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雷达资料快速更新四维变分同化中增加地面资料同化对强对流临近数值预报的影响

刘瑞婷 陈明轩 肖现 秦睿 高峰 杨璐 吴剑坤 孙娟珍

刘瑞婷,陈明轩,肖现,秦睿,高峰,杨璐,吴剑坤,孙娟珍. 2021. 雷达资料快速更新四维变分同化中增加地面资料同化对强对流临近数值预报的影响. 气象学报,79(6):921-942 doi: 10.11676/qxxb2021.065
引用本文: 刘瑞婷,陈明轩,肖现,秦睿,高峰,杨璐,吴剑坤,孙娟珍. 2021. 雷达资料快速更新四维变分同化中增加地面资料同化对强对流临近数值预报的影响. 气象学报,79(6):921-942 doi: 10.11676/qxxb2021.065
Liu Ruiting, Chen Mingxuan, Xiao Xian, Qin Rui, Gao Feng, Yang Lu, Wu Jiankun, Sun Juanzhen. 2021. The impact of assimilating surface observations in rapid-refresh four-dimensional Variational Radar Data Assimilation System on model-based severe convection nowcasting. Acta Meteorologica Sinica, 79(6):921-942 doi: 10.11676/qxxb2021.065
Citation: Liu Ruiting, Chen Mingxuan, Xiao Xian, Qin Rui, Gao Feng, Yang Lu, Wu Jiankun, Sun Juanzhen. 2021. The impact of assimilating surface observations in rapid-refresh four-dimensional Variational Radar Data Assimilation System on model-based severe convection nowcasting. Acta Meteorologica Sinica, 79(6):921-942 doi: 10.11676/qxxb2021.065

雷达资料快速更新四维变分同化中增加地面资料同化对强对流临近数值预报的影响

doi: 10.11676/qxxb2021.065
详细信息
    作者简介:

    刘瑞婷,主要从事强对流数值模拟、临近预报研究。E-mail:rtliu@ium.cn

    通讯作者:

    陈明轩,主要从事临近预报相关研究。E-mail: mxchen@ium.cn

  • 中图分类号: P456

The impact of assimilating surface observations in rapid-refresh four-dimensional Variational Radar Data Assimilation System on model-based severe convection nowcasting

  • 摘要: 基于雷达资料快速更新四维变分同化(RR4DVar)技术和三维数值云模式发展的快速更新雷达四维变分分析系统(VDRAS),通过在系统中加入地面自动气象站观测资料的同化方法,对发生在北京地区的10个强对流过程开展了地面资料同化的高分辨率模拟分析和检验评估,并与已经业务使用的地面资料融合方法进行对比。研究结果发现,地面观测资料同化使边界层1 km高度以下的分析场改善最为明显,风速和风向的均方根误差分别平均降低0.1 m/s和7.2°,温度的均方根误差降低0.2℃。模式最低层100 m高度的风速均方根误差降低0.5 m/s,风速的误差随高度上升逐渐增大。模式最低层风向的均方根误差降低15.5°,温度的均方根误差降低0.4℃,且1.5 km高度以下的温度偏差都减小。区域内地面10 m高风速的均方根误差平均降低0.2 m/s,风向的均方根误差降低10.8°,地面2 m气温的偏差也降低。随着预报时效的延长,地面温度和风场的误差不断增大,但地面资料同化方法在一定程度上可以提高1 h内地面气象要素的预报效果。对2019年5月17日北京地区局地强对流新生和增强过程的详细分析表明,地面自动气象站观测资料的同化方法相对于融合,可以通过更细致准确地分析低层大气的热动力特征,改善低层气象要素的预报效果。在此基础上,通过探究对流单体的局地触发机理发现,海风锋辐合线与城市的相互作用一定程度上影响了对流的局地新生和发展,该同化方法可以进一步提高北京地区局地突发强对流的临近数值预报能力。

     

  • 图 1  北京及周边地区地形高度 (色阶),北京和天津S波段雷达 (BJRS、TJRS,+表示) 的位置及扫描范围 (150 km),检验所用风廓线雷达 (黑色ο表示)、地基微波辐射计 (红色Δ表示) 和秒探空 (蓝色◊表示) 的位置,以及模拟范围 (黑色虚线框)

