区域集合预报模式,降水成员具有明显等同性,且概率预报技巧FSS评分较高,GRAPES-REPS V3.0区域集合预报模式降水预报效果全面优于GRAPES-REPS V2.0区域集合预报模式。(2)GRAPES-REPS V3.0区域集合预报的集合平均降水BIAS评分及小雨和暴雨ETS评分均明显优于ECMWF全球集合预报系统,降水概率预报与ECMWF降水概率具有一定可比性。(3)个例分析结果表明,不同集合预报模式通过刻画中尺度特征物理量不确定性来捕捉降水预报不确定性,初始时刻,GRAPES-REPS V3.0区域集合预报模式和ECMWF全球集合预报模式环流形势分布较为相似,随预报时效演变,GRAPES-REPS V3.0区域集合预报模式对中尺度动力、热力场捕捉更为准确,相应地对降水落区与量级预报较好,概率预报技巧较优。(4)与ECMWF全球集合预报模式相比,GRAPES区域集合预报模式集合成员能很好地预报降水发生、发展、消亡整个过程,故GRAPES-REPS V3.0区域集合预报系统对中国汛期降水具有较强的预报能力。
  • 图  1  GRAPES-REPS V2.0区域集合预报系统(红色),GRAPES-REPS V3.0区域集合预报系统(蓝色)的0~24 h预报时效不同集合成员24h累积降水ETS评分:(a)小雨;(b)中雨;(c)大雨;(d)暴雨。上述为2019年7月1日至7月31日(每日两个起报时次,00时和12时)月平均结果

    Figure  1.  24-h accumulated precipitation ETS scores of different ensemble members at 0-24-h forecast lead times for the GRAPES-REPS V2.0 regional ensemble prediction system (red) and GRAPES-REPS V3.0 regional ensemble prediction system (blue): (a) Light rain; (b) moderate rain; (c) heavy rain; (d) rainstorm. The above results are monthly averages from 1 to 31 July, 2019 (two forecast initial times a day, 0000 UTC and 1200 UTC)

    图  2  (a1–d1)GRAPES-REPS V3.0区域集合预报系统和(a2–d2)GRAPES-REPS V2.0区域集合预报系统逐6小时24 h累积降水的不同邻域半径r的FSS评分随预报时效演变:(a1,a2)小雨;(b1,b2)中雨;(c1,c2)大雨;(d1,d2)暴雨。统计时段如图1

    Figure  2.  Evolution of 24-h accumulated precipitation FSS scores at 6-h intervals with forecast lead times in different neighborhood radiuses r for (a1–d1) the GRAPES-REPS V3.0 regional ensemble prediction system and (a2-d2) the GRAPES-REPS V2.0 regional ensemble prediction system: (a1, a2) Light rain; (b1, b2) moderate rain; (c1, c2) heavy rain; (d1, d2) rainstorm. The statistical periods are the same as those in Fig. 1

    图  3  GRAPES-REPS V2.0区域集合预报系统(红线)和GRAPES-REPS V3.0区域集合预报系统(蓝线)邻域半径为60 km时逐6小时24 h累积降水的FSS评分随预报时效演变:(a)小雨;(b)中雨;(c)大雨;(d)暴雨。统计时段同图1

    Figure  3.  Evolution of 24-h accumulated precipitation FSS scores at 6-h intervals with forecast lead times in a 60-km neighborhood radius for the GRAPES-REPS V2.0 regional ensemble prediction system (red) and the GRAPES-REPS V3.0 regional ensemble prediction system (blue): (a) Light rain; (b) moderate rain; (c) heavy rain; (d) rainstorm. The statistical periods are the same as those in Fig. 1

    图  4  GRAPES-REPS V3.0区域集合预报系统(蓝色),ECMWF全球集合预报系统(红色)不同集合预报成员12~24 h预报时效的12 h累积降水ETS评分:(a)小雨;(b)中雨;(c)大雨;(d)暴雨。上述为2019年7月1日至9月30日(每日两个起报时次:00时和12时)3个月平均结果

