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Comparative Analysis on Precipitation Forecasting Capabilities of Two Ensemble Prediction Systems Around Qinling Area
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摘要: 利用欧洲中期天气预报中心 (ECMWF)、美国大气环境预报中心 (NCEP) 集合预报系统 (EPS) 降水量预报资料，CMORPH (NOAA Climate Prediction Center Morphing Method) 卫星与全国3万个自动气象站降水量融合资料，基于技巧评分、ROC (relative operating characteristic) 分析等方法，对比两个集合预报系统对秦岭及周边地区的降水预报性能。结果表明:两个系统均能较好表现降水量的空间形态，对于不同量级降水，ECMWF集合预报系统0~240 h控制及扰动预报优于NCEP集合预报系统，但NCEP集合预报系统264~360 h预报时效整体表现更好; ECMWF集合预报系统0~120 h大雨集合平均优于NCEP集合预报系统，两个系统集合平均的预报技巧整体低于其控制及扰动成员预报，这种现象ECMWF集合预报系统表现更为显著; ECMWF集合预报系统降水预报概率优于NCEP集合预报系统。ROC分析显示，随着预报概率的增大，ECMWF集合预报系统在命中率略微下降的情况下，显著减小了空报率，NCEP集合预报系统则表现出高空报、高命中率。Abstract: Using precipitation forecast data of ECMWF and NCEP ensemble prediction systems, hourly rainfall data fusion by CMORPH (NOAA Climate Prediction Center Morphing Method) satellites and 30000 automatic weather stations, the precipitation and precipitation probability forecasting capability of ECMWF and NCEP ensemble prediction systems around Qinling area are comparatively analyzed from June to October in 2013 and 2014, mainly based on classic skill score and ROC (relative operating characteristic) statistical method. Results show that the precipitation spatial distribution pattern can be better described by both ECMWF and NCEP ensemble prediction systems with the disadvantages that forecasted high value center is larger and the precipitation amplitude is small; the correlation coefficient of ECMWF control forecast and perturb forecast with observations is higher, the standard deviation ratio is close to 1.0 in previous 10 days, which is better than NCEP, but NCEP forecast skill score has better performance than ECMWF in 264-360 hours.The ensemble mean skill score for heavy rain of ECMWF ensemble prediction system is better than NCEP in 0-120 hours. The forecast skill score of ensemble mean is lower than control forecast and perturb forecast for both two systems. Ensemble mean significantly reduces the standard deviation of precipitation amplitude and this is not conducive to the accuracy of synoptic scale precipitation prediction. Ensemble mean significantly increases forecast bias of light rain and increases the false rate, while the forecast bias of heavy rain and the fail rate decrease. This phenomenon is more remarkable when ensemble mean contains more perturb members and forecast skill is roughly the same between different members, and this makes ECMWF ensemble mean skill scores for light rain lower than NCEP.Overall, ECMWF probability forecast effect is better than NCEP. When precipitation threshold increases, BS score of both two models increases sharply while the forecast capacity significantly reduces, for storms, the ROC area is smaller than climate probability sometimes. The ROC analysis show that as the forecast probability improves, ECMWF ensemble prediction system slightly decreases the hit rate and significantly reduces the false rate, however, NCEP have a high false rate and higher hit rate. So depending on user's requirements, different model can be chosen as reference.