    Figure 1.  Topography of Beijing-Tianjin-Hebei region and the simulation domain in the black dotted box;radar sites of S-band in Beijing and Tianjin (BJRS and TJRS) are denoted by "+" and scanning ranges (150 km);observations used for verification at wind profiler,ground-based microwave radiometers,second-level radiosonde are denoted by black "ο",red "Δ" and blue "◊",respectively

    图 3  2019年5月17日 (a、c、e) 北京雷达最低仰角 (0.5°) 观测的反射率因子 (色阶,单位:dBz)、(b、d、f) 区域内所有地面自动站观测的温度 (色阶,单位:°C) 和风场 (风矢)(a、b. 08时10分,c、d. 08时30分,e、f. 10时30分)

    Figure 3.  Beijing radar reflectivity (shaded,unit: dBz) at the lowest elevation angle (0.5°) (a,c,e ),and AWS surface temperature (shaded,unit:°C) and wind vectors (arrow) in the domain (b,d,f ) on 17 May 2019 (a,b. 08:10 UTC;c,d. 08:30 UTC;e,f. 10:30 UTC)

    图 4  SURF-MESO (a、c) 与SURF-4DVar (b、d) 在同化分析循环中雷达径向速度 (单位:m/s,黑色) 和雨水混合比 (单位:g/kg,蓝色) 的观测与背景场 (Innov,虚线)、观测与分析场 (Resi,实线) 残差的均方根误差 (a、b) 以及平均偏差 (c、d) 统计

    Figure 4.  RMSE(a,b) and BIAS(c,d) for innovation (observations minus background;Innov,dotted) and analysis residual (observations minus analysis;Resi,solid) for radial velocity (unit:m/s,black) and rain water mixing ratio (unit:g/kg,blue) with respect to analysis/forecast cycle for (a,c) SURF-MESO and (b,d) SURF-4DVar

    图 5  SURF-4DVar在同化分析循环中地面U风 (单位:m/s,黑色)、V风 (单位:m/s,蓝色) 和气温 (单位:ºC,红色) 的观测与背景场 (Innov,虚线)、观测与分析场 (Resi,实线) 残差的均方根误差 (a) 和平均偏差 (b) 统计

    Figure 5.  RMSE(a) and BIAS (b) for innovation (observations minus background;Innov,dotted) and analysis residual (observations minus analysis;Resi,solid) for surface U wind (unit:m/s,black),surface V wind (unit:m/s,blue) and surface air temperature (unit:ºC,red) with respect to analysis/forecast cycle for SURF-4DVar

    图 6  2019年5月17日08时30分 (a—c) 和10时30分 (d—f) 的近地面温度 (色阶,单位:°C) 和风场的分析场 (a、d. 地面自动气象站观测,b、e. SURF-MESO,c、f. SURF-4DVar;黑色直线AB对应图13的垂直剖面位置)

    Figure 6.  Surface temperature (shaded,unit:°C) and wind vectors ( arrows) at 08:30 UTC (a—c) and 10:30 UTC (d—f) 17 May 2019 from (a,d) AWS observations,(b,e) SURF-MESO and (c,f) SURF-4DVar (the black line AB indicates the location of vertical cross section shown in Fig. 13)

    图 7  2019年5月17日08时30分 (a—c) 和10时30分 (d—f) 的近地面风速大小 (色阶,单位:m/s) 和风场的分析场 (a、d. 地面自动气象站观测,b、e. SURF-MESO,c、f. SURF-4DVar)

    Figure 7.  Surface wind speed (shaded,unit:m/s) and wind vectors (arrow) at 08:30 UTC (a—c) and 10:30 UTC (d—f) 17 May 2019 from (a,d) AWS observations,(b,e) SURF-MESO and (c,f) SURF-4DVar

    图 8  2019年5月17日54511地面自动气象站观测和分析场 (a) 近地面温度 (左纵轴) 及与观测的温度差 (右纵轴) 时间序列 (单位:℃)、(b) 风场的时间序列 (全风羽代表4 m/s)

    Figure 8.  (a) Time series of observed and analyzed surface temperature (left Y-axis) and the difference (right Y-axis) between the observations and analysis (unit: ℃),and (b) analyzed wind field (full barb = 4 m/s) at AWS 54511 on 17 May 2019

    图 9  2019年5月17日00时 (a、c) 和12时 (b、d) 的54511探空观测和分析场温度 (a、b,单位:℃)、风场 (c、d) 的廓线 (全风羽代表4 m/s)

