水科学进展2023，Vol.34Issue(5)：685-696,12.DOI:10.14042/j.cnki.32.1309.2023.05.004
基于贝叶斯三角帽法的多源降水数据融合分析及应用
Multi-source precipitation data fusion analysis and application based on Bayesian-Three Cornered Hat method
摘要
Abstract
At present,precipitation products still have great uncertainty.Precipitation and its spatial distribution can be estimated more accurately by using multi-source precipitation data fusion.To achieve data fusion in no-gauged areas,Bayesian-Three Cornered Hat method is adopted to integrate precipitation products based on gauged data,satellite remote sensing and reanalysis data without any prior information,to explore the influence of precipitation products with different input quantities on the accuracy of fusion data,and to study the contribution rates of each precipitation product to the accuracy of fusion data.It is applied in the source region of the Yellow River.The results show that the performance of the fusion data is better than that of the original precipitation products on the monthly scale.On the daily scale,the performance of the fusion data is obviously better than that of satellite remote sensing and reanalysis precipitation products,but lower than that of the gauge-based precipitation product CHM_PRE.Two gauge-based precipitation products,CN05.1 and CHM_PRE,have the largest contribution rates to the fusion data.The application in the source region of the Yellow River shows that the Bayesian-Three Cornered Hat method can estimate precipitation more accurately.It is suitable for no-gauged areas,and can provide the reference basis for data fusion analysis and its application.关键词
多源降水/数据融合/不确定性分析/贝叶斯三角帽Key words
multi-source precipitation/data fusion/uncertainty analysis/Bayesian-Three Cornered Hat分类
天文与地球科学引用本文复制引用
赵君,刘雨,徐进超,王国庆,邵月红,杨林..基于贝叶斯三角帽法的多源降水数据融合分析及应用[J].水科学进展,2023,34(5):685-696,12.基金项目
国家重点研发计划资助项目(2021YFC3201101) （2021YFC3201101）
江苏省重点研发计划资助项目(BE2020633)The study is financially supported by the National Key R&D Program of China(No.2021YFC3201101)and Key R&D Project of Jiangsu Province,China(No.BE2020633). （BE2020633）
