PRELIMINARY EVALUATION OF FORECAST SKILL OF GRAPES GUANGZHOU REGIONAL MODELING SYSTEM
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摘要: 基于华南地区自动站逐小时观测资料, 采用传统站点评分、邻域法等评估华南区域高分辨率数值模式(包括GRAPES_GZ_R 1 km模式和GRAPES_GZ 3 km模式)对降水、地面温度和风场等要素的预报能力。结果表明: GRAPES_GZ_R 1 km模式的降水预报技巧优于GRAPES_GZ 3 km模式, 模式预报以正偏差为主。对于不同起报时间的预报, 00时(世界时, 下同)起报的预报效果优于12时。GRAPES_GZ_R 1 km模式的TS评分是GRAPES_GZ 3 km模式的两倍以上, 对不同降水阈值的评分均较高。分数技巧评分(FSS)显示GRAPES_GZ_R 1 km模式6 h累计降水预报在0.1 mm、1 mm及5 mm以上的降水均可达到最低预报技巧尺度, 对所检验降水对象的空间位置把握能力更好。2 m气温和10 m风速检验结果表明两个模式均能较好把握广东省温度的分布特征, GRAPES_GZ_R 1 km模式对2 m气温预报结果优于GRAPES_GZ 3 km模式, 预报绝对误差更小; 两个模式对风速的预报整体偏强, 预报偏差在1~4 m/s之间, 但相比之下GRAPES_GZ 3 km模式在风场预报上表现更好。GRAPES_GZ_R 1 km模式的2 m气温和10 m风速预报偏差随降水过程存在明显波动, 强降水过后温度预报整体偏低, 风速预报偏强, 在模式产品订正、使用等需要考虑模式对主要天气系统的预报情况。总的来说, GRAPES_GZ_R 1 km模式的预报产品具有较好的参考价值。Abstract: Based on hourly observational data from automatic stations in south China, traditional site scoring and neighborhood methods are used to evaluate the forecast skill of GRAPES Guangzhou Regional Modeling System(including GRAPES_GZ_R 1 km model and GRAPES_GZ 3 km model) in forecasting precipitation, surface temperature, and wind fields. The analysis of the results shows that the precipitation forecasting skills of the GRAPES_GZ_R 1 km model are better than those of the GRAPES_GZ 3 km model, and the deviation of GRAPES_GZ_R 1 km forecasts from observations is mainly positive. The Threat Score(TS) of rainfall forecast by GRAPES_GZ_R 1 km is significantly improved at all thresholds and is more than twice that of GRAPES_GZ 3 km. But GRAPES_GZ_R 1 km only has the highest score in the first 3 hours, while its TS decreases gradually with the increase of integration time and precipitation threshold. The Fraction Skill Scores(FSS) of GRAPES_GZ_R 1 km show improvement in both 6 h and24 h accumulated precipitation forecast. Meanwhile, GRAPES_GZ_R 1 km can achieve the lowest forecast skill scale for rainfall above 0.1 mm, 1 mm and 5 mm, while GRAPES_GZ 3 km usually fails to reach the lowest forecast skill scale. The forecast of location and intensity of rainfall has an overall improvement.Daily time evolution of root mean square errors for 2 m temperature forecast are similar, while the amount predicted by GRAPES_GZ_R 1 km is less than that by GRAPES_GZ 3 km. The rainfall and 2 m temperature forecast have made remarkable progresses. However, it is apparent that GRAPES_GZ 3 km performs better in forecasting 10 m wind fields, as mean bias and root mean square errors of 10 m wind field forecast are less than those by GRAPES_GZ_R 1 km. Mean errors are reduced by about 1~2 m/s. In addition, the bias of 2 m temperature and 10 m wind speed forecasts by GRAPES_GZ_R 1 km fluctuates significantly with the precipitation process. The 2 m temperature forecast is generally lower and the 10 m wind speed is stronger than observations. In general, forecast products of GRAPES_GZ_R 1 km have good reference value.
