华南区域高分辨率数值模式前汛期预报初步评估

PRELIMINARY EVALUATION OF FORECAST SKILL OF GRAPES GUANGZHOU REGIONAL MODELING SYSTEM

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;Z;1 km模式和GRAPES;Z 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;Z;1 km模式的降水预报技巧优于GRAPES;Z 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;Z;1 km模式的TS评分是GRAPES;Z 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;Z;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;Z;1 km模式对2 m气温预报结果优于GRAPES;Z 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;Z 3 km模式在风场预报上表现更好。GRAPES;Z;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;Z;1 km模式的预报产品具有较好的参考价值。

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.