Spatiotemporal Accuracy Assessment of Multi-source Fusion Precipitation Product SUPER in the HanJiang River Basin
At present,the multi-source precipitation data fusion technology is mostly based on the correction and fusion of ground rainfall station observations.However,there are still uncertainties in ground observations,especially in areas with a scarcity of ground rainfall stations.The multi-source precipitation fusion technique based on the theory of mathematical uncertainty can determine the errors and inter-correlations of each precipitation dataset without relying on ground observation data and establish optimal clear-rain classification time series for each grid,effectively enhancing the reliability of the fusion products.This study aims to evaluate the performance of the new generation precipitation data optimization fusion product,Statistical Uncertainty analysis-based Precipitation mERging framework(SUPER),developed based on this theory.The assessment is conducted in the upper Hanzhong basin and the middle Guotan basin of the Han River basin.Ground-based high-density rainfall station data along with ECMWF Reanalysis v5(ERA5)reanalysis precipitation data and Integrated Multi-satellitE Retrievals for GPM(IMERG)satellite precipitation products are used to evaluate the accuracy and performance of SUPER at various spatiotemporal scales.The results show that:(1)Compared with ERA5 and IMERG,SUPER product performs better at daily and monthly scales.SUPER has a higher consistency with ground measured precipitation,smaller errors,the lowest false alarm rate,and the highest detection success rate;(2)SUPER precipitation product performs better in regions with gentle terrain than in areas with complex topography.The accuracy of SUPER is higher in the Guotan Basin than in the Han River Basin.SUPER has a higher CSI index in the Guotan Basin.In areas with complex topography,the accuracy of precipitation products decreases with the increase of elevation,for example,in the Han River Basin,the accuracy of SUPER product decreases with increasing elevation from the south to the north;(3)SUPER's fusion algorithm and datasets can effectively reduce the random error of precipitation data and the clear-rain classification error.However,the treatment of systematic bias is relatively simple,leaving room for further improvement.Also note that,SUPER fusion includes SM2Rain precipitation data based on microwave soil moisture retrieval,and the accuracy of precipitation based on microwave soil moisture in areas with complex topography is relatively low.This study comprehensively analyzes the performance of SUPER in the study area,laying a solid research foundation for the practical application and future enhancements of subsequent products.
multi-source fusion precipitation productSUPERIMERGERA5spatiotemporal scaleaccuracy assessmentHan River Basin
NETL
NSTL
万方数据