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图 1 秦岭地区日平均降水量 (填色) 及其标准差 (等值线, 单位: mm)
Fig. 1 bserved daily mean precipitation (the shaded) and its standard deviation (the isoline, unit: mm)
图 2 两个集合预报系统的日平均降水量 (填色) 及其标准差 (等值线, 单位: mm) (a) ECMWF系统控制预报, (b) ECMWF集合平均, (c) NCEP系统控制预报, (d) NCEP系统集合平均
Fig. 2 Forecasted daily mean precipitation (the shaded) and its standard deviation (the isoline, unit: mm) during study period (a) ECMWF control forecast, (b) ECMWF ensemble mean, (c) NCEP control forecast, (d) NCEP ensemble mean
图 3 扰动成员、控制预报和观测的日平均降水量和标准差 (阴影为对应集合预报系统扰动成员的振幅范围)
(a) ECMWF系统平均降水量, (b) NCEP系统平均降水量, (c) ECMWF系统降水量标准差, (d) NCEP系统降水量标准差
Fig. 3 Daily mean precipitation and its standard deviation of perturbation members, control forecast and observation (the shaded denotes amplitude of perturbation members of corresponding ensemble prediction system)
(a) daily mean precipitation of ECMWF, (b) daily mean precipitation of NCEP, (c) standard deviation of ECMWF, (d) standard deviation of NCEP
图 4 扰动预报、控制预报、集合平均的降水预报技巧评分 (阴影为对应集合预报系统扰动成员的振幅范围)
(a) ECMWF系统小雨预报偏差, (b) NCEP系统小雨预报偏差, (c) ECMWF系统小雨预报TS评分, (d) NCEP系统小雨预报TS评分, (e) ECMWF系统大雨预报偏差, (f) NCEP系统大雨预报偏差, (g) ECMWF系统大雨预报TS评分, (h) NCEP系统大雨预报TS评分
Fig. 4 Skill scores of perturbation members, control forecast and ensemble mean for daily precipitation forecasting of ECMWF and NCEP (the shaded denotes amplitude of perturbation members of corresponding ensemble prediction system)
(a) ECMWF forecast bias of light rain, (b) NCEP forecast bias of light rain, (c) ECMWF TS score of light rain, (d) NCEP TS score of light rain, (e) ECMWF forecast bias of heavy rain, (f) NCEP forecast bias of heavy rain, (g) ECMWF TS score of heavy rain, (h) NCEP TS score of heavy rain
图 5 降水预报技巧评分的空间分布 (填色表示控制预报，等值线表示集合平均)
(a) ECMWF系统小雨预报偏差, (b) NCEP系统小雨预报偏差, (c) ECMWF系统小雨TS评分, (d) NCEP系统小雨TS评分
Fig. 5 he spatial distribution of precipitation forecast skill scores for ECMWF and NCEP (the shaded denotes the control forecast, the isoline denotes the ensemble mean)
(a) ECMWF forecast bias of light rain, (b) NCEP forecast bias of light rain, (c) ECMWF TS score of light rain, (d)NCEP TS score of light rain
图 6 不同量级降水的Brier评分及ROC面积
(a) ECMWF系统预报的BS评分, (b) NCEP系统预报的BS评分, (c) ECMWF系统预报的ROC面积, (d) NCEP系统预报的ROC面积
Fig. 6 Brier score and ROC area for different magnitude rainfall
(a) ECMWF BS score, (b) NCEP BS score, (c) ECMWF ROC area, (d) NCEP ROC area
图 7 不同预报时效ECMWF和NCEP系统小雨、大雨预报的ROC曲线图
(a) ECMWF系统小雨预报和ROC曲线, (b) NCEP系统小雨预报ROC曲线, (c) ECMWF系统大雨预报ROC曲线, (d) NCEP系统大雨预报ROC曲线
Fig. 7 ROC charts of light rain and heavy rain for ECMWF and NCEP
(a) ROC chart of light rain for ECMWF, (b) ROC chart of light rain for NCEP, (c) ROC chart of heavy rain for ECMWF, (d) ROC chart of heavy rain for NCEP
表 1 ECMWF和NCEP系统24 h累积降水量预报与观测ACC和σ
Table 1 The standard deviation ratio and abnormal correlation coefficient of 24-hour accumulated precipitation between the forecast and observation at different forecast period
预报时效/h ECMWF系统控制预报 ECMWF系统集合平均 NCEP系统控制预报 NCEP系统集合平均 ACC σ ACC σ ACC σ ACC σ 24 0.728 0.892 0.681 0.812 0.718 0.815 0.707 0.777 48 0.712 1.003 0.649 0.829 0.703 0.938 0.684 0.877 72 0.693 1.030 0.605 0.769 0.682 1.047 0.652 0.929 96 0.683 0.954 0.598 0.693 0.684 1.093 0.644 0.919 120 0.691 0.902 0.589 0.600 0.683 1.067 0.623 0.807 144 0.678 0.891 0.570 0.562 0.662 1.041 0.592 0.711 168 0.652 0.924 0.546 0.536 0.644 0.994 0.565 0.654 192 0.653 0.879 0.533 0.461 0.645 1.023 0.557 0.619 216 0.641 1.003 0.497 0.472 0.637 1.019 0.536 0.648 240 0.620 0.829 0.484 0.407 0.619 0.956 0.530 0.551 264 0.528 1.177 0.486 0.473 0.612 0.923 0.501 0.479 288 0.601 1.040 0.447 0.416 0.611 1.029 0.469 0.517 312 0.591 1.135 0.432 0.427 0.600 1.081 0.449 0.526 336 0.588 1.098 0.426 0.402 0.582 0.994 0.436 0.499 360 0.572 1.110 0.417 0.429 0.588 1.194 0.426 0.601 
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