Multiscale comparative evaluation of the GPM IMERG v5 and TRMM 3B42 v7 precipitation products from 2015 to 2017 over a climate transition area of China

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The performance of the latest released Integrated Multi-satellitE Retrievals for GPM mission (IMERG) version 5 (IMERG v5) and the TRMM Multisatellite Precipitation Analysis 3B42 version 7 (3B42 v7) are evaluated and compared at multiple temporal scales over a semi-humid to humid climate transition area (Huaihe River basin) from 2015 to 2017. The impacts of rainfall rate, latitude and elevation on precipitation detection skills are also investigated. Results indicate that both satellite estimates showed a high Pearson correlation coefficient (r, above 0.89) with gauge observations, and an overestimation of precipitation at monthly and annual scales. Mean daily precipitation of IMERG v5 and 3B42 v7 display a consistent spatial pattern, and both characterize the observed precipitation distribution well, but 3B42 v7 tends to markedly overestimate precipitation over water bodies. Both satellite precipitation products overestimate rainfalls with intensity ranging from 0.5 to 25 mm/day, but tend to underestimate light (0-0.5 mm/day) and heavy ( > 25 mm/day) rainfalls, especially for torrential rains (above 100 mm/day). Regarding each gauge station, the IMERG v5 has larger mean r (0.36 for GPM, 0.33 for TRMM) and lower mean relative root mean square error (RRMSE, 1.73 for GPM, 1.88 for TRMM) than those of 3B42 v7. The higher probability of detection (POD), critical success index (CSI) and lower false alarm ratio (FAR) of IMERG v5 than those of 3B42 v7 at different rainfall rates indicates that IMERG v5 in general performs better in detecting the observed precipitations. This study provides a better understanding of the spatiotemporal distribution of accuracy of IMERG v5 and 3B42 v7 precipitation and the influencing factors, which is of great significance to hydrological applications.


  • Cheng Chen
  • Qiuwen Chen
  • Zheng Duan
  • Jianyun Zhang
  • Kangle Mo
  • Zhe Li
  • Guoqiang Tang
Externa organisationer
  • Nanjing Hydraulic Research Institute
  • Hohai University
  • Technical University of Munich
  • Institute of Geographic Sciences and Natural Resources Research,CAS
  • Tsinghua University

Ämnesklassifikation (UKÄ) – OBLIGATORISK

  • Oceanografi, hydrologi, vattenresurser


TidskriftRemote Sensing
Utgåva nummer6
StatusPublished - 2018 jun 1
Peer review utfördJa
Externt publiceradJa