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Key words:
- GRAPES_GZ /
- precipitation forecast /
- verification and evaluation /
- Fraction Skill Score
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图 2 00时实况日平均降水(a)、12时实况日平均降水(b)、00时起报(c~f)和12时起报(g~i)的GRAPES_GZ_R 1 km模式(c、d、g、h)和GRAPES_GZ 3 km模式(e、f、i、j)不同预报时效的过程累积日平均降水和偏差分布
图 3 GRAPES_GZ_R 1 km模式和GRAPES_GZ 3 km模式00时起报(a、c)和12时起报(b、d)的24 h降水预报泰勒图(a、b)和2 m气温预报泰勒图(c、d)
图 4 2019年6月5日—7月14日GRAPES_GZ_R 1 km模式和GRAPES_GZ 3 km模式不同起报时次预报晴雨准确率时间序列及实况24 h累积降水(a~b)、24 h累积降水平均TS评分(c~d)、BIAS评分(e~h)
图 6 GRAPES_GZ_R 1 km模式(a、c)和GRAPES_GZ 3 km模式(b、d)00时(a、b)和12时(c、d)起报的不同阈值、不同邻域半径下6 h累积降水FSS评分热力图
横坐标为阈值，纵坐标为邻域半径，图中同时标注出了FSS评分值。
图 8 2019年6月5日—7月14日观测(第一列)、GRAPES_GZ_R 1 km模式预报(第二列)和GRAPES_GZ 3 km模式预报(第三列)的日平均2 m气温(第一行)和10 m风速(第二行)的分布
图 9 GRAPES_GZ_R 1 km模式和GRAPES_GZ 3 km模式2 m气温(第一列)和10 m风速(第二列)预报平均偏差(预报减去观测)(第一行)和均方根误差(第二行)
阴影区域表示00时和12时计算结果，实线为平均值，灰色柱状为日广东全省平均雨量。
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[1] 陈德辉, 薛纪善, 沈学顺. 中国气象局新一代数值天气预报系统(GRAPES)[C]//海峡两岸气象科学技术研讨会, 2006. [2] 薛纪善, 陈德辉. 数值预报系统GRAPES的科学设计与应用[M]. 北京: 科学出版社, 2008. [3] 陈德辉, 沈学顺. 新一代数值预报系统GRAPES研究进展[J]. 应用气象学报, 2006, 17(6): 773-777. [4] 张人禾, 沈学顺. 中国国家级新一代业务数值预报系统GRAPES的发展[J]. 科学通报, 2008, 53(20): 2 393-2 395. [5] 徐双柱, 张兵, 谌伟. GRAPES模式对长江流域天气预报的检验分析[J]. 气象, 2007, 33(11): 65-71. [6] 祁莉, 马琼, 张文君. GRAPES模式对2011/2012年冬季寒潮天气过程预报能力的检验[J]. 大气科学学报, 2017, 40(6): 791-802. [7] 张进, 麻素红, 陈德辉, 等. GRAPES_TYM改进及其在2013年西北太平洋和南海台风预报的表现[J]. 热带气象学报, 2017, 33(1): 64-73. [8] 林晓霞, 王硕甫, 冯业荣. 近海加强台风"威马逊"(1409)环境条件及结构特征的数值研究[J]. 中山大学学报(自然科学版), 2017, 56(5): 101-111. [9] 钟有亮, 陈静, 王静, 等. GRAPES区域集合预报系统对登陆台风预报的检验评估[J]. 热带气象学报, 2017, 33(6): 953-964. [10] 潘留杰, 张宏芳, 王建鹏, 等. 日本高分辨率模式对中国降水预报能力的客观检验[J]. 高原气象, 2014, 33(2): 483-494. [11] 潘留杰, 张宏芳, 陈小婷, 等. 基于邻域法的高分辨率模式降水的预报能力分析[J]. 热带气象学报, 2015, 31(5): 632-642. [12] ROBERTS N M, LEAN H W. Scale-selective verification of rainfall accumulations from high-resolution forecasts of convective events[J]. Mon Wea Rev, 2008, 136(1): 78-97. [13] 曹越, 赵琳娜, 巩远发, 等. ECMWF高分辨率模式降水预报能力评估与误差分析[J]. 暴雨灾害, 2019, 38(3): 249-258. [14] 陈鹤, 唐杰. ECMWF极端天气指数产品在湖南降水预报中的评估[J]. 河南科学, 2018, 36(8): 1 268-1 273. [15] 朱鹏飞, 邱学兴, 王东勇, 等. ECMWF降水极端天气指数在安徽省的应用评估[J]. 暴雨灾害, 2015, 34(4): 316-323. [16] 季晓东, 漆梁波. ECMWF模式降水预报与极端天气预报指数在暴雨预报中的评估与应用[J]. 暴雨灾害, 2018, 37(6): 566-573. [17] 彭新东, 常燕, 王式功. GRAPES模式对2008年两次强降水过程的数值预报检验[J]. 高原气象, 2010, 29(2): 73-82. [18] 许晨璐, 王建捷, 黄丽萍. 千米尺度分辨率下GRAPES-Meso4.0模式定量降水预报性能评估[J]. 气象学报, 2017, 75(6): 3-28. [19] 徐道生, 陈德辉, 张邦林, 等. TRAMS_RUC_1 km模式初始场和侧边界方案的改进研究[J]. 大气科学, 2020, 44(3): 173-186. [20] TAYLOR K E. Summarizing multiple aspects of model performance in a single diagram[J]. J Geophys Res Atmos, 2001, 106(D7): 7 183-7 192. -
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