Comparison analysis of six purely satellite-derived global precipitation estimates

• Published in: Journal of hydrology (Amsterdam) Vol. 581; p. 124376
• Main Authors: Chen, Hanqing, Yong, Bin, Shen, Yan, Liu, Jiufu, Hong, Yang, Zhang, Jianyun
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.02.2020
• Subjects: algorithms; China; cold season; Error source; floods; humid zones; Light rainfall; Mainland China; rain; remote sensing; RMSE; Satellite-only precipitation products; satellites; China; Mainland China; Satellite-only precipitation products; Light rainfall; RMSE; Error source
• ISSN: 0022-1694; 1879-2707
• DOI: 10.1016/j.jhydrol.2019.124376

•Six satellite-only precipitation products (SPPs) were evaluated over the globe.•The error sources of five SPPs over mainland China were revealed.•IMERG-Late is the best one of six evaluated SPPs.•A power function is observed between RMSE and logarithm of precipitation intensity.•GPM-based SPPs in light rainfall still exhibit large errors. We executed a comprehensive evaluation and intercomparison between six purely satellite-derived precipitation estimates (i.e., IMERG-Late, IMERG-Early, GSMaP-NRT, GSMaP-MVK, TMPA-RT and PERSIANN-CCS) at global and regional scales for the period from February 2017 to January 2019. The results show that IMERG-Late exhibits the best performance among six evaluated products, while the worst performance was found in GSMaP-NRT and GSMaP-MVK. The root mean squared error (RMSE) has a power function to the logarithm of precipitation intensity in all six satellite products. On the basis of our findings, the RMSE of all products in rainfall events with intensity exceeding 32 mm/day (or 8 mm/h) accounts for beyond 30% of the corresponding precipitation intensity, which might result in a significant impact on the detectability and forecast of flash floods simulated by satellite precipitation. Additionally, both IMERG and GSMaP overestimate the proportions of light rainfall occurrences, and also display relatively larger errors in light precipitation (0.2–0.4 mm/h or 1–2 mm/day) with the RMSE values exceeding 0.5 mm (or 2 mm) at hourly (or daily) time scale. As for the error analysis, we decomposed the total bias of each product into hits, misses and false biases at hourly and 0.1° resolution over mainland China except for TMPA-RT. We found that the false bias is the dominated error sources for these five products in cold season over semi-humid areas despite that the hit bias accounts for a non-negligible proportion for GSMaP suite. The missed precipitation is the dominated error sources of PERSIANN-CCS both in two seasons over most of humid regions, and meanwhile is one of major error sources for other four products. We expect that the findings of this study not only provide some valuable feedbacks for algorithm developers to improve the GPM-based satellite precipitation retrievals, but also provide some guidance for data users across the world.