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民航华北空管两代快速更新循环同化数值预报系统的检验评估

许晨璐 袁慧玲 吴玲芳 柳贵钧

许晨璐, 袁慧玲, 吴玲芳, 等. 2022. 民航华北空管两代快速更新循环同化数值预报系统的检验评估[J]. 气候与环境研究, 27(4): 523−532 doi: 10.3878/j.issn.1006-9585.2021.21071
引用本文: 许晨璐, 袁慧玲, 吴玲芳, 等. 2022. 民航华北空管两代快速更新循环同化数值预报系统的检验评估[J]. 气候与环境研究, 27(4): 523−532 doi: 10.3878/j.issn.1006-9585.2021.21071
XU Chenlu, YUAN Huiling, WU Lingfang, et al. 2022. Evaluation of Two Generation Rapid Refresh Assimilation Numerical Prediction Systems in North China Regional Air Traffic Management Bureau of CAAC [J]. Climatic and Environmental Research (in Chinese), 27 (4): 523−532 doi: 10.3878/j.issn.1006-9585.2021.21071
Citation: XU Chenlu, YUAN Huiling, WU Lingfang, et al. 2022. Evaluation of Two Generation Rapid Refresh Assimilation Numerical Prediction Systems in North China Regional Air Traffic Management Bureau of CAAC [J]. Climatic and Environmental Research (in Chinese), 27 (4): 523−532 doi: 10.3878/j.issn.1006-9585.2021.21071

民航华北空管两代快速更新循环同化数值预报系统的检验评估

doi: 10.3878/j.issn.1006-9585.2021.21071
基金项目: 国家重点研发计划课题2017YFC1502703,中央高校基本科研业务费专项资金0209-14380104,华北空管局科技立项202007
详细信息
    作者简介:

    许晨璐,女,1991年出生,工程师,主要从事数值预报模式性能评估研究。E-mail:xclllcx@163.com

    通讯作者:

    袁慧玲,E-mail: yuanhl@nju.edu.cn

  • 中图分类号: P456.7

Evaluation of Two Generation Rapid Refresh Assimilation Numerical Prediction Systems in North China Regional Air Traffic Management Bureau of CAAC

Funds: National Key Research and Development Program of China (Grant 2017YFC1502703), the Fundamental Research Funds for the Central Universities (Grant 0209-14380104), Science and Technology Project of North China Regional Air Traffic Management Bureau of CAAC (Grant 202007)
  • 摘要: 民航华北空管气象中心自主研发了第一代快速更新循环同化数值预报系统(NMC-RAP)和更新的第二代快速循环同化数值预报系统(NMC-HRRR)。针对2020年和2021年6~8月华北区域3次大范围雷雨天气过程的雷达反射率的预报结果,从雷达回波的空间分布、航空雷暴临近预报系统(Aviation Thunderstorm Nowcasting System, ATNS)评分、主要机场对流预警的预报效果等角度,对两代快速更新循环同化数值预报系统的预报性能进行了对比分析。结果表明:1)两代系统均能较好体现雷达回波的空间分布特征,回波强度也与区域雷达拼图实况资料比较接近。对于强回波中心落区这样的关键预报,NMC-HRRR总能体现出更大的优势,与实况更吻合。2)两代系统的弱回波预报的ATNS评分均优于强回波。其中,NMC-HRRR 0~9 h内预报的强、弱回波评分整体较高,特别是0~360 min和第540分钟的强回波预报,显著优于NMC-RAP。3)NMC-HRRR在预报对飞行有影响的回波开始出现的时间方面优势明显。对5个主要机场强对流预警的整体评估,定量证明了NMC-HRRR相较NMC-RAP,在强、弱回波的定时、定点预报准确率及其时空分布的改进都是有显著效果的。
  • 图  1  (a)实况、(b)NMC-RAP和(c)NMC-HRRR系统预报的2020年6月1日06:00起报的第150分钟雷达反射率强度的空间分布

    Figure  1.  Spatial distributions of (a) observation and predicted radar reflectivity with the 150th minute forecasts from (b) NMC-RAP and (c) NMC-HRRR systems, both initialized at 0600 UTC 1 Jun 2020

    图  2  同图1,但为2021年6月13日04:00起报的第240分钟雷达反射率强度

    Figure  2.  Same as Fig. 1, but for the radar reflectivity with the 240th minute forecasts initialized at 0400 UTC 13 Jun 2021

    图  3  同图1,但为2021年8月23日08:00起报的第300分钟雷达反射率强度

    Figure  3.  Same as Fig. 1, but for the radar reflectivity with the 300th minute forecasts initialized at 0800 UTC 23 Aug 2021

