A study on the model uncertainty of rainstorm forecast in Guangzhou on 7 May 2017
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摘要: 2017年5月7日,在弱天气尺度强迫下,广州发生了暖区特大暴雨,局地发展迅速,降水强度极端,多家业务模式出现了漏报情况。为了探究此次降水过程模式预报的不确定性,采用条件非线性最优参数扰动(Conditional Nonlinear Optimal Perturbation related to Parameters,CNOP-P)方法筛选出最能体现中小尺度系统非线性误差增长特征的关键物理参数,以此构造CNOP-P-RP模式扰动方案,并基于CMA-Meso模式进行对流尺度集合预报试验,最后探究了CNOP-P关键参数影响局地对流发生、发展不同阶段的物理机理。结果显示,不同降水阶段的CNOP-P敏感参数主要与垂直扩散、云雨自动转换或其他水成物向雨滴的转换有关。与业务上常用的随机物理倾向扰动(Stochastically Perturbed Parameterization Tendencies,SPPT)方案相比,在本次降水过程中,基于CNOP-P-RP方案构造的集合预报试验具有更高的降水和地面要素的概率预报技巧,集合预报系统可靠性也占优。进一步分析发现,垂直扩散不确定性导致的山前温度梯度和地面冷池的变化在对流触发和暴雨发展中起了重要作用。7日00—04时(北京时,下同),花都强降水中心附近垂直扩散的增强使热量、动量和水汽的垂直输送加强,由此造成的雪、霰粒子融化增多是降水量增大的主要原因,说明该阶段雨滴的形成虽以云水的凝结碰并为主,但冰相粒子的作用不容忽视;7日04—08时,随着水汽输送和上升运动增强,更活跃的暖雨过程主导了增城强降水中心降水量的增多。该研究初步证明CNOP-P-RP方案在刻画对流尺度模式不确定性方面的可行性,可为华南暖区暴雨预报的改进提供一定参考。
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关键词:
- 条件非线性最优参数扰动 /
- 模式不确定性 /
- CMA模式 /
- 对流尺度集合预报
Abstract: A warm-sector torrential rain under weak synoptic scale forcing occurred in Guangzhou on 7 May 2017. The precipitation process developed rapidly and locally, and the precipitation intensity is extremely high. Many operational numerical weather prediction models failed to forecast this storm. To study the model uncertainty in the forecast of this precipitation process, the Conditional Nonlinear Optimal Perturbation related to Parameters (CNOP-P) is adopted to select key physical parameters which can best represent the nonlinear error growth characteristics of the meso-micro scale system. A new model perturbation scheme CNOP-P-RP is constructed based on these key parameters. Convective-permitting ensemble prediction experiment is carried out based on the CMA-Meso model. Finally, the physical mechanism behind the influences of key parameters selected by CNOP-P on local convection in different stages is investigated. The result shows that the key parameters selected by CNOP-P are mainly related to vertical diffusion, auto-conversion from cloud to rain and conversion from other hydrometeors to raindrops. Compared with Stochastic Perturbed Parameterization Tendencies (SPPT) scheme which is widely utilized in operational ensemble prediction systems, the ensemble prediction experiment based on the CNOP-P-RP scheme is more skillful and reliable for probability forecast of precipitation and surface elements in this process. Further analysis shows the variation of piedmont temperature gradient and surface cold pool caused by the uncertainty of vertical diffusion plays an important role in convective triggering and rainstorm development. From 00:00 to 04:00 BT 7 May, the enhancement of vertical diffusion near the center of heavy precipitation in Huadu