    Figure  4.  12-h accumulated precipitation ETS scores of different ensemble members at 12-24-h forecast lead times for the GRAPES-REPS V3.0 regional ensemble prediction system (blue) and the ECMWF global ensemble prediction system (red): (a) Light rain; (b) moderate rain; (c) heavy rain; (d) rainstorm. The above results are three-month averages from 1 July to 30 September 2019 (two forecast initial times a day, 0000UTC and 1200UTC)

    图  5  GRAPES-REPS V3.0区域集合预报系统(蓝色),ECMWF全球集合预报系统(红色)不同预报时效的集合平均12 h累积降水(a1–d1)ETS评分和(a2–d2)BIAS评分:(a1,a2)小雨;(b1,b2)中雨;(c1,c2)大雨;(d1,d2)暴雨。统计时段同图4

    Figure  5.  Ensemble mean 12-h accumulated precipitation (a1–d1) ETS and (a2–d2) bias scores with different forecast lead times for the GRAPES-REPS V3.0 regional ensemble prediction system (blue) and the ECMWF global ensemble prediction system (red): (a1, a2) Light rain; (b1, b2) moderate rain; (c1, c2) heavy rain; (d1, d2) rainstorm. The statistical periods are the same as those in Fig. 4

    图  6  GRAPES-REPS V3.0区域集合预报系统(蓝色),ECMWF全球集合预报系统(红色)的12 h累积降水Brier评分:(a)小雨;(b)中雨;(c)大雨;(d)暴雨。统计时段同图4

    Figure  6.  12-h accumulated precipitation Brier scores for the GRAPES-REPS V3.0 regional ensemble prediction system (blue) and the ECMWF global ensemble prediction system (red): (a) Light rain; (b) moderate rain; (c) heavy rain; (d) rainstorm. The statistical periods are the same as those in Fig. 4

    图  7  GRAPES-REPS V3.0区域集合预报系统(蓝色),ECMWF全球集合预报系统(红色)的12 h累积降水AROC评分:(a)小雨;(b)中雨;(c)大雨;(d)暴雨。统计时段同图4

    Figure  7.  12-h accumulated precipitation AROC scores for the GRAPES-REPS V3.0 regional ensemble prediction system (blue) and the ECMWF global ensemble prediction system (red): (a) Light rain; (b) moderate rain; (c) heavy rain; (d) rainstorm. The statistical periods are the same as those in Fig. 4

    图  8  GRAPES-REPS V3.0区域集合预报系统(实线)和ECMWF全球集合预报系统(短虚线)的12 h累积降水可靠性曲线(横坐标为预报概率,表示为对某类降水事件,预报出现降水的集合成员数与总集合成员数比值。纵坐标为观测频率,表示在该预报概率下,观测出现降水的格点数与预报出现降水的格点数之比)(蓝色:小雨,红色:中雨,绿色:大雨,紫色:暴雨):(a1,a2)12~24 h预报时效的12 h累积降水;(b1,b2)36~48 h预报时效的12 h累积降水;(c1,c2) 60~72 h预报时效的12 h累积降水。统计时段同图4

    Figure  8.  12-h accumulated precipitation reliability diagrams (Horizontal coordinate is forecast probability, which is expressed as the ratio of the ensemble member numbers for predicted precipitation to the total ensemble member numbers for the certain precipitation events. Vertical coordinate is observation frequency, which is expressed as the ratio of the grid numbers for observed precipitation to the grid numbers for predicted precipitation under the corresponding forecast probability) for the GRAPES-REPS V3.0 regional ensemble prediction system (solid line) and the ECMWF global ensemble prediction system (short dashed line): (a1, a2) 12-h accumulated precipitation at 12-24-h forecast lead times; (b1, b2) 12-h accumulated precipitation at 36-48-h forecast lead times; (c1, c2) 12-h accumulated precipitation at 60-72-h forecast lead times. Blue: light rain, red: moderate rain, green: heavy rain, and purple: rainstorm. The statistical periods are the same as those in Fig.4

