摘要
高质量的水文气象观测数据是开展气象和水文灾害监测、预报预警及长期气候变化趋势分析的基本支撑。针对流域尺度内现有融合降水数据空间分辨率低的问题,通过集合卡尔曼滤波(EnKF)融合算法对清江流域33个地面站点和TRMM及CMORPH两种卫星产品的降水数据在日尺度上进行融合,得到了0.05°×0.05°的清江流域融合降水数据MSAP。利用留一交叉验证的方式对卫星降水、地面插值和MSAP融合降水数据进行定量分析,证明EnKF融合算法能从相关性系数R、平均绝对误差MAE和均方根误差RMSE 3个方面改善清江流域降水的精度,并且融合算法改善了卫星数据和地面数据在流域边界部分区域精度低的缺点,展现了EnKF融合算法在降水数据融合中的应用潜力。进一步地,将MSAP与CMFD、ERA5和MSWEP等3种主流再分析数据进行对比,并对1998年汛期清江流域与长江干流四次洪峰遭遇中最大的两次遭遇所对应的场次强降水进行了空间分析。结果表明,在时间尺度上,MSAP数据具有最高的R和最小的MAE及RMSE;在误差的空间分布上,MSAP数据在各站点精度评价指标的空间差异最小,四种再分析数据精度由高到低排序为MSAP>CMFD>MSWEP>ERA5;在历时5日和历时2日的场次强降水过程中,CMFD、MSWEP和MSAP均能在一定程度上反映出暴雨中心,在空间分布和降水量级上,MSAP和CMFD基本保持一致。
High-quality hydrometeorological observation data is the basic support for meteorological and hydrological disaster monitoring,forecasting and warning,and long-term climate change trend analysis.To solve the problem of low spatial resolution of existing integrated precipitation data at the watershed scale,this paper uses ensemble Kalman Filter(EnKF)fusion algorithm to merge precipitation data from 33 ground stations and TRMM and CMORPH satellite products at the daily scale in Qingjiang River Basin,yielding the 0.05°×0.05°fusion precipitation production of Qingjiang River Basin i.e.,MSAP.The Leave-One-Out Cross-Validation method is used to quantitatively ana⁃lyze the satellite precipitation data,ground interpolation data and MSAP fused precipitation data.It is proved that the EnKF fusion algorithm can improve the precision of precipitation in Qingjiang River Basin from three aspects:correlation coefficient R,mean absolute error MAE and root mean square error RMSE.In addition,the fusion algorithm overcame the shortcomings of low accuracy of satellite data and ground data in part of the watershed boundary area,which shows that the EnKF fusion algorithm has application potential in precipitation data fu⁃sion.Furthermore,MSAP is compared with CMFD,ERA5 and MSWEP reanalysis data,and the spatial distribution of heavy precipitation events corresponding to the two largest flood peaks in Qingjiang River Basin and Yangtze River during the flood season in 1998 is analyzed.The results also show that the MSAP data has the highest R and the smallest MAE,RMSE in terms of the time scale.In terms of the spatial distribution of errors,the spatial difference of accuracy evaluation index of MSAP data in each site is the smallest,and the order of accuracy of the four kinds of reanalysis data from high to low is MSAP>CMFD>MSWEP>ERA5.CMFD,MSWEP and MSAP can reflect the center of rainstorm to some extent in the process of 5-day and 2-day heavy precipitation events.In terms of spatial distribution and precipitation amount,MSAP and
作者
郭家力
郭东淏
丁光旭
李颖
李英海
杨旭
张海荣
GUO Jia-li;GUO Dong-hao;DING Guang-xu;LI Ying;LI Ying-hai;YANG Xu;ZHANG Hai-rong(Hubei Key Laboratory of Construction and Management in Hydropower Engineering,China Three Gorges University,Yichang 443002,Hubei Province,China;College of Hydraulic&Environmental Engineering,China Three Gorges University,Yichang 443002,Hubei Province,China;Hubei Key Laboratory of Intelligent Yangtze and Hydroelectric Science,China Yangtze Power Co.,Ltd.,Yichang 443133,Hubei Province,China)
出处
《中国农村水利水电》
北大核心
2023年第5期72-78,84,共8页
China Rural Water and Hydropower
基金
国家自然科学基金项目(52179018,52009065,51909010)
智慧长江与水电科学湖北省重点实验室(中国长江电力股份有限公司)开放基金(ZH2002000103)
宜昌市自然科学基金项目(A20-3-005)。
关键词
清江流域
多源数据融合
卫星降水产品
集合卡尔曼滤波
再分析降水
Qingjiang River Basin
multi-source data fusion
satellite-based precipitation production
EnKF
reanalysis precipitation