strengthened the vertical transport of heat, momentum and water vapor. The melting of snow and graupel particles is the main reason for the increase of precipitation, indicating that although the formation of raindrops is mainly caused by condensation near the top of boundary layer and collision of cloud water, the effect of ice particles cannot be ignored. From 04:00 BT to 08:00 BT 7 May, with the strengthening of water vapor transport and upward movement, a more active warm rain process dominated the increase of precipitation in the heavy precipitation center at Zengcheng. This study preliminarily proves the feasibility of the CNOP-P-RP scheme in describing the uncertainties in convection-permitting ensemble prediction systems, and provides some references for the improvement of warm-sector torrential rain forecast in South China. -
图 1 2017年5月6日20时起报的不同业务模式对广东地区24 h累计降水量的预报结果 (a. NCEP_GFS 50 km模式,b. ECMWF_IFS 12.5 km模式)(陈静等,2017)
Figure 1. Forecasts of 24 h accumulated precipitation in Guangdong by different operational models initialized at 20:00 BT 6 May 2017 (a. NCEP_GFS 50 km model,b. ECMWF_IFS 12.5 km model) (Chen,et al,2017)
图 4 A阶段4 h累计降水量观测值 (a) 和对照预报 (b),EXP1a (c) 和EXP2a (d) 中4 h累计降水的集合平均 (色阶) 和集合离散度 (实线),EXP1a集合平均相对于对照预报 (e) 和EXP2a集合平均相对于对照预报 (f) 的4 h累计降水量之差 (HD:花都,ZC:增城,单位:mm)
Figure 4. Observed (a) and control experiment forecast (b) of 4 h accumulated precipitation at stage A,ensemble average (shaded) and ensemble spread (solid line) of 4 h accumulated precipitation in EXP1a (c) and EXP2a (d),(e) difference in 4 h accumulated precipitation between the ensemble average of EXP1a and the control experiment,(f) difference in 4 h accumulated precipitation between the ensemble average of EXP2a and the control experiment forecast (HD:Huadu,ZC:Zengcheng,unit:mm)
图 5 B阶段4 h累计降水量观测值 (a) 和对照预报 (b),EXP1b (c) 和EXP2b (d) 中4 h累计降水的集合平均 (色阶) 和集合离散度 (实线),EXP1b集合平均相对于对照预报 (e) 和EXP2b集合平均相对于对照预报 (f) 的4 h累计降水量之差 (单位:mm)
Figure 5. Observed (a) and control experiment forecast (b) of 4 h accumulated precipitation at stage B,ensemble average (shaded) and ensemble spread (solid line) of 4 h accumulated precipitation in EXP1b (c) and EXP2b (d),(e) difference in 4h accumulated precipitation between the ensemble average of EXP1b and the control experiment forecast,(f) difference in 4 h accumulated precipitation between the ensemble average of EXP2b and the control experiment forecast (unit:mm)
图 7 集合预报试验中连续分级概率评分 (a—c),集合预报一致性评分 (d—f) 和离群值评分 (g—h) 随预报时间的演变 (a、d、g. 2 m气温,b、e、h. 2 m湿度,c、f、i. 10 m风速)
Figure 7. Evolutions of continuous ranked probability score (a—c),ensemble prediction consistency score (d—f) and outlier score (g—h) with prediction time in ensemble prediction experiments (a,d,g. 2 m temperature,b,e,h. 2 m humidity,c,f,i. 10 m wind speed)
