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Performance evaluation and correction of precipitation data using the 20-year IMERG and TMPA precipitation products in diverse subregions of China
Atmospheric Research ( IF 5.5 ) Pub Date : 2021-02-01 , DOI: 10.1016/j.atmosres.2020.105304 Qian Ma , Yi Li , Hao Feng , Qiang Yu , Yufeng Zou , Fenggui Liu , Bakhtiyor Pulatov
Atmospheric Research ( IF 5.5 ) Pub Date : 2021-02-01 , DOI: 10.1016/j.atmosres.2020.105304 Qian Ma , Yi Li , Hao Feng , Qiang Yu , Yufeng Zou , Fenggui Liu , Bakhtiyor Pulatov
Abstract The latest Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) dataset version 6, which is the successor of the multi-satellite 3B42 dataset from the Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (TMPA), is now recommended for many purposes. It is unclear how the quality of the latest IMERG data products compares to that of the delisted TMPA series data sets. This study evaluated the accuracy of TRMM 3B42 version 7 and IMERG final version 6 products at 677 stations in the 7 subregions of China over 2000–2019 at the daily, monthly and annual timescales based on gauge-observed precipitation data. The performance of TMPA and IMERG precipitation products was assessed by six statistical indices. The product with the best performance was corrected based on the linear relationship of monthly precipitation between remote sensing products and ground-observed precipitation. The correction was then used for the predicting and mapping of precipitation in July 2019. The results showed that (1) IMERG outperformed TMPA at all three timescales, and the accuracy increased with the expanding timescale. (2) IMERG exhibited better performance in all subregions than TMPA and performed best in the mid-temperate semihumid zone and worst in the mid-temperature arid zone. (3) IMERG showed higher accuracy than TMPA in all months for most subregions and mainland China but performed worse in dry (December, January and February) and wet (July, August and September) months. (4) The linear relationship between remote sensing products and ground-observed precipitation could be used to correct and predict satellite precipitation data without up-to-date gauge observations, the predicted map indicated the good spatial variation in precipitation and better accuracy than the original IMERG data. In conclusion, the IMERG dataset was more accurate and could be used for precipitation investigations at large scales in China.
更新日期：2021-02-01
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