Earth and Space Science (Dec 2019)

The Assessment and Comparison of TMPA and IMERG Products Over the Major Basins of Mainland China

  • Jianbin Su,
  • Haishen Lü,
  • Dongryeol Ryu,
  • Yonghua Zhu

DOI
https://doi.org/10.1029/2019EA000977
Journal volume & issue
Vol. 6, no. 12
pp. 2461 – 2479

Abstract

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Abstract The Integrated Multi‐satellitE Retrievals for Global Precipitation Measurement mission (IMERG) aims to deliver the “best” precipitation estimation from space and has attracted much attention. The Version 05 of IMERG products including the near‐real‐time “Early” and “Late” run products (IMERG‐E and IMERG‐L, respectively), and the post‐real‐time “Final” run IMERG product (IMERG‐F) are assessed at both national and basin scales against gauge observations over Mainland China for a 4‐year period (from April 2014 to March 2018). As control products for comparison, their predecessor Tropical Rainfall Measurement Mission (TRMM) Multi‐satellite Precipitation Analysis (TMPA) products (i.e., TMPA‐RT and TMPA‐V7) are also employed. Components analysis confirms the best performance of IMERG‐F among the five SPEs in three different categories. All five SPEs feature increasing bias and root mean square difference (RMSD) with increasing daily gauge total precipitation, and such issue is less pronounced for IMERG‐F—as evidenced by the lowest bias and RMSD across all precipitation rates. Besides, compared to TMPA, IMERG products exhibit better accuracy in detecting real precipitation evens, especially for light‐to‐medium rain (<60 mm/day), but they do not demonstrate significant improvement in the assessment of severe over/underestimation. In the basin‐scale comparison, all five SPEs catch the key variation feature of basin‐averaged precipitation time series (except TMPA‐RT over Continental River Basin). Overall, IMERG‐F demonstrates the best performance over all nine basins despite the slight overestimation, followed by TMPA‐V7. IMERG‐E and IMERG‐L show performance close to or even better than TMPA‐V7 in terms of the correlation coefficient and RMSD.

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