Chinese Geographical Science

, Volume 26, Issue 4, pp 439–455 | Cite as

Evaluation of latest TMPA and CMORPH precipitation products with independent rain gauge observation networks over high-latitude and low-latitude basins in China

  • Shanhu Jiang
  • Liliang Ren
  • Bin Yong
  • Yang Hong
  • Xiaoli Yang
  • Fei Yuan


The Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) and National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center (CPC) morphing technique (CMORPH) are two important multi-satellite precipitation products in TRMM-era and perform important functions in GPM-era. Both TMPA and CMORPH systems simultaneously upgraded their retrieval algorithms and released their latest version of precipitation data in 2013. In this study, the latest TMPA and CMORPH products (i.e., Version-7 real-time TMPA (T-rt) and gauge-adjusted TMPA (T-adj), and Version-1.0 real-time CMORPH (C-rt) and Version-1.0 gauge-adjusted CMORPH (C-adj)) are evaluated and intercompared by using independent rain gauge observations for a 12-year (2000–2011) period over two typical basins in China with different geographical and climate conditions. Results indicate that all TMPA and CMORPH products tend to overestimate precipitation for the high-latitude semiarid Laoha River Basin and underestimate it for the low-latitude humid Mishui Basin. Overall, the satellite precipitation products exhibit superior performance over Mishui Basin than that over Laoha River Basin. The C-adj presents the best performance over the high-latitude Laoha River Basin, whereas T-adj showed the best performance over the low-latitude Mishui Basin. The two gauge-adjusted products demonstrate potential in water resource management. However, the accuracy of two real-time satellite precipitation products demonstrates large variability in the two validation basins. The C-rt reaches a similar accuracy level with the gauge-adjusted satellite precipitation products in the high-latitude Laoha River Basin, and T-rt performs well in the low-latitude Mishui Basin. The study also reveals that all satellite precipitation products obviously overestimate light rain amounts and events over Laoha River Basin, whereas they underestimate the amount and events over Mishui Basin. The findings of the precision characteristics associated with the latest TMPA and CMORPH precipitation products at different basins will offer satellite precipitation users an enhanced understanding of the applicability of the latest TMPA and CMORPH for water resource management, hydrologic process simulation, and hydrometeorological disaster prediction in other similar regions in China. The findings will also be useful for IMERG algorithm development and update in GPM-era.