    表  1  NMC-RAP和NMC-HRRR系统2020年6月1日06:00起报的0~9 h雷达反射率预报结果的航空雷暴临近预报系统(ATNS)评分

    Table  1.   Aviation Thunderstorm Nowcasting System (ATNS) scores of radar reflectivity with 0−9 h forecasts from NMC-RAP and NMC-HRRR systems initialized at 0600 UTC 1 Jun 2020

    预报时效/minNMC-RAP系统评分NMC-HRRR系统评分
    强回波弱回波强回波弱回波
    00.210.620.610.82
    300.540.690.650.80
    600.380.620.590.69
    900.360.580.480.70
    1200.310.590.530.70
    1500.310.570.560.66
    1800.300.580.530.63
    2400.320.550.470.53
    3000.380.540.480.48
    3600.270.460.380.36
    4200.370.400.320.44
    4800.350.330.330.51
    5400.330.330.530.60
    注:强回波≥35 dBZ,弱回波≥20 dBZ(包含强回波),下同。
    下载: 导出CSV

    表  2  同表1,但为2021年6月13日04:00起报的ATNS评分

    Table  2.   Same as Table 1, but for the ATNS scores initialized at 0400 UTC 13 Jun 2021

    预报时效/minNMC-RAP系统评分NMC-HRRR系统评分
    强回波弱回波强回波弱回波
    00.090.270.770.73
    300.350.340.750.70
    600.430.390.610.69
    900.450.420.370.70
    1200.360.420.460.66
    1500.360.400.430.60
    1800.270.370.460.62
    2400.260.330.440.60
    3000.180.340.330.50
    3600.270.360.320.45
    4200.330.450.220.39
    4800.200.360.260.45
    5400.170.310.190.40
    下载: 导出CSV

    表  3  同表1,但为2021年8月23日08:00起报的ATNS评分

    Table  3.   Same as Table 1, but for the ATNS scores initialized at 0800 UTC 23 Aug 2021

    预报时效/minNMC-RAP系统评分NMC-HRRR系统评分
    强回波弱回波强回波弱回波
    00.080.510.370.73
    300.440.670.340.71
    600.380.600.190.50
    900.310.550.280.51
    1200.220.510.290.56
    1500.220.480.360.61
    1800.300.490.410.65
    2400.340.470.320.52
    3000.440.500.560.61
    3600.590.500.720.60
    4200.550.480.680.54
    4800.520.520.680.50
    5400.460.510.620.46
    下载: 导出CSV

    表  4  实况和NMC-RAP系统、NMC-HRRR系统2020年6月1日06:00起报的0~9 h雷达反射率对华北区域5个主要机场的对流预警

    Table  4.   Severe convective warning of radar reflectivity at five airports in North China based on observations, and 0−9 h forecasts from NMC-RAP and NMC-HRRR systems initialized at 0600 UTC 1 Jun 2020

    机场来源不同预报时效(单位:min)下的预警
    0306090120150180240300360420480540
    首都实况
    RAP
    HRRR
    大兴实况
    RAP
    HRRR
    天津实况
    RAP
    HRRR
    石家庄实况
    RAP
    HRRR
    太原实况
    RAP
    HRRR
    下载: 导出CSV

    表  5  实况和NMC-RAP、NMC-HRRR 2020年6月1日06:00起报的0~9 h雷达反射率预报结果对华北区域5个主要机场的对流预警总和

    Table  5.   Counts of total severe convective warning of radar reflectivity at 5 airports in North China based on observations, and 0−9 h forecasts from NMC-RAP and NMC-HRRR systems initialized at 0600 UTC 1 Jun 2020

    ATNS评分之和
    弱回波强回波
    实况62
    NMC-RAP20
    NMC-HRRR42
    下载: 导出CSV

    表  6  同表4,但为2021年6月13日04:00起报

    Table  6.   Same as Table 4, but initialized at 0400 UTC 13 Jun 2021

    机场来源不同预报时效(单位:min)下的预警
    0306090120150180240300360420480540
    首都实况
    RAP
    HRRR
    大兴实况
    RAP
    HRRR
    天津实况
    RAP
    HRRR
    石家庄实况
    RAP
    HRRR
    太原实况
    RAP
    HRRR
    下载: 导出CSV