    Figure 9.  Vertical profiles of 54511 sounding observations and analyzed temperature (a,b;unit:℃) and wind field (c,d) at 00:00 UTC (a,c) and 12:00 UTC (b,d;full barb = 4 m/s) 17 May 2019

    图 10  2019年5月17日VDRAS分析场和雷达观测 (a) 最低6个仰角平均的径向速度均方根误差和 (b) 雷达最低仰角径向速度均方根误差的时间序列

    Figure 10.  (a) Root mean square error (RMSE) of radial velocity (Vr) between VDRAS analysis and observations at the lowest six elevation angles and (b) RMSE variation of VDRAS analyzed radial velocity verified at the lowest elevation angle from 07:00 UTC to 13:00 UTC 17 May 2019

    图 11  2019年5月17日09时起报的30 min (b、c) 和1 h (e、f) 组合反射率因子 (色阶,单位:dBz) 预报 和雷达观测 (a、d)(a. 09时30分,d. 10时,b、e. SURF-MESO,c、f. SURF-4DVar)

    Figure 11.  Composite radar reflectivity (color shaded,unit:dBz) for the 30 min (b,c) and 1 h (d,e) forecasts initialized from 09:00 UTC on 17 May 2019 and for radar observation (a,d)(a. 09:30 UTC, d. 10:00 UTC;b,e. SURF-MESO;c,f. SURF-4DVar)

    图 12  2019年5月17日09时起报的30 min (b、c) 和1 h (e、f) 温度 (色阶,单位:℃) 和风场预报 (风矢) 及地面自动气象站观测(a、d) (a. 09时30分,d. 10时,b、e. SURF-MESO,c、f. SURF-4DVar)

    Figure 12.  Surface temperature (shaded,unit:℃) and wind vectors (arrow) for 30 min (b,c) and 1 h (e,f) forecasts initialized from 09:00 UTC on 17 May 2019 and for AWS observation (a,d)(a. 09:30 UTC,d. 10:00 UTC;b,e. SURF-MESO;c,f. SURF-4DVar)

    图 13  2019 年5 月17 日08 时 (a、d、g)、08 时30 分 (b、e、h) 和09 时 (c、f、i) 的扰动温度 (a—c,单位:℃)、垂直速度 (d—f,单位:m/s)及水汽含量 (g—i,单位:g/kg) 分析场叠加风场 (垂直速度扩大100倍) 和组合反射率因子 (等值线,dBz) 沿图6中所示直线AB的垂直剖面 (灰色阴影为地形)

    Figure 13.  Vertical cross sections of low level temperature perturbation (a—c,unit:℃),vertical velocity (d—f,unit:m/s) and water vapor content (g—i,unit:g/kg) with wind vectors (arrows,vertical velocity × 100) and composite radar reflectivity (red contour,unit:dBz) along crossline AB shown in Fig. 6 at 08:00 UTC (a,d,g), 08:30 UTC (b,e,h) and 09:00 UTC (c,f,i) 17 May 2019 (the gray shaded areas denote the terrain across the section)

    表  1  北京地区10个对流个例简介

    Table  1.   Description of the 10 convection cases occurred in Beijing

    序号对流个例天气实况成因分析
    12017年8月5日05时—
    6日00时(世界时,下同)
    雷阵雨,局地短时雨强较大低涡东移引导冷空气南下
    22018年6月29日23时—
    30日12时
    雷阵雨,伴有大风、冰雹和局地强降水低层切变线
    32018年7月24日05—23时雷阵雨东移高空槽和副高外围偏南气流
    42018年8月7日05时—
    8日00时
    强降雨,局地大暴雨副热带高压外围偏南气流和冷空气
    52018年8月12日04—23时强降雨副高外围偏南气流
    62019年5月16日22时—
    17日13时
    分散性雷阵雨,局地暴雨强对流系统
    72019年7月28日05时—
    29日00时
    降水,伴有短时强降水和雷暴大风高空槽和副高外围暖湿气流
    82019年8月1日06—23时雷阵雨,伴有短时强降水和短时大风局地生成及北上加强的对流云团
    92019年8月6日05—23时自西向东出现明显降水伴有雷电短波槽
    102020年6月17日23时—
    18日12时
    小冰雹、雷暴大风、短时强降水高空冷涡
    下载: 导出CSV