satellite precipitation Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) Climate Prediction Center morphing technique (CMORPH) precision evaluation 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Adler R F, Huffman G J, Chang G J et al., 2003. The version 2 Global Precipitation Climarology Project (GPCP) monthly precipitation analysis (1979–present). Journal of Hydrometeorology, 4(6): 1147–1167. doi: 10.1175/1525-7541(2003) 004<1147:TVGPCP>2.0.CO;2CrossRefGoogle Scholar
  2. Bartier P M, Keller C P, 1996. Multivariate interpolation to incorporate thematic surface data using inverse distance weighting (IDW). Computers & Geosciences, 22(7): 795–799. doi: 10.1016/0098-3004(96)00021-0CrossRefGoogle Scholar
  3. Behrangi A, Khakbaz B, Jaw T C et al., 2011. Hydrologic evaluation of satellite precipitation products over a mid-size basin. Journal of Hydrology, 397(3–4): 225–237. doi: 10.1016/j. jhydrol.2010.11.043CrossRefGoogle Scholar
  4. Bitew M M, Gebremichael M, 2011. Evaluation of satellite rainfall products through hydrologic simulation in a full distributed hydrologic model. Water Resources Research, 47(6): W06526. doi: 10.1029/2010WR009917CrossRefGoogle Scholar
  5. Chen S, Hong Y, Cao Q et al., 2013. Similarity and difference of the two successive V6 and V7 TRMM multisatellite precipitation analysis performance over China. Journal of Geophysical Research, 118(23): 13060–13074. doi: 10.1002/2013JD019964Google Scholar
  6. Ebert E E, Janowiak J E, Kidd C, 2007. Comparison of near-real-time precipitation estimates from satellite observations and numerical models. Bulletin of the American Meteorological Society, 88(1): 47–64. doi: 10.1175/BAMS-88-1-47CrossRefGoogle Scholar
  7. Gebregiorgis A S, Hossain F, 2015. How well can we estimate error variance of satellite precipitation data around the world? Atmospheric Research, 154: 39–59. doi: 10.1016/j.atmosres. 2014.11.005CrossRefGoogle Scholar
  8. Hong Y, Adler R F, Hossain F et al., 2007. A first approach to global runoff simulation using satellite rainfall estimation. Water Resources Research, 43(8): W08502. doi: 10.1029/2006 WR005739CrossRefGoogle Scholar
  9. Hossain F, Huffman G J, 2008. Investigating error metrics for satellite rainfall at hydrologically relevant scales. Journal of Hydrometeorology, 9(3): 563–575. doi: 10.1175/2007JHM 925.1CrossRefGoogle Scholar
  10. Hou A Y, Kakar R K, Neeck S et al., 2014. The global precipitation measurement mission. Bulletin of the American Meteorological Society, 95(5): 701–722. doi: 10.1175/BAMS-D-13- 00164.1CrossRefGoogle Scholar
  11. Hu Q F, Yang D W, Li Z et al., 2014. Multi-scale evaluation of six high-resolution satellite monthly rainfall estimates over a humid region in China with dense rain gauges. International Journal of Remote Sensing, 35(4): 1272–1294. doi: 10.1080/01431161.2013.876118CrossRefGoogle Scholar
  12. Huffman G J, Adler R F, Bolvin D T et al., 2007. The TRMM Multisatellite Precipitation Analysis (TMPA): quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. Journal of Hydrometeorology, 8(1): 38–55. doi: 10. 1175/JHM560.1CrossRefGoogle Scholar
  13. Huffman G J, Bolvin D T, 2013. Real-Time TRMM Multi-Satellite Precipitation Analysis data set documentation. ftp://mesoa. Scholar
  14. Jiang S H, Ren L L, Hong Y et al., 2012. Comprehensive evaluation of multi-satellite precipitation products with a dense rain gauge network and optimally merging their simulated hydrological flows using the Bayesian Model Averaging Method. Journal of Hydrology, 452–453: 213–225. doi: 10.1016/j. jhydrol. 2012.05.055CrossRefGoogle Scholar
  15. Jiang S H, Ren L L, Hong Y et al., 2014. Improvement of multi-satellite real-time precipitation products for ensemble streamflow simulation in a middle latitude basin in South China. Water Resources Management, 28(8): 2259–2278. doi: 10.1007/s11269-014-0612-4CrossRefGoogle Scholar
  16. Jiang S H, Ren L L, Yong B et al., 2010. Evaluation of highresolution satellite precipitation products with surface rain gauge observations from Laohahe Basin in northern China. Water Science and Engineering, 3(4): 405–417. doi: 10.3882/j.issn.1674-2370.2010.04.004Google Scholar
  17. Joyce R J, Janowiak J E, Arkin P A et al., 2004. CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. Journal of Hydrometeorology, 5(3): 487–503. doi: org/10.1175/1525-7541CrossRefGoogle Scholar
