摘要
高质量、高时空分辨率的网格降水产品对于智能网格预报、水循环、地气相互作用以及防灾减灾具有重要意义。国家气象信息中心对原三源降水CMPAS(CMA Multi-source merged Precipitation Analysis System)进行了升级,进一步融合了质控后的国家级非考核站降水信息,研发了新的三源降水产品CMPAS_new。利用中国气象局的站点观测数据和水利部降水观测数据对CMPAS和CMPAS_new降水产品进行检验评估,并与CMORPH、GPM降水产品进行对比。结果表明:CMORPH、GPM、CMPAS和CMPAS_new降水产品都能够合理地反映出中国地区降水的空间分布;使用中国气象局逐小时站点观测进行检验评估,从误差时间序列可以看出,融合了非考核站降水观测的CMPAS_new优于CMPAS;使用水利部日降水观测数据进行检验评估,从误差时间序列、均方根误差空间分布可以看出,CMPAS_new优于其他三种降水产品,CMPAS优于GPM,GPM优于CMORPH,未来可将三源降水的背景场CMORPH替换为GPM。从个例分析看,CMPAS_new较好地监测到了四川中北部地区的降水,在广东省惠东县高潭镇强降水监测上,CMPAS_new与站点观测更为接近,能够较好地反映出高潭镇此次极端降水。
The grid precipitation products of high-quality and high-temporal resolution are important for smart grid forecasting,hydrological cycle,land-atmosphere interaction and disaster prevention and mitigation.Based on the original three-source merged precipitation CMPAS,the national Meteorological Information Center developed a new CMPAS precipitation(CMPAS_new)by integrating the precipitation informatioal with the national non-assessment station after quality control.CMPAS precipitation and CMPAS_new precipitation were evaluated by the China Meteorological Administration(CMA)precipitation observation data and the precipitation observation data of the Ministry of Water Resources(MWR)respectively,and then be compared with CMORPH precipitation and GPM precipitation.The results show that the above four kinds of precipitation can reasonably reflect the spatial distribution of precipitation.Besides,CMPAS_new precipitation performs better than CMPAS precipitation from the error time series by using the hourly site observation of CMA,and it also performs best from the error time series and the spatial distribution of RMSE by using the daily precipitation observation data of MWR,followed by CMPAS precipitation,GPM precipitation and CMORPH precipitation.In addition,CMPAS_new precipitation can better reflect precipitation in the central and northern Sichuan Province while it is closer to the site observation on the monitoring of heavy precipitation in Gaotan Town,Guangdong Province.
作者
孙帅
师春香
潘旸
谷军霞
白磊
苏传程
韩帅
孙金森
SUN Shuai;SHI Chunxiang;PAN Yang;GU Junxia;BAI Lei;SU Chuancheng;HAN Shuai;SUN Jinsen(National Meteorological Information Center,Beijing 100081,China;Guangxi Meteorological Information Center,Nanning 530022,China;Zhucheng Meteorological Administration of Shandong Province,Zhucheng 262200,China)
出处
《水文》
CSCD
北大核心
2020年第6期10-15,23,共7页
Journal of China Hydrology
基金
国家重点研发计划项目(2018YFC1506601)
国家自然科学基金重点项目(91437220)
中国气象局项目“气象资料质量控制及多源数据融合与再分析”。
关键词
降水
CMORPH
GPM
CMPAS
非考核站
precipitation
CMORPH precipitation
GPM precipitation
CMPAS precipitation
non-assessment station