基于EMOS方法的多源遥感降水数据融合与性能评估——以黄河中游为例

选择对气候变化和人类活动极为敏感的黄河中游为研究区,采用集成模型输出统计方法(EMOS)对多源遥感降水数据(CHIRPS、CMORPH、PERSIANN-CDR和TMPA 3B42)进行数据融合,并对其基本统计性能和降水等级性能进行评估。研究发现:采用EMOS数据融合方法获得的降水数据集可更好地捕捉观测降水的空间分布和年际均值,其基本统计性能(δBIAS=-3.4%;δRMSE=13.1mm;γKGE=0.233)明显优越于4种独立的遥感降水产品、4种产品的均值(MME)以及采用分位数映射法(QM)获取的数据集,但各统计指标存在较大的空间差异性;EMOS方法获得降水数据集显著地改善了对中雨的检测性能(探测率DPOD=0.38);相对MME,EMOS对大雨的综合探测能力(关键成功指数ECSI)提高了12%,表明该方法对高强度降水的正确检测能力更具优势。基于EMOS多源数据融合方法获得的高精度数据集,可为黄河中游典型生态恢复区的极端降水-水沙关系研究提供科学支撑,具有较好的科学价值和应用前景。

Performance of merged multi-satellite precipitation data products based on the ensemble model output statistic (EMOS):A case study in the middle Yellow River

The error in the precipitation estimation will bring great uncertainty to hydrological process simulation and climate change prediction research.A series of remote sensing precipitation products have provided the possibility to accurately estimate large scale precipitation.However,the remote sensing products from different sources are quite inconsistent.It is urgent to obtain high resolution,high precision precipitation data sets through multi source data fusion technology,with the purpose of reducing the error of single remote sensing precipitation products and improve the accuracy of precipitation estimation.The middle reaches of the Yellow River is located in the semi arid and semi humid transition zone of China,which is a crucial area for the ecological environment restoration projects.Precipitation is the main water resources for this region,while an accurate precipitation estimation is of great singificance to the sustainable development of the regional ecological environment and the stability of economic life.