    表  7  同表5,但为2021年6月13日04:00起报

    Table  7.   Same as Table 5, but initialized at 0400 UTC 13 Jun 2021

    ATNS评分之和
    弱回波强回波
    实况96
    NMC-RAP24
    NMC-HRRR54
    下载: 导出CSV

    表  8  同表4,但为2021年8月23日08:00起报

    Table  8.   Same as Table 4, but initialized at 0800 UTC 23 Aug 2021

    机场来源不同预报时效(单位:min)下的预警
    0306090120150180240300360420480540
    首都实况
    RAP
    HRRR
    大兴实况
    RAP
    HRRR
    天津实况
    RAP
    HRRR
    石家庄实况
    RAP
    HRRR
    太原实况
    RAP
    HRRR
    下载: 导出CSV

    表  9  同表5,但为2021年8月23日08:00起报

    Table  9.   Same as Table 5, but initialized at 0800 UTC 23 Aug 2021

    ATNS评分之和
    弱回波强回波
    实况1610
    NMC-RAP115
    NMC-HRRR125
    下载: 导出CSV

    表  10  同表5,但为3次过程的总和

    Table  10.   Same as Table 5,but for total counts of three convective storms

    ATNS评分之和
    弱回波强回波
    实况3118
    NMC-RAP159
    NMC-HRRR2111
    下载: 导出CSV
  • [1] Alexander C R, Weygandt S, Benjamin S, et al. 2015. The 2015 Operational Upgrades to the Rapid Refresh (RAP) and High-Resolution Rapid Refresh (HRRR) [C]// The 27th Conference on Weather Analysis and Forecasting/23rd Conference on Numerical Weather Prediction. Amer. Meteor. Soc., Phoenix, AZ, USA.
    [2] Benjamin S G, Dévényi D, Weygandt S S, et al. 2004. An hourly assimilation-forecast cycle: The RUC [J]. Mon. Wea. Rev., 132(2): 495−518. doi:10.1175/1520-0493(2004)132<0495:AHACTR>2.0.CO;2
    [3] Bodas-Salcedo A, Webb M J, Brooks M E, et al. 2008. Evaluating cloud systems in the Met Office global forecast model using simulated CloudSat radar reflectivities [J]. J. Geophys. Res. : Atmos., 113(D8): D00A13. doi: 10.1029/2007JD009620
    [4] 陈葆德, 王晓峰, 李泓, 等. 2013. 快速更新同化预报的关键技术综述 [J]. 气象科技进展, 3(2): 29−35. doi: 10.3969/j.issn.2095-1973.2013.02.003

    Chen Baode, Wang Xiaofeng, Li Hong, et al. 2013. An overview of the key techniques in rapid refresh assimilation and forecast [J]. Advances in Meteorological Science and Technology (in Chinese), 3(2): 29−35. doi: 10.3969/j.issn.2095-1973.2013.02.003
    [5] Ciach G J, Krajewski W F. 1999. On the estimation of radar rainfall error variance [J]. Advances in Water Resources, 22(6): 585−595. doi: 10.1016/S0309-1708(98)00043-8
    [6] Davis C A, Ahijevych D A, Trier S B. 2002. Detection and prediction of warm season midtropospheric vortices by the rapid update cycle [J]. Mon. Wea. Rev., 130(1): 24−42. doi:10.1175/1520-0493(2002)130<0024:DAPOWS>2.0.CO;2
    [7] 范水勇, 陈敏, 仲跻芹, 等. 2009. 北京地区高分辨率快速循环同化预报系统性能检验和评估 [J]. 暴雨灾害, 28(2): 119−125. doi: 10.3969/j.issn.1004-9045.2009.02.004

    Fan Shuiyong, Chen Min, Zhong Jiqin, et al. 2009. Performance tests and evaluations of Beijing local high-resolution rapid update cycle system [J]. Torrential Rain and Disasters (in Chinese), 28(2): 119−125. doi: 10.3969/j.issn.1004-9045.2009.02.004
    [8] Gao J D, Stensrud D J. 2012. Assimilation of reflectivity data in a convective-scale, cycled 3DVar framework with hydrometeor classification [J]. J. Atmos. Sci., 69(3): 1054−1065. doi: 10.1175/JAS-D-11-0162.1
    [9] Gao J D, Stensrud D J. 2014. Some observing system simulation experiments with a hybrid 3DEnVAR system for storm-scale radar data assimilation [J]. Mon. Wea. Rev., 142(9): 3326−3346. doi: 10.1175/MWR-D-14-00025.1
    [10] Gao J D, Xue M, Brewster K, et al. 2004. A three-dimensional variational data analysis method with recursive filter for Doppler radars [J]. J. Atmos. Oceanic Technol., 21(3): 457−469. doi:10.1175/1520-0426(2004)021<0457:ATVDAM>2.0.CO;2
    [11] 勾亚彬, 刘黎平, 杨杰, 等. 2014. 基于雷达组网拼图的定量降水估测算法业务应用及效果评估 [J]. 气象学报, 72(4): 731−748. doi: 10.11676/qxxb2014.050

