STUDY ON IMPACTS OF DYNAMIC DOWNSCALING AND MULTI-PHYSICAL PARAMETERIZATION SCHEME COMBINATION ON ENSEMBLE FORECAST OF ANNUALLY FIRST RAINY SEASON IN SOUTH CHINA
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摘要: 针对华南前汛期降水过程，基于全球集合预报系统（GEFS）资料，利用WRF中尺度模式以及GEFS动力降尺度获取的区域集合预报初值场，通过多物理过程参数化方案组合和模式积分方法实现华南前汛期降水的区域集合预报。对2019年5月15日—6月15日共32天的华南前汛期降水过程进行了单一物理过程区域集合预报（REFS_SINGLE）和多物理过程区域集合预报（REFS_MULTI）的数值模拟批量敏感性试验，通过GEFS、REFS_SINGLE和REFS_MULTI的对比分析，探讨多物理过程参数化方案组合对华南前汛期降水的影响，同时利用一次华南前汛期暴雨过程进一步探讨集合预报试验的预报效果。结果表明：（1）REFS集合平均的预报效果明显好于控制性预报。（2）REFS降水集合离散度与预报误差的对应关系好于GEFS。（3）积分48小时后，REFS_MULTI和REFS_SINGLE的扰动能量分别是GEFS的4.7倍和6.3倍。（4）降水级别越大，REFS的TS评分效果就越好于GEFS；REFS_MULTI略微好于REFS_SINGLE。（5）基于32天的批量试验，REFS的AUC值有28天大于GEFS，REFS_MULTI有22天大于REFS_SINGLE，表明REFS的预报技巧好于GEFS，且REFS_MULTI的预报技巧好于REFS_SINGLE。Abstract: Based on the Global Ensemble Forecast System (GEFS) data, the WRF model and the GEFS dynamic downscaling method are used to obtain the regional ensemble forecast initial states. And through the combination of multi-physical process parameterization schemes and model integration method, the regional ensemble forecast of precipitation in the annually first rainy season in south China is realized. A batch test of single physical process regional ensemble forecast system (REFS_SINGLE) and multi-physical process regional ensemble forecast system (REFS_MULTI) are carried out for the 32-day precipitation period in south China from May 15 to June 15, 2019. Through the comparative analysis of GEFS, REFS_SINGLE and REFS_MULTI, the influence of multi-physical process parameterization scheme combination on precipitation in the annually first rainy season in south China is explored. Meanwhile, a precipitation case in the annually first rainy season in south China is used to further explore the forecasting performance of different ensemble forecasts. The results are as follows. (1) The ensemble mean of REFS is significantly better than that of the control forecast. (2) The correspondence between the precipitation ensemble spread and the forecast error of the REFS is better than that between the precipitation ensemble spread and the forecast error of the GEFS. (3) After 48h, the perturbation energy of the REFS_MULTI and the REFS_SINGLE are 4.7 times and 6.3 times that of the GEFS, respectively. (4) The higher the precipitation level, the higher the TS score of the REFS than that of the GEFS; the REFS_MULTI is slightly better than the REFS_SINGLE. (5) Based on the 32-day batch test, the AUC value of REFS is greater than that of GEFS for 28 days, and REFS_MULTI is greater than REFS_SINGLE for 22 days, indicating that the forecasting skills of REFS are better than that of GEFS, and the forecasting skills of REFS_MULTI are better than that of REFS_SINGLE.
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图 5 2019年6月11日08时起报的三组方案控制性预报在24~48 h的预报及实况24 h累积降水分布图
a. REFS_MULTI方案；b. REFS_SINGLE方案；c. GEFS方案；d.实况。单位：mm。
图 7 2019年6月11日08时起报的三组集合预报方案24~48 h集合平均预报（阴影）和降水的离散度（等值线）
a. REFS_MULTI；b. REFS_SINGLE；c. GEFS
图 10 2019年6月11日14时(a)、11日20时(b)、12日08时(c)、13日08时(d)起报的三组集合方案(REFS_MULTI (左)、REFS_SINGLE (中)、GEFS (右))在500 hPa扰动能量随预报时效的演变
图 11 2019年06月11日08时起报的三组集合方案(REFS_MULTI (左)、REFS_SINGLE (中)、GEFS (右))第36 h的500 hPa风场集合平均扰动与预报误差散点图
图 12 不同预报时效500 hPa的U分量风Talagrand分布
从上到下分别为REFS_MULTI、REFS_SINGLE和GEFS方案, 从左到右分别为0 h、12 h、24 h、36 h和48 h预报时效。
表 1 多物理参数化方案
方案 方案名称 代码 云微物理参数化方案 WDM6 1 Thompson 2 Morrison 3 积云对流参数化方案 Kain-Fritsch 1 BMJ 2 Grell-Freitas 3 边界层参数方案 YSU 1 MYJ 2 MYNN2 3 陆面过程参数化方案 Thermal Diffusion 1 Noah 2 CLM4 3 长波辐射参数方案 RRTMG 1 CAM 2 GFDL 3 短波辐射参数化方案 RRTMG 1 CAM 2 GFDL 3 
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表 2 21个集合成员物理参数化方案组合
成员编号 代码组合 00 1，1，1，1，1，1 01 2, 1, 1, 1, 1, 3 02 3，1，1，1，1，2 03 1，2，1，1，2，1 04 2，2，1，1，2，3 05 3, 2，1，1，2, 2 06 1，3，1，1，3，1 07 2, 3, 2, 2, 3, 3 08 3, 3, 2, 2, 3, 2 09 1，1，2, 2，1，1 10 2，1，2，2，1，3 11 3，1，2, 2，1，2 12 1，2，2，2，2，1 13 2, 2, 2, 2, 2, 3 14 3, 2, 3, 3, 2, 2 15 1，3，3，3，3，1 16 2，3，3，3，3，3 17 3，3，3，3，3，2 18 1，1，3, 3，1，1 19 2，1，3, 3，1，3 20 3，1，3, 3，1，2 注：代码组合顺序：云微物理参数化方案，积云参数化方案，边界层参数化方案，陆面过程参数化方案，长波辐射参数化方案，短波辐射参数化方案。
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表 4 142019年5月15日-6月15日逐日08时起报不同集合预报方案24~48 h不同降水类型集合平均累积降水TS评分平均值
试验 小雨 中雨 大雨 REFS_MULTI 0.3583 0.2084 0.1019 REFS_SINGLE 0.3525 0.2018 0.1006 GEFS 0.3242 0.1513 0.0176
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