    图  9  2019年8月4日12:00起报的12~36 h预报时效的24 h累积降水量(a)实况(单位:mm),(b1,c1)集合平均降水量分布(单位:mm),(b2,c2)大于或等于50 mm降水概率预报。(b1,b2)GRAPES-REPS V3.0区域集合预报系统,(c1, c2)ECMWF全球集合预报系统

    Figure  9.  24-h accumulated precipitation at 12–36-h forecast lead times initialized from 1200 UTC 4 August 2019: (a) Observed precipitation (units: mm); (b1, c1) ensemble mean precipitation (units: mm); (b2, c2) probability of 24-h accumulated precipitation greater or equal to 50 mm. (b1, b2) GRAPES-REPS V3.0 regional ensemble prediction system, (c1, c2) ECMWF global ensemble prediction system

    图  10  2019年8月4日12:00模式起报的集合平均(a1,a2)0 h预报时效的环流形势(阴影:700 hPa涡度场,单位:10−5 s−1,蓝色:700 hPa风向杆);(b1–d1,b2–d2)沿图9a白色直线 [过(29°N,103.5°E)和(33.5°N,106.5°E)两点直线] 的剖面,其中(b1,b2)0 h预报时效的涡度场(阴影,单位:10−5 s−1),(c1,c2)0 h预报时效的假相当位温场(阴影,单位:K),(d1,d2)0~3 h预报时效的3 h累积降水量(单位:mm)。(a1–d1)GRAPES-REPS V3.0区域集合预报模式,(a2–d2)ECMWF全球集合预报模式

    Figure  10.  Ensemble mean circulation situation diagrams initialized from 1200 UTC 4 August 2019. (a1, a2) Shaded circulation pattern at 00 h forecast lead time: vortex field at the 700-hPa level (units: 10−5 s−1), blue: wind-direction shaft at the 700-hPa level; (b1–d1, b2–d2) The cross section along the white line in Fig. 9a [crossing (29°N,103.5°E) and (33.5°N,106.5°E) points), where (b1, b2) the vortex field at 00 h forecast lead time (shaded, units: 10−5 s−1), (c1, c2) pseudo-equivalent potential temperature at 00 h forecast lead time (shaded, units: K), (d1–d2) The 3-h model forecasted accumulated precipitation (units: mm) at 0-3-h forecast lead times. (a1–d1) GRAPES-REPS V3.0 regional ensemble prediction system and (a2–d2) ECMWF global ensemble prediction system

    图  11  图10,但为2019年8月4日12:00模式起报的集合平均结果:(a1–c1,a2–c2)12 h预报时效,其中(c1, c2)等值线:大于0 m s-1的垂直速度(单位:m s-1),(d1–d2)12~15 h预报时效

    Figure  11.  Same as Fig. 10, but for the ensemble mean results initialized from 1200 UTC 4 August 2019: (a1–c1, a2–c2) at 12 h forecast lead time, where (c1, c2) is contour: vertical wind exceeding 0 m s-1 (units: m s-1), (d1–d2) at 12-15-h forecast lead times

    图  12  2019年8月4日12:00起报的(a1,a2)区域A(30.3°N~33°N,103°E~106.5°E)和(b1,b2)区域B(28.3°N~30.3°N,101°E~105.5°E)平均的6 h累积降水量随预报时效的演变(蓝色实线:控制预报,黑色虚线:集合成员)及相应时效的观测(红色实线,单位:mm)。(a1,b1)GRAPES-REPS V3.0区域集合预报系统,(a2,b2)ECMWF全球集合预报系统

    Figure  12.  Evolution of (a1, a2) domain-A averaged (130.3°N–33°N, 03°E–106.5°E,) and (b1, b2) domain-B averaged (82.3°N–30.3°N, 101°E–105.5°E) 6-h accumulated precipitation with forecast lead times initialized from 1200 UTC 4 August 2019 (blue solid line: control forecast, black dashed line: ensemble members) and the observation in corresponding times (red solid line, units: mm). (a1, b1) GRAPES-REPS V3.0 regional ensemble prediction system, (a2, b2) ECMWF global ensemble prediction system