    Gou Yabin, Liu Liping, Yang Jie, et al. 2014. Operational application and evaluation of the quantitative precipitation estimates algorithm based on the multi-radar mosaic [J]. Acta Meteorologica Sinica (in Chinese), 72(4): 731−748. doi: 10.11676/qxxb2014.050
    [12] Hamill T M, Snyder C. 2000. A hybrid ensemble Kalman filter-3D variational analysis scheme [J]. Mon. Wea. Rev., 128(8): 2905−2919. doi:10.1175/1520-0493(2000)128<2905:AHEKFV>2.0.CO;2
    [13] Hu M, Xue M, Brewster K. 2006. 3DVAR and cloud analysis with WSR-88D Level-II data for the prediction of the Fort Worth, Texas, Tornadic thunderstorms. Part I: Cloud analysis and its impact [J]. Mon. Wea. Rev., 134(2): 675−698. doi: 10.1175/MWR3092.1
    [14] Huang Y, Zhang W, Xu C L, et al. 2019. The Establishment of Convective Weather Forecasting System Applied to Air Traffic Management in North China [C]// 2019 IEEE 1st International Conference on Civil Aviation Safety and Information Technology (ICCASIT). Kunming, China: IEEE, 435–439. doi: 10.1109/ICCASIT48058.2019.8973183
    [15] Pan Y J, Xue M, Zhu K F, et al. 2018. A prototype regional GSI-based EnKF-variational hybrid data assimilation system for the rapid refresh forecasting system: Dual-resolution implementation and testing results [J]. Advances in Atmospheric Sciences, 35(5): 518−530. doi: 10.1007/s00376-017-7108-0
    [16] 彭菊香, 李红莉, 崔春光. 2011. 华中区域LAPS中尺度分析场的检验与评估 [J]. 气象, 37(2): 170−176. doi: 10.7519/j.issn.1000-0526.2011.02.006

    Peng Juxiang, Li Hongli, Cui Chunguang. 2011. Verification and evaluation of LAPS analysis field in central China [J]. Meteor. Mon. (in Chinese), 37(2): 170−176. doi: 10.7519/j.issn.1000-0526.2011.02.006
    [17] Shen H, Huang Y, Chen M, et al. 2019. Introduction to the short-term aviation weather forecasting system in North China [C]// The 19th Conference on Aviation, Range, and Aerospace Meteorology. Amer. Meteor. Soc., Phoenix, AZ, USA.
    [18] 苏翔, 袁慧玲, 朱跃建. 2021. 四种定量降水预报客观订正方法对比研究 [J]. 气象学报, 79(1): 132−149. doi: 10.11676/qxxb2020.071

    Su Xiang, Yuan Huiling, Zhu Yuejian. 2021. A comparative study of four objective quantitative precipitation forecast calibration methods [J]. Acta Meteorologica Sinica (in Chinese), 79(1): 132−149. doi: 10.11676/qxxb2020.071
    [19] Sun J Z. 2005. Initialization and numerical forecasting of a supercell storm observed during STEPS [J]. Mon. Wea. Rev., 133(4): 793−813. doi: 10.1175/MWR2887.1
    [20] Sun J Z, Crook N A. 1997. Dynamical and microphysical retrieval from Doppler radar observations using a cloud model and its adjoint. Part I: Model development and simulated data experiments [J]. J. Atmos. Sci., 54(12): 1642−1661. doi:10.1175/1520-0469(1997)054<1642:DAMRFD>2.0.CO;2
    [21] 孙娟珍, 陈明轩, 范水勇. 2016. 雷达资料同化方法:回顾与前瞻 [J]. 气象科技进展, 6(3): 17−27. doi: 10.3969/j.issn.2095-1973.2016.03.002

    Sun Juanzhen, Chen Mingxuan, Fan Shuiyong. 2016. Radar data assimilation methods: Review and future perspective [J]. Advances in Meteorological Science and Technology (in Chinese), 6(3): 17−27. doi: 10.3969/j.issn.2095-1973.2016.03.002
    [22] 唐文苑, 郑永光, 张小雯. 2018. 基于FSS的高分辨率模式华北对流预报能力评估 [J]. 应用气象学报, 29(5): 513−523. doi: 10.11898/1001-7313.20180501