图 8 7日00时 (a) 和02时 (b) EXP1a集合平均与对照预报的2 m气温差 (色阶,单位:10−1°C)、地面气压差 (绿线,单位:10−1 hPa) 和10 m风差值 (单位:10−1 m/s)
Figure 8. Differences in 2 m temperature (shaded,unit:10−1℃),surface pressure (green lines,unit:10−1 hPa) and 10 m wind (unit:10−1 m/s) between the ensemble average of EXP1a and control experiment forecast at 00:00 BT (a) and 02:00 BT 7 May (b)
图 10 7日04时 (a) 和06时 (b) EXP1b集合平均与对照预报的2 m气温差 (色阶,单位:10−1°C)、地面气压差 (绿线,单位:10−1 hPa) 和10 m风速差 (单位:10−1 m/s)
Figure 10. Differences in 2 m temperature (shaded,unit:10−1℃),surface pressure (green lines,unit:10−1 hPa) and 10m wind (unit:10−1 m/s) between the ensemble average of EXP1b and control experiment forecast at 04:00 BT (a) and 06:00 BT 7 May (b)
图 12 HP_a与LP_a (a),HP_b与LP_b (b) 的水凝物混合比之差时空平均的垂直廓线 (Qr:雨,Qv:水汽,Qc:云水,Qi:云冰,Qs:雪,Qg:霰)
Figure 12. Spatiotemporal average vertical profiles of differences in hydrometeor mixing ratio between HP_a and LP_a (a) and between HP_b and LP_b (b)(Qr:rain,Qv:water vapor,Qc:cloud water,Qi:cloud ice,Qs:snow,Qg:graupel)
表 1 从各参数化过程中选取的物理参数
Table 1. Physical parameters selected from various parameterization schemes
物理过程 序号 代码名称 参数名称 参考值 扰动范围 边界层过程 P1 RLAM 渐近混合长度 150 [30,450] 边界层过程 P2 BRCR 临界理查森数 0.5 [0.125,1] 边界层过程 P3 PFAC 廓线指数 2 [1,3] 边界层过程 P4 CFAC 计算近地层顶Prandtl数的比例系数 7.8 [3.9,15.6] 云微物理过程 P5 AVTR 计算雨滴下落速度的参数 841.9 [420,1263] 云微物理过程 P6 AVTG 计算霰粒子下落速度的参数 330 [165,495] 云微物理过程 P7 AVTS 计算雪粒子下落速度的参数 11.72 [5,18] 云微物理过程 P8 N0R 雨滴谱截距 8×106 [8×105,8×107] 云微物理过程 P9 PEAUT 云滴的碰并效率 0.55 [0.1,0.85] 云微物理过程 P10 DENG 霰粒子密度 500 [100,900] 云微物理过程 P11 N0G 霰粒子谱截距 4×106 [2×105,6×107] 云微物理过程 P12 XNCR 云滴数浓度 3×108 [1×107,1×109] 云微物理过程 P13 DIMAX 云冰的最大直径 5×10−4 [2×10−4,8×10−4] 云微物理过程 P14 R0 云雨自动转换的临界液滴半径 8×10−6 [4×10−6,1.2×10−5] 云微物理过程 P15 QS0 雪霰自动转换的临界混合比 6×10−4 [1×10−4,1×10−3] 表 2 相关云微物理参数的作用
Table 2. Effects of relevant cloud microphysical parameters
序号 代码名称 参数名称 相关的云微物理转换项 P5 AVTR 计算雨滴下落速度的参数 云水碰并雨;雨滴凝结/蒸发;雨滴碰并云冰增长为雪/霰; P6 AVTG 计算霰粒子下落速度的参数 云水碰并霰;霰融化为雨;霰升华/凝华;霰融化并蒸发; P7 AVTS 计算雪粒子下落速度的参数 云水碰并雪;雪融化为雨;雪升华/凝华;雪融化并蒸发; P8 N0R 雨滴谱截距 云水碰并雨;雨撞冻霰;雨蒸发/凝结;云冰碰并雨滴增长为雪/霰;雨滴碰并云冰增长为雪/霰;雪碰并雨滴增长为霰;雨滴碰并雪增长为雪/霰;雨滴碰并霰; P9 PEAUT 云滴的碰并效率 云雨自动转换; P10 DENG 霰粒子密度 霰融化为雨;云冰碰并霰;云水碰并霰;雨碰并霰;霰升华/凝华;霰融化并蒸发; P11 N0G 霰粒子谱截距 霰融化为雨;云冰碰并霰;云水碰并霰;雨碰并霰;霰升华/凝华;霰融化并蒸发; P12 XNCR 云滴数浓度 云雨自动转换;云水异质冻结核化; P13 DIMAX 云冰的最大直径 云冰碰并雨增长为雪/霰;云冰碰并雪;云冰碰并霰;云冰升华/凝华;冰雪自动转换; P14 R0 云雨自动转换的临界液滴半径 云雨自动转换; P15 QS0 雪霰自动转换的临界混合比 雪霰自动转换; 表 3 模式积分时间的设置
Table 3. Setting of mode integration time
降水阶段 起报时间 研究时段 A阶段 2017年5月6日20时 2017年5月7日00—04时 B阶段 2017年5月6日20时 2017年5月7日04—08时 表 4 A阶段的CNOP-P求解结果
Table 4. CNOP-P solution results at stage A