  18. Kidd C, Huffman G, 2011. Global precipitation measurement. Meteorological Applications, 18(3): 334–353. doi: 10.1002/met.284CrossRefGoogle Scholar
  19. Kucera P A, Ebert E E, Turk F J et al., 2013. Precipitation from space: advancing Earth System Science. Bulletin of the American Meteorological Society, 94(3): 365–375. doi: org/10. 1175/BAMS-D-11-00171.1CrossRefGoogle Scholar
  20. Li X H, Zhang Q, Xu C Y, 2014. Assessing the performance of satellite-based precipitation products and its dependence on topography over Poyang Lake Basin. Theoretical and Applied Climatology, 115(3): 713–729. doi: 10.1007/s00704-013-0917-xCrossRefGoogle Scholar
  21. Li Z, Yang D W, Hong Y, 2013. Multi-scale evaluation of high-resolution multi-sensor blended global precipitation products over the Yangtze River. Journal of Hydrology, 500: 157–169. doi: 10.1016/j.jhydrol.2013.07.023CrossRefGoogle Scholar
  22. Liu J Z, Duan Z, Jiang J C et al., 2014. Evaluation of three satellite precipitation products TRMM 3B42, CMORPH, and PERSIANN over a subtropical watershed in China. Advance in Meteorology, 151239. doi: org/10.1155/2015/151239Google Scholar
  23. Pan M, Li H, Wood E, 2010. Assessing the skill of satellite based precipitation estimates in hydrologic applications. Water Resources Research, 46(9): W09535. doi: 10.1029/2009WR 008290CrossRefGoogle Scholar
  24. Qin Y X, Chen Z Q, Shen Y et al., 2014. Evaluation of satellite rainfall estimates over the Chinese Mainland. Remote Sensing, 6(11): 11649–11672. doi: 10.3390/rs61111649CrossRefGoogle Scholar
  25. Shen Y, Xiong A Y, Wang Y et al., 2010. Performance of high-resolution satellite precipitation products over China. Journal of Geophysical Research, 115(D2): D02114. doi: 10.1029/2009JD012097CrossRefGoogle Scholar
  26. Shen Y, Zhao P, Pan Y et al., 2014. A high spatiotemporal gaugesatellite merged precipitation analysis over China. Journal of Geophysical Research, 119(6): 3063–3075. doi: 10. 1002/2013JD020686Google Scholar
  27. Su F G, Hong Y, Lettenmaier D P et al., 2008. Evaluation of TRMM Multi-satellite Precipitation Analysis (TMPA) and its utility in hydrologic prediction in La Plata Basin. Journal of Hydrometeorology, 9(4): 622–640. doi: org/10.1175/2007 JHM944.1CrossRefGoogle Scholar
  28. Tong K, Su F G, Yang D et al., 2014. Evaluation of satellite precipitation retrievals and their potential utilities in hydrologic modeling over the Tibetan Plateau. Journal of Hydrology, 519: 423–437. doi: 10.1016/j.jhydrol.2014.07.044CrossRefGoogle Scholar
  29. Wu H, Adler R F, Tian Y D et al., 2014. Real-time global flood estimation using satellite-based precipitation and a coupled land surface and routing model. Water Resources Research, 50(3): 2693–2717. doi: 10.1002/2013WR014710.CrossRefGoogle Scholar
  30. Xie P P, 2013. CMORPH_V1.0_README. Scholar
  31. Xue X W, Hong Y, Limaye A S et al, 2013). Statistical and hydrological evaluation of TRMM-based Multi-satellite Precipitation Analysis over the Wangchu Basin of Bhutan: are the latest satellite precipitation products 3B42V7 ready for use in ungauged basins? Journal of Hydrology, 499: 91–99. doi: 10. 1016/j.jhydrol.2013.06.042Google Scholar
  32. Yong B, Chen B, Gourley J J et al., 2014. Intercomparison of the Version-6 and Version-7 TMPA precipitation products over high and low latitudes basins with independent gauge networks: is the newer version better in both real-time and post-real-time analysis for water resources and hydrologic extremes? Journal of Hydrology, 508: 77–87. doi: 10.1016/j. jhydrol.2013.10.050CrossRefGoogle Scholar
  33. Yong B, Hong Y, Ren L L et al., 2012. Assessment of evolving TRMM-based multisatellite real-time precipitation estimation methods and their impacts on hydrologic prediction in a high latitude basin. Journal of Geophysical Research, 117(9): D09108. doi: 10.1029/2011JD017069Google Scholar

Copyright information

© Science Press, Northeast Institute of Geography and Agricultural Ecology, CAS and Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Shanhu Jiang
    • 1
  • Liliang Ren
    • 1
  • Bin Yong
    • 1
  • Yang Hong
    • 2
  • Xiaoli Yang
    • 1
  • Fei Yuan
    • 1
  1. 1.State Key Laboratory of Hydrology-Water Resources and Hydraulic EngineeringHohai UniversityNanjingChina
  2. 2.School of Civil Engineering and Environmental Sciences, School of MeteorologyUniversity of OklahomaOklahomaUSA

Personalised recommendations