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气象:2020,46(3):367-380
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GRAPES_3 km数值模式对流风暴预报能力的多方法综合评估
张小雯,唐文苑,郑永光,盛杰,朱文剑
(国家气象中心,北京 100081)
Comprehensive Evaluations of GRAPES_3 km Numerical Model in Forecasting Convective Storms Using Various Verification Methods
ZHANG Xiaowen,TANG Wenyuan,ZHENG Yongguang,SHENG Jie,ZHU Wenjian
(National Meteorological Centre, Beijing 100081)
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投稿时间:2019-04-22    修订日期:2019-09-13
中文摘要: 利用传统点对点TS评分、邻域法以及对象检验等多种方法,综合评估了GRAPES_3 km模式的对流风暴预报性能,分析了传统检验方法和新型空间检验方法对高分辨率模式评估的适用性和差异性,并同GRAPES_Meso模式的相关结果进行了对比。结果表明:对强对流典型个例的预报评估发现,综合应用多种评估方法能够更全面地评估对流风暴预报,获取模式在对流风暴初生和发展变化过程中的预报性能。使用点对点评分方法,GRAPES_3 km模式对风暴和强风暴的预报都明显优于GRAPES_Meso模式。对于模式不同起报时间的预报,起报时间越新预报效果越好。邻域TS方法考虑了时空偏差,GRAPES_3 km模式20和35 dBz采用时间邻域1 h,空间点对点时预报技巧最高;50 dBz时空偏差较大,时间邻域尺度为3 h技巧最高。分数技巧评分(FSS)显示GRAPES_3 km模式对不同阈值的对流风暴预报均能达到最低技巧尺度,而GRAPES_Meso模式对35 dBz以上的对流风暴基本无预报能力。对象检验可以评估对流风暴特征的预报效果,GRAPES_3 km模式的对流风暴个数预报与实况较为一致,但面积预报明显低估。该模式对β中尺度的对流风暴形态、位置等预报较好,对γ中尺度的对流风暴预报尺度偏大、形状偏圆、轴角偏小,对α中尺度的对流风暴预报尺度偏小、形状偏扁、轴角偏大。GRAPES_Meso模式的对流风暴面积、个数、尺度预报较实况均偏小,位置预报偏差较大,形状预报较实况偏圆、轴角偏小。传统点对点TS评分方法和新型空间检验方法对高分辨率模式对流风暴预报的检验结论一致,依然具有一定的参考价值,但新型空间检验方法能够提供更详细的评估信息。
Abstract:GRAPES_3 km (Global/ Regional Assimilation and Prediction System) is a convection-permitting model, which provides an important objective basis for short-term forecast of severe weather. In this paper, the performance of GRAPES_3 km model in severe weather forecasting was comprehensively evaluated using traditional pixel-versus-pixel threat score, neighborhood and object-based methods. The applicability and differences of the traditional and new spatial verification methods for high resolution model assessment were analyzed, and the results were compared with those of GRAPES_Meso model. The results are showed as follows. Through case analysis, it is found that the characteristics of convective storm forecast and the evolution of convection 〖JP2〗can be comprehensively and objectively evaluated by various verification methods. GRAPES_ 3 km is superior to GRAPES_Meso in forecasting convective storms, especially severe storms over 50 dBz. The latest forecast of the initial forecast time is the best. Neighborhood TS method takes space-time deviation into account.The forecasting skill is the highest when the time neighborhood of GRAPES_3 km model is 1 h for 20 dBz and 35 dBz, and 3 h for 50 dBz. The fractions skill score (FSS) shows that GRAPES_3 km model can achieve the lowest forecast skill scale for convective storms with different thresholds, while GRAPES_Meso model usually fails to reach the lowest skill scale for storms above 35 dBz. Method for object-based diagnostic evaluation (MODE) can be used to evaluate the forecast of convective storm attributes. GRAPES_3 km model is consistent with the actual number of storms at all scales, but the area is obviously underestimated. The model can predict the shape and location of meso-β scale convective storms, while for meso-γ scale convective storms, the forecast scale is larger, the shape is more circular and the axis angle is smaller, but conversely for meso-α scale convective storms. The traditional pixel-versus-pixel verification method and new spatial verification methods have the same conclusion for convection-permitting model. Comparatively, the new spatial verification methods can provide more detailed information of convective storms.
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基金项目:国家重点研发计划(2018YFC1507504和2017YFC1502003)共同资助
引用文本:
张小雯,唐文苑,郑永光,盛杰,朱文剑,2020.GRAPES_3 km数值模式对流风暴预报能力的多方法综合评估[J].气象,46(3):367-380.
ZHANG Xiaowen,TANG Wenyuan,ZHENG Yongguang,SHENG Jie,ZHU Wenjian,2020.Comprehensive Evaluations of GRAPES_3 km Numerical Model in Forecasting Convective Storms Using Various Verification Methods[J].Meteor Mon,46(3):367-380.