多源融合实况分析1km网格降水产品在广东省暴雨过程中的准确性评估

Assessment of multi-source observation merged 1 km-grid precipitation product during the disastrous rainstorms in Guangdong

中国国家气象信息中心研制的多源融合实况分析1 km网格降水(简称为ART_(1) km降水)产品可为灾害性天气监测预警、智能网格预报、智慧气象服务等提供更精细的数据支撑。选取广东省2019-2022年5-8月的20例致灾暴雨过程,利用未融合制作ART_(1) km降水产品的区域站和水文站等两类独立站的降水资料,评估ART_(1)km降水产品在暴雨过程中的准确性。结果表明:ART_(1) km降水产品成功反映了广东省暴雨过程的降水落区、强度和变化趋势,且在珠三角、粤东东部和粤北北部表现最佳。降水越强,两类独立站的ART_(1) km降水和观测降水的相关性越高,但均方根误差和平均值误差也越大。降水不分级时,全省约60%的独立站的ART_(1) km降水和观测降水的相关系数≥0.8,超90%的独立站的均方根误差在[1.0,5.0]mm范围内,超60%的独立站的平均值误差在±0.1 mm内。降水分级后,当小时降水较弱(<5 mm),全省大部分独立站的相关系数<0.5、均方根误差在[1.0,5.0]mm范围内、平均值误差在[0.0,0.5]mm范围内;当小时降水较强(≥20 mm),全省42%~56%的独立站的相关系数≥0.5,大部分独立站的均方根误差≥10 mm、平均值误差<0 mm(且当小时降水≥50 mm,平均值误差<-10 mm)。两类独立站的ART_(1) km降水总体表现为低估,更多的站点观测融入制作ART_(1) km降水产品,将有助于提升该产品的质量。

This paper aims to assess the latest 1 km-grid Analysis Real Time(ART_(1) km)precipitation product developed by the National Meteorological Information Center of China Meteorological Administration(CMA),which can provide great support for disaster weather moni⁃toring and warning,intelligent grid forecasting and weather services.Observed precipitation data from the independent stations(including non-uploaded regional meteorological stations and hydrometric stations)that were not integrated into the ART_(1) km precipitation product as well as precipitation classification inspection are used to assess the quality of this product during twenty disastrous rainstorm cases from May to August during 2019-2022 in Guangdong.The results show that the ART_(1) km precipitation product successfully reproduces the precipita⁃tion location,strength,and trends in these cases,with the best performance in the Pearl River Delta,the east of eastern Guangdong,and the north of northern Guangdong.The stronger the precipitation,the greater the correlation as well as the root mean square error(RMSE)and mean error(ME)between the ART_(1) km precipitation and the observed precipitation.When the hourly precipitation is not classified,about 60% of these independent stations present a correlation efficient≥0.8,more than 90% of the stations present an RMSE within the range of[1.0,5.0]mm,and more than 60%of the stations present a ME within±0.1 mm.When the hourly precipitation is<5 mm,most of the sta⁃tions have a correlation efficient<0.5,an RMSE within the range of[1.0,5.0]mm,and a ME within[0.0,0.5]mm.When the hourly precipi⁃tation is≥20 mm,42%~56%of the stations have a correlation efficient≥0.5,and most of the stations have an RMSE≥10 mm and a ME<0 mm,even when the hourly precipitation is≥50 mm,most of the stations have a ME<-10 mm.Overall,ART_(1) km precipitation is usually underestimated at the independent stations,and integrating observations from more sites into producing ART_(1) km precipitation is helpful to improve the quality of the products.