Fusing Satellite Precipitation Products Based on Top–Down and Bottom–Up Approaches and an Improved Double Instrumental Variable Method for the Chuanyu Region, China, from 2007 to 2019

Abstract

Precipitation is one of the crucial variables in the hydrological and ecological cycles. High-quality precipitation data are of great importance for climate change, water resource management, and agricultural research over complex terrains. Recently, satellite precipitation products have been produced based on different retrieval algorithms, mainly the top–down and bottom–up approaches. Fusing satellite precipitation products based on these two different approaches may combine their advantages and achieve a better data quality for describing precipitation. In this paper, an improved double instrumental variable (IMDIV) method is proposed for data quality enhancement by merging IMERG (integrated multi-satellite retrievals for global precipitation measurement), which is based on the top–down approach, and SM2RAIN (soil moisture to rain), which is based on the bottom–up approach. In detail, IMERG-Early (IMERG early run) and IMERG-Final (IMERG final run) are merging with SM2RAIN at a daily scale, respectively. Rain gauge station records from GHCNd (Global Historical Climatology Network Daily) are used to evaluate the original and fused precipitation products for the Chuanyu region, China, from 2007 to 2019. The results show that the proposed IMDIV method outperforms the original DIV method on precipitation fusion tasks. Moreover, the proposed IMDIV-EAS (fusing IMERG-Early and SM2RAIN) and IMDIV-FIS (fusing IMERG-Final and SM2RAIN) products both outperform the original precipitation products IMERG and SM2RAIN, with higher correlation coefficients (R) of 0.603 and 0.634; better RMSEs of 5.136 and 5.088 mm/day; and better biases of 0.514 and 0.509 mm/day. The results demonstrate the effectiveness of the proposed method and the high quality of the fused products, which could be useful for hydrology and climate studies.