    Tang Wenyuan, Zheng Yongguang, Zhang Xiaowen. 2018. FSS-based evaluation on convective weather forecasts in North China from high resolution models [J]. J. Appl. Meteor. Sci. (in Chinese), 29(5): 513−523. doi: 10.11898/1001-7313.20180501
    [23] Tong M J, Xue M. 2005. Ensemble Kalman filter assimilation of Doppler radar data with a compressible nonhydrostatic model: OSS experiments [J]. Mon. Wea. Rev., 133(7): 1789−1807. doi: 10.1175/MWR2898.1
    [24] Wang H L, Sun J Z, Fan S Y, et al. 2013a. Indirect assimilation of radar reflectivity with WRF 3D-Var and its impact on prediction of four summertime convective events [J]. Journal of Applied Meteorology and Climatology, 52(4): 889−902. doi: 10.1175/JAMC-D-12-0120.1
    [25] Wang H L, Sun J Z, Zhang X, et al. 2013b. Radar data assimilation with WRF 4D-Var. Part I: System development and preliminary testing [J]. Mon. Wea. Rev., 141(7): 2224−2244. doi: 10.1175/MWR-D-12-00168.1
    [26] 王珏, 冷亮, 吴涛. 2015. SWAN系统中QPE产品的应用评估 [J]. 气象科技, 43(3): 380−386. doi: 10.19517/j.1671-6345.2015.03.006

    Wang Jue, Leng Liang, Wu Tao. 2015. Evaluation of radar quantitative precipitation estimation in SWAN [J]. Meteorological Science and Technology (in Chinese), 43(3): 380−386. doi: 10.19517/j.1671-6345.2015.03.006
    [27] 魏东, 尤凤春, 范水勇, 等. 2010. 北京快速更新循环预报系统(BJ-RUC)模式探空质量评估分析 [J]. 气象, 36(8): 72−80. doi: 10.7519/j.issn.1000-0526.2010.08.010

    Wei Dong, You Fengchun, Fan Shuiyong, et al. 2010. Assessment and analysis of sounding information obtained from Beijing rapid update cycle forecast system [J]. Meteorological Monthly (in Chinese), 36(8): 72−80. doi: 10.7519/j.issn.1000-0526.2010.08.010
    [28] Xiao Q N, Kuo Y H, Sun J Z, et al. 2005. Assimilation of Doppler radar observations with a regional 3DVAR system: Impact of Doppler velocities on forecasts of a heavy rainfall case [J]. J. Appl. Meteor. Climatol., 44(6): 768−788. doi: 10.1175/JAM2248.1
    [29] 熊秋芬. 2011. GRAPES_Meso模式的降水格点检验和站点检验分析 [J]. 气象, 37(2): 185−193. doi: 10.7519/j.issn.1000-0526.2011.02.008

    Xiong Qiufen. 2011. Verification of GRAPES_Meso precipitation forecasts based on fine-mash and station datasets [J]. Meteorological Monthly (in Chinese), 37(2): 185−193. doi: 10.7519/j.issn.1000-0526.2011.02.008
    [30] 许晨璐, 王建捷, 黄丽萍. 2017. 千米尺度分辨率下GRAPES-Meso4.0模式定量降水预报性能评估 [J]. 气象学报, 75(6): 851−876. doi: 10.11676/qxxb2017.068

    Xu Chenlu, Wang Jianjie, Huang Liping. 2017. Evaluation on QPF of GRAPES-Meso4.0 model at convection-permitting resolution [J]. Acta Meteorologica Sinica (in Chinese), 75(6): 851−876. doi: 10.11676/qxxb2017.068
    [31] Xu Q. 1996. Generalized adjoint for physical processes with parameterized discontinuities. Part I: Basic issues and heuristic examples [J]. J. Atmos. Sci., 53(8): 1123−1142. doi:10.1175/1520-0469(1996)053<1123:gafppw>2.0.co;2
    [32] 杨显玉, 文军, 牛广山, 等. 2020. 西北地区的快速更新循环同化预报系统性能检验和评估 [J]. 高原气象, 39(1): 90−101. doi: 10.7522/j.issn.1000-0534.2019.00010

    Yang Xianyu, Wen Jun, Niu Guangshan, et al. 2020. Performance tests and evaluations of northwest rapid update cycle prediction system [J]. Plateau Meteorology (in Chinese), 39(1): 90−101. doi: 10.7522/j.issn.1000-0534.2019.00010
    [33] Zhou T J, Yu R C, Chen H M, et al. 2008. Summer precipitation frequency, intensity, and diurnal cycle over China: A comparison of satellite data with rain gauge observations [J]. J. Climate, 21(16): 3997−4010. doi: 10.1175/2008JCLI2028.1
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出版历程
  • 收稿日期:  2021-04-21
  • 网络出版日期:  2021-11-23
  • 刊出日期:  2022-08-01

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