迭代步数 CNOP-P对应的参数敏感性排序 目标函数 谱投影梯度 0 P2 P13 P10 P8 P4 P11 P1 P7 P14 P3 P15 P6 P12 P5 P9 −363789.86 5.14×10−1 1 P1 P5 P8 P6 P14 P12 P9 P11 P13 P15 P2 P7 P10 P4 P3 −1890936.43 1.05×10− 2 P1 P5 P8 P6 P7 P12 P14 P9 P2 P3 P10 P11 P4 P13 P15 −2428671.82 2.37×10−2 3 P1 P6 P12 P7 P5 P8 P9 P14 P2 P10 P3 P13 P4 P11 P15 −2444782.84 5.747×10−4 4 P1 P6 P7 P12 P8 P9 P14 P2 P10 P5 P3 P4 P13 P11 P15 −2396353.53 1.05×10−5 5 P1 P6 P7 P12 P9 P8 P14 P10 P2 P5 P13 P3 P4 P11 P15 −2412798.59 5.95×10−6 6 P1 P6 P7 P12 P9 P8 P14 P10 P2 P5 P3 P13 P4 P11 P15 −2407900.10 8.83×10−6 7 P1 P6 P7 P12 P9 P8 P14 P10 P2 P5 P3 P13 P4 P11 P15 −2364014.09 3.32×10−6 8 P1 P6 P7 P12 P9 P8 P14 P2 P10 P5 P3 P13 P4 P11 P15 −2377794.96 6.63×10−6 9 P1 P6 P7 P12 P9 P8 P14 P2 P10 P5 P3 P13 P4 P11 P15 −2369254.15 6.74×10−6 10 P1 P6 P7 P12 P9 P8 P14 P2 P10 P5 P3 P13 P4 P11 P15 −2388536.83 6.16×10−6 表 5 B阶段的CNOP-P求解结果
Table 5. CNOP-P solution results at stage B
迭代步数 CNOP-P对应的参数敏感性排序 目标函数 谱投影梯度 0 P3 P2 P8 P1 P7 P13 P4 P9 P5 P10 P11 P15 P14 P12 P6 −280918.01 4.48×10−1 1 P6 P5 P14 P1 P8 P7 P9 P12 P2 P15 P10 P13 P11 P4 P3 −633662.63 1.87×10−2 2 P6 P5 P14 P1 P8 P7 P12 P9 P13 P10 P15 P3 P11 P2 P4 −669609.18 3.12×10−4 3 P5 P6 P14 P1 P8 P7 P9 P12 P10 P13 P15 P11 P3 P2 P4 −687939.93 1.44×10−4 4 P5 P6 P14 P1 P8 P7 P12 P9 P10 P13 P15 P3 P11 P2 P4 −683710.55 2.39×10−4 5 P5 P6 P14 P1 P8 P7 P9 P12 P10 P13 P15 P3 P11 P2 P4 −686132.89 6.26×10−5 6 P5 P6 P14 P1 P8 P7 P9 P12 P10 P13 P15 P3 P11 P2 P4 −692323.12 5.79×10−5 7 P5 P6 P14 P1 P8 P7 P9 P12 P10 P13 P15 P3 P11 P2 P4 −677959.37 1.37×10−4 8 P5 P6 P14 P1 P8 P7 P9 P12 P10 P13 P15 P3 P11 P2 P4 −680330.13 8.11×10−5 9 P5 P6 P14 P1 P8 P7 P9 P12 P10 P13 P15 P3 P11 P2 P4 −683884.21 1.36×10−5 10 P5 P6 P14 P1 P8 P7 P9 P12 P10 P13 P15 P3 P11 P2 P4 −687757.99 1.80×10−5 表 6 确定关键参数个数的对比试验设置
Table 6. Settings of comparative experiments to determine the number of key parameters
试验序号 降水阶段 扰动参数个数 扰动参数序号 EXPa_2 A阶段 2 P1 P6 EXPa_4 A阶段 4 P1 P6 P7 P12 EXPa_6 A阶段 6 P1 P6 P7 P12 P9 P8 EXPa_8 A阶段 8 P1 P6 P7 P12 P9 P8 P14 P2 EXPa_10 A阶段 10 P1 P6 P7 P12 P9 P8 P14 P2 P10 P5 EXPa_12 A阶段 12 P1 P6 P7 P12 P9 P8 P14 P2 P10 P5 P3 P13 EXPa_15 A阶段 15 P1 P6 P7 P12 P9 P8 P14 P2 P10 P5 P3 P13 P4 P11 P15 EXPb_2 B阶段 2 P6 P5 EXPb_4 B阶段 4 P6 P5 P14 P1 EXPb_6 B阶段 6 P6 P5 P14 P1 P8 P7 EXPb_8 B阶段 8 P6 P5 P14 P1 P8 P7 P9 P12 EXPb_10 B阶段 10 P6 P5 P14 P1 P8 P7 P9 P12 P10 P13 EXPb_12 B阶段 12 P6 P5 P14 P1 P8 P7 P9 P12 P10 P13 P15 P11 EXPb_15 B阶段 15 P6 P5 P14 P1 P8 P7 P9 P12 P10 P13 P15 P11 P3 P2 P4 表 7 对流尺度集合预报试验设置
Table 7. Settings of convective scale ensemble prediction experiments
试验序号 降水阶段 模式扰动方案 扰动参数序号 EXP1a A阶段 CNOP-P-RP P1 P6 P7 P12 P9 P8 P14 P2 EXP1b B阶段 CNOP-P-RP P6 P5 P14 P1 P8 P7 P9 P12 EXP2a A阶段 SPPT / EXP2b B阶段 SPPT / 表 8 强降水中心4 h累计降水量对比 (单位:mm)
Table 8. Comparison of 4 h cumulative precipitation at heavy precipitation center (unit:mm)
降水阶段 观测值 对照预报 集合平均 强降水成员均值 弱降水成员均值 A 97.2 56.6 63.1 80.8 34.8 B 63.3 18.0 45.3 97.5 16.5 -
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