    表  1  GRAPES-REPS V1.0, V2.0和V3.0系统参数对比

    Table  1.   Comparison of the system configurations of GRAPES-REPS V1.0, V2.0, and V3.0

    模式版本GRAPES-REPS V1.0(2014)GRAPES-REPS V2.0(2016)GRAPES-REPS V3.0(2019)
    控制预报GRAPES-MESO V3.4GRAPES-MESO V4.0GRAPES-MESO V4.3
    分辨率0.15°/L500.15°/L500.1°/L50
    控制预报初值和侧边界T213-GEPST639-GEPSNCEP-GFS
    同化分析技术云分析
    初值不确定性ETKF(6 h循环)ETKF+MSB(12 h循环)ETKF(6 h循环)
    模式不确定性多物理过程组合多物理+SPPT单一物理+SPPT
    边界不确定性T213-GEPST639-GEPSGRAPES-GEPS
    台风不确定性条件性台风涡旋重定位技术
    集合成员数151515
    预报区域15°N~65°N,70°E~140°E 15°N~65°N,70°E~140°E 15°N~65°N,70°E~140°E
    预报时效84 h(0000、1200 UTC) ,
    6 h(0600、1800 UTC)
    84 h(0000、1200 UTC)84 h(0000、1200 UTC) ,
    6 h(0600、1800 UTC)
    模式输出间隔3 h1 h1 h
    下载: 导出CSV

    表  2  双态分类联列表

    Table  2.   The binary classification contingency table

    观测有降水观测无降水预报相加
    预报有降水a(命中)b(空报)a+b
    预报无降水c(漏报)d(正确否定)c+d
    观测相加a+cb+da+b+c+d
    注:a表示对某一降水事件,预报有降水和观测有降水的格点数,即为预报命中;b表示预报有降水,观测无降水的格点数,即为预报空报;c表示预报无降水,观测有降水的格点数,即为预报漏报;d表示预报和观测均无降水的格点数,即为预报正确否定
    下载: 导出CSV
  • [1] 贝耐芳, 赵思雄. 2002. 初值及物理过程对“98.7”暴雨预报结果的影响 [J]. 气候与环境研究, 7(4): 386−396. doi: 10.3878/j.issn.1006-9585.2002.04.02

    Bei Naifang, Zhao Sixiong. 2002. Effect of initial data and physical processes on the heavy rainfall prediction in July 1998 [J]. Climatic and Environmental Research (in Chinese), 7(4): 386−396. doi: 10.3878/j.issn.1006-9585.2002.04.02
    [2] Brier G W. 1950. Verification of forecasts expressed in terms of probability [J]. Mon. Wea. Rev., 78(1): 1−3. doi:10.1175/1520-0493(1950)078<0001:VOFEIT>2.0.CO;2
    [3] Bröcker J, Smith L A. 2007. Increasing the reliability of reliability diagrams [J]. Wea. Forecasting, 22(3): 651−661. doi: 10.1175/WAF993.1
    [4] Buizza R. 2008. The value of probabilistic prediction [J]. Atmos. Sci. Lett., 9(2): 36−42. doi: 10.1002/asl.170
    [5] 陈洪滨, 范学花. 2009. 2008年极端天气和气候事件及其他相关事件的概要回顾 [J]. 气候与环境研究, 14(3): 329−340. doi: 10.3878/j.issn.1006-9585.2009.03.10

    Chen Hongbin, Fan Xuehua. 2009. Some extreme events of weather, climate and related phenomena in 2008 [J]. Climatic and Environmental Research (in Chinese), 14(3): 329−340. doi: 10.3878/j.issn.1006-9585.2009.03.10
    [6] 陈静, 薛纪善, 颜宏. 2003a. 物理过程参数化方案对中尺度暴雨数值模拟影响的研究 [J]. 气象学报, 61(2): 203−218. doi: 10.11676/qxxb2003.019

    Chen Jing, Xue Jishan, Yan Hong. 2003a. The impact of physics parameterization schemes on mesoscale heavy rainfall simulation [J]. Acta Meteor. Sinica (in Chinese), 61(2): 203−218. doi: 10.11676/qxxb2003.019
    [7] 陈静, 薛纪善, 颜宏. 2003b. 华南中尺度暴雨数值预报的不确定性与集合预报试验 [J]. 气象学报, 61(4): 432−446. doi: 10.3321/j.issn:0577-6619.2003.04.005