    表  2  三维温度和风的分析场客观检验

    Table  2.   Objective verification of 3D temperature and wind analysis fields

    风速均方根
    误差(m/s)
    风速平均绝对
    误差(m/s)
    风向均方根
    误差(°)
    风向平均绝对
    误差(°)
    温度均方根
    误差(℃)
    温度平均绝对
    误差(℃)
    2 km1 km2 km1 km2 km1 km2 km1 km2 km1 km2 km1 km
    SURF-4DVar2.392.271.731.6240.6043.0528.6930.421.631.431.131.02
    SURF-MESO2.492.401.791.7044.4850.2728.7532.511.761.631.211.15
    下载: 导出CSV

    表  3  各站点1 km高度以下温度和风的分析场检验

    Table  3.   Verification of temperature and wind analysis fields below 1 km at different sites

    风速均方根
    误差(m/s)
    风速平均绝对
    误差(m/s)
    风向均方根
    误差(°)
    风向平均绝对
    误差(°)
    温度均方根
    误差(℃)
    温度平均绝对
    误差(℃)
    SURF-4DVarSURF-MESOSURF-4DVarSURF-MESOSURF-4DVarSURF-MESOSURF-4DVarSURF-MESOSURF-4DVarSURF-MESOSURF-4DVarSURF-MESO
    观象台2.082.151.461.4838.2743.7727.5028.811.682.101.481.81
    海淀2.352.511.651.7739.3644.6129.6730.251.021.130.720.79
    延庆2.432.491.731.7554.1358.5038.9738.281.401.671.021.23
    怀柔1.952.301.371.60
    平谷1.241.360.900.98
    探空2.152.561.712.0138.2765.0119.6535.101.361.650.830.96
    下载: 导出CSV

    表  4  强对流个例的地面10 m风和2 m温度的分析场检验

    Table  4.   Verification of 10 m wind and 2 m temperature analysis fields for convection cases

    个例风速均方根
    误差(m/s)
    风速平均
    偏差(m/s)
    风向均方根
    误差(°)
    风向平均
    偏差(°)
    温度均方根
    误差(℃)
    温度平均
    偏差(℃)
    SURF-4DVarSURF-MESOSURF-4DVarSURF-MESOSURF-4DVarSURF-MESOSURF-4DVarSURF-MESOSURF-4DVarSURF-MESOSURF-4DVarSURF-MESO
    11.621.940.380.5948.2860.8−1.35−3.780.911.08−0.1−0.24
    21.671.870.450.4546.9260.73−3.53−12.771.221.35−0.11−0.20
    31.11.250.140.2550.0462.96−3.270.730.961.01−0.08−0.1
    40.750.8200.0261.4972.050.58−2.771.471.490−0.06
    50.850.92−0.08−0.1863.6870.83−0.990.870.900.94−0.05−0.09
    61.671.920.310.4146.4357.56−0.26−3.141.101.27−0.08−0.17
    71.21.460.20.3257.1467.95−3.07−4.230.830.94−0.09−0.21
    82.042.420.570.8639.4353.660.38−3.3211.21−0.11−0.27
    91.121.210.250.3356.2263.12−0.25−0.350.830.87−0.07−0.12
    101.291.430.06−0.0453.4461.45−0.89−2.961.211.32−0.04−0.07
    平均1.331.520.230.3052.3163.11−1.26−3.171.041.15−0.07−0.15
    下载: 导出CSV

    表  5  地面2 m温度和10 m风场的预报场客观检验

    Table  5.   Verification of 2 m temperature and 10 m wind forecasts

    时间U风平均
    偏差(m/s)
    U风均方根
    误差(m/s)
    V风平均
    偏差(m/s)
    V风均方根
    误差(m/s)
    温度平均
    偏差(℃)
    温度均方根
    误差(℃)
    SURF-4DVarSURF-MESOSURF-4DVarSURF-MESOSURF-4DVarSURF-MESOSURF-4DVarSURF-MESOSURF-4DVarSURF-MESOSURF-4DVarSURF-MESO
    t=15 min−0.18−0.111.081.32 0.09−0.041.051.29−0.05−0.121.061.14
    t=30 min−0.19−0.141.291.43 0.02−0.091.241.37−0.01−0.061.101.15
    t=45 min−0.17−0.131.451.52−0.04−0.121.391.47 0.04 0.011.221.24
    t=60 min−0.12−0.111.561.59−0.06−0.121.521.54 0.11 0.091.351.37
    下载: 导出CSV
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  • 收稿日期:  2021-03-10
  • 录用日期:  2021-11-08
  • 修回日期:  2021-07-19
  • 网络出版日期:  2021-10-25
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