    Chen Jing, Xue Jishan, Yan Hong. 2003b. The uncertainty of mesoscale numerical prediction of South China heavy rain and the ensemble simulations [J]. Acta Meteor. Sinica (in Chinese), 61(4): 432−446. doi: 10.3321/j.issn:0577-6619.2003.04.005
    [8] Chen J, Xue J S. 2009. Heavy rainfall ensemble prediction: Initial condition perturbation vs multi-physics perturbation [J]. Acta Meteor. Sinica, 23(1): 53−67.
    [9] Donaldson Jr R J, Dyer R M, Kraus M J. 1975. An objective evaluator of techniques for predicting severe weather events [C]//Preprints: Ninth Conference on Severe Local Storms. Norman: American Meteorological Society, 321–326.
    [10] Du J. 2007. Uncertainty and ensemble forecast [Z]. Science and Technology Infusion Climate Bulletin, 42pp.
    [11] Du J, Mullen S L, Sanders F. 1997. Short-range ensemble forecasting of quantitative precipitation [J]. Mon. Wea. Rev., 125(10): 2427−2459. doi:10.1175/1520-0493(1997)125<2427:SREFOQ>2.0.CO;2
    [12] 杜钧, 陈静. 2010. 单一值预报向概率预报转变的基础: 谈谈集合预报及其带来的变革 [J]. 气象, 36(11): 1−11. doi: 10.7519/j.issn.1000-0526.2010.11.001

    Du Jun, Chen Jing. 2010. The corner stone in facilitating the transition from deterministic to probabilistic forecasts—Ensemble forecasting and its impact on numerical weather prediction [J]. Meteor. Mon. (in Chinese), 36(11): 1−11. doi: 10.7519/j.issn.1000-0526.2010.11.001
    [13] 段晚锁, 汪叶, 霍振华, 等. 2019. 数值天气预报和气候预测的集合预报方法: 思考与展望 [J]. 气候与环境研究, 24(3): 396−406. doi: 10.3878/j.issn.1006-9585.2018.18133

    Duan Wansuo, Wang Ye, Huo Zhenhua, et al. 2019. Ensemble forecast methods for numerical weather forecast and climate prediction: Thinking and prospect [J]. Climatic and Environmental Research (in Chinese), 24(3): 396−406. doi: 10.3878/j.issn.1006-9585.2018.18133
    [14] Friederichs P, Hense A. 2008. A probabilistic forecast approach for daily precipitation totals [J]. Wea. Forecasting, 23(4): 659−673. doi: 10.1175/2007WAF2007051.1
    [15] Huo Z H, Duan W S, Zhou F F. 2019. Ensemble forecasts of tropical cyclone track with orthogonal conditional nonlinear optimal perturbations [J]. Adv. Atmos. Sci., 36(2): 231−247. doi: 10.1007/s00376-018-8001-1
    [16] 江志红, 丁裕国, 陈威霖. 2007. 21世纪中国极端降水事件预估 [J]. 气候变化研究进展, 3(4): 202−207. doi: 10.3969/j.issn.1673-1719.2007.04.003

    Jiang Zhihong, Ding Yuguo, Chen Weilin. 2007. Projection of precipitation extremes for the 21st century over China [J]. Advances in Climate Change Research (in Chinese), 3(4): 202−207. doi: 10.3969/j.issn.1673-1719.2007.04.003
    [17] 李俊, 杜钧, 刘羽. 2015. 北京“7.21”特大暴雨不同集合预报方案的对比试验 [J]. 气象学报, 73(1): 50−71. doi: 10.11676/qxxb2015.008

    Li Jun, Du Jun, Liu Yu. 2015. A comparison of initial condition-, multi-physics- and stochastic physics-based ensembles in predicting Beijing “7.21” excessive storm rain event [J]. Acta Meteor. Sinica (in Chinese), 73(1): 50−71. doi: 10.11676/qxxb2015.008
    [18] 刘雪晴, 陈静, 陈法敬, 等. 2020. 降水邻域集合概率方法尺度敏感性试验 [J]. 大气科学, 44(2): 282−296. doi: 10.3878/j.issn.1006-9895.1903.18228

    Liu Xueqing, Chen Jing, Chen Fajing, et al. 2020. Scale sensitivity experiments of precipitation neighborhood ensemble probability method [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 44(2): 282−296. doi: 10.3878/j.issn.1006-9895.1903.18228
    [19] Mason I. 1979. On reducing probability forecasts to yes/no forecasts [J]. Mon. Wea. Rev., 107(2): 207−211. doi:10.1175/1520-0493(1979)107<0207:ORPFTY>2.0.CO;2
    [20] Molteni F, Buizza R, Palmer T N, et al. 1996. The ECMWF ensemble prediction system: Methodology and validation [J]. Quart. J. Roy. Meteor. Soc., 122(529): 73−119. doi: 10.1002/qj.49712252905
    [21] Murphy A H. 1973. A new vector partition of the probability score [J]. J. Appl. Meteor., 12(4): 595−600. doi:10.1175/1520-0450(1973)012<0595:ANVPOT>2.0.CO;2
    [22] 彭新东, 常燕, 王式功. 2010. GRAPES模式对2008年两次强降水过程的数值预报检验 [J]. 高原气象, 29(2): 321−330.

    Peng Xindong, Chang Yan, Wang Shigong. 2010. Numerical validation of GRAPES model with two severe precipitation processes in 2008 [J]. Plateau Meteorology (in Chinese), 29(2): 321−330.
    [23] 齐艳军, 张人禾, Li T. 2016. 1998年夏季长江流域大气季节内振荡的结构演变及其对降水的影响 [J]. 大气科学, 40(3): 451−462. doi: 10.3878/j.issn.1006-9895.1507.15107

    Qi Yanjun, Zhang Renhe, Li T. 2016. Structure and evolution characteristics of atmospheric intraseasonal oscillation and its impact on the summer rainfall over the Yangtze River basin in 1998 [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 40(3): 451−462. doi: 10.3878/j.issn.1006-9895.1507.15107
    [24] Roberts N M, Lean H W. 2008. Scale-selective verification of rainfall accumulations from high-resolution forecasts of convective events [J]. Mon. Wea. Rev., 136(1): 78−97. doi: 10.1175/2007MWR2123.1
    [25] Saito K, Seko H, Kunii M, et al. 2012. Effect of lateral boundary perturbations on the breeding method and the local ensemble transform Kalman filter for mesoscale ensemble prediction [J]. Tellus A: Dynamic Meteorology and Oceanography, 64(1): 11594. doi: 10.3402/tellusa.v64i0.11594
    [26] Schaefer J T. 1990. The critical success index as an indicator of warning skill [J]. Wea. Forecasting, 5(4): 570−575. doi:10.1175/1520-0434(1990)005<0570:TCSIAA>2.0.CO;2
    [27] Stensrud D J, Fritsch J M. 1994. Mesoscale convective systems in weakly forced large-scale environments. Part II: Generation of a mesoscale initial condition [J]. Mon. Wea. Rev., 122(9): 2068−2083. doi:10.1175/1520-0493(1994)122<2068:mcsiwf>2.0.co;2
    [28] Stensrud D J, Brooks H E, Du J, et al. 1999. Using ensembles for short-range forecasting [J]. Mon. Wea. Rev., 127(4): 433−446. doi:10.1175/1520-0493(1999)127<0433:uefsrf>2.0.co;2
    [29] Stensrud D J, Bao J W, Warner T T. 2000. Using initial condition and model physics perturbations in short-range ensemble simulations of mesoscale convective systems [J]. Mon. Wea. Rev., 128(7): 2077−2107. doi:10.1175/1520-0493(2000)128<2077:uicamp>2.0.co;2
    [30] 孙建华, 卫捷, 傅慎明, 等. 2018. 江淮流域持续性强降水过程的多尺度物理模型 [J]. 大气科学, 42(4): 741−754. doi: 10.3878/j.issn.1006-9895.1803.17246

    Sun Jianhua, Wei Jie, Fu Shenming, et al. 2018. The multi-scale physical model for persistent heavy rainfall events in the Yangtze–Huaihe River valley [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 42(4): 741−754. doi: 10.3878/j.issn.1006-9895.1803.17246
    [31] Swets J A. 1986. Indices of discrimination or diagnostic accuracy: Their ROCs and implied models [J]. Psychological Bulletin, 99(1): 100−117. doi: 10.1037/0033-2909.99.1.100
    [32] 谭燕, 陈德辉. 2007. 基于非静力模式物理扰动的中尺度集合预报试验 [J]. 应用气象学报, 18(3): 396−406. doi: 10.3969/j.issn.1001-7313.2007.03.017

    Tan Yan, Chen Dehui. 2007. Meso-scale ensemble forecasts on physical perturbation using a non-hydrostatic model [J]. Journal of Applied Meteorological Science (in Chinese), 18(3): 396−406. doi: 10.3969/j.issn.1001-7313.2007.03.017
    [33] 陶诗言, 张小玲, 张顺利. 2004. 长江流域梅雨锋暴雨灾害研究 [M]. 北京: 气象出版社, 192pp.

    Tao Shiyan, Zhang Xiaoling, Zhang Shunli. 2004. A Study on the Disaster of Heavy Rainfalls over the Yangtze River Basin in the Meiyu Period (in Chinese) [M]. Beijing: China Meteorological Press, 192pp.
    [34] 王叶红, 赵玉春, 崔春光. 2006. 多普勒雷达估算降水和反演风在不同初值方案下对降水预报影响的数值研究 [J]. 气象学报, 64(4): 485−499. doi: 10.11676/qxxb2006.048

    Wang Yehong, Zhao Yuchun, Cui Chunguang. 2006. Numerical research on effects upon precipitation forecast of Doppler-radar estimated precipitation and retrieved wind field under different model initial schemes [J]. Acta Meteor. Sinica (in Chinese), 64(4): 485−499. doi: 10.11676/qxxb2006.048
    [35] 王婧卓, 陈静, 庄照荣, 等. 2018a. GRAPES区域集合预报模式的初值扰动增长特征 [J]. 大气科学, 42(2): 367−382. doi: 10.3878/j.issn.1006-9895.1708.17141

    Wang Jingzhuo, Chen Jing, Zhuang Zhaorong, et al. 2018a. Characteristics of initial perturbation growth rate in the regional ensemble prediction system of GRAPES [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 42(2): 367−382. doi: 10.3878/j.issn.1006-9895.1708.17141
    [36] 王婧卓, 马红云, 宋洁, 等. 2018b. 云凝结核浓度对北京一次降水过程影响的数值模拟 [J]. 气象科学, 38(1): 95−103. doi: 10.3969/2017jms.0022

    Wang Jingzhuo, Ma Hongyun, Song Jie, et al. 2018b. Simulation study on the effects of aerosol concentration upon a precipitation event in Beijing [J]. Journal of the Meteorological Sciences (in Chinese), 38(1): 95−103. doi: 10.3969/2017jms.0022
    [37] 吴秋霞, 史历, 翁永辉, 等. 2007. AREMS/973模式系统对2004年中国汛期降水实时预报检验 [J]. 大气科学, 31(2): 298−310. doi: 10.3878/j.issn.1006-9895.2007.02.11

    Wu Qiuxia, Shi Li, Weng Yonghui, et al. 2007. Verification for AREMS/973 Real-time precipitation forecasts over China during the flood season in 2004 [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 31(2): 298−310. doi: 10.3878/j.issn.1006-9895.2007.02.11
    [38] 吴政秋, 张进, 陈静, 等. 2020. GRAPES区域集合预报条件性台风涡旋重定位方法研究 [J]. 气象学报, 78(2): 163−176. doi: 10.11676/qxxb2020.027

    Wu Zhengqiu, Zhang Jin, Chen Jing, et al. 2020. The study on the method of conditional typhoon vortex relocation for GRAPES regional ensemble prediction [J]. Acta Meteor. Sinica (in Chinese), 78(2): 163−176. doi: 10.11676/qxxb2020.027
    [39] 徐致真, 陈静, 王勇, 等. 2019. 中尺度降水集合预报随机参数扰动方法敏感性试验 [J]. 气象学报, 77(5): 849−868. doi: 10.11676/qxxb2019.039

    Xu Zhizhen, Chen Jing, Wang Yong, et al. 2019. Sensitivity experiments of a stochastically perturbed parameterizations (SPP) scheme for mesoscale precipitation ensemble prediction [J]. Acta Meteor. Sinica (in Chinese), 77(5): 849−868. doi: 10.11676/qxxb2019.039
    [40] Xue J S, Liu Y. 2007. Numerical weather prediction in China in the new century—Progress, Problems and Prospects [J]. Advances in Atmospheric Sciences, 24(6): 1099−1108. doi: 10.1007/s00376-007-1099-1
    [41] 闫敬华, Majewski D. 2003. 中尺度数值预报模式初边值作用的试验研究 [J]. 热带气象学报, 19(4): 337−344. doi: 10.3969/j.issn.1004-4965.2003.04.001

    Yan Jinghua, Majewski D. 2003. Experimental study of the role of initial and boundary conditions in mesoscale numerical weather prediction [J]. Journal of Tropical Meteorology (in Chinese), 19(4): 337−344. doi: 10.3969/j.issn.1004-4965.2003.04.001
    [42] 袁月, 李晓莉, 陈静, 等. 2016. GRAPES区域集合预报系统模式不确定性的随机扰动技术研究 [J]. 气象, 42(10): 1161−1175. doi: 10.7519/j.issn.1000-0526.2016.10.001

    Yuan Yue, Li Xiaoli, Chen Jing, et al. 2016. Stochastic parameterization toward model uncertainty for the GRAPES mesoscale ensemble prediction system [J]. Meteor. Mon. (in Chinese), 42(10): 1161−1175. doi: 10.7519/j.issn.1000-0526.2016.10.001
    [43] 翟盘茂, 任福民, 张强. 1999. 中国降水极值变化趋势检测 [J]. 气象学报, 57(2): 208−216. doi: 10.11676/qxxb1999.019

    Zhai Panmao, Ren Fumin, Zhang Qiang. 1999. Detection of trends in China’s precipitation extremes [J]. Acta Meteor. Sinica (in Chinese), 57(2): 208−216. doi: 10.11676/qxxb1999.019
    [44] Zhang D L, Fritsch J M. 1986. A case study of the sensitivity of numerical simulation of mesoscale convective systems to varying initial conditions [J]. Mon. Wea. Rev., 114(12): 2418−2431. doi:10.1175/1520-0493(1986)114<2418:acsots>2.0.co;2
    [45] 张立凤, 罗雨. 2010. 初始场对暴雨数值预报的影响及集合预报试验 [J]. 气象科学, 30(5): 650−656. doi: 10.3969/j.issn.1009-0827.2010.05.012

    Zhang Lifeng, Luo Yu. 2010. The effect of initial condition on numerical precipitation prediction and ensemble forecast [J]. Scientia Meteorologica Sinica (in Chinese), 30(5): 650−656. doi: 10.3969/j.issn.1009-0827.2010.05.012
    [46] Zhang X B, Wan Q L, Xue J S, et al. 2015. The impact of different physical processes and their parameterizations on forecast of a heavy rainfall in South China in annually first raining season [J]. Journal of Tropical Meteorology, 21(2): 194−210. doi: 10.16555/j.1006-8775.2015.02.010
  • 加载中
图(12) / 表(2)
计量
  • 文章访问数:  83
  • HTML全文浏览量:  22
  • PDF下载量:  44
  • 被引次数: 0
出版历程
  • 收稿日期:  2020-04-15
  • 录用日期:  2020-08-28
  • 网络出版日期:  2020-08-31

目录

    /

    返回文章
    返回