Water Resources Management

, Volume 29, Issue 13, pp 4885–4901 | Cite as

Evaluating the Performance of Merged Multi-Satellite Precipitation Products Over a Complex Terrain

  • Saeed Golian
  • Saber Moazami
  • Pierre-Emmanuel Kirstetter
  • Yang Hong


The aim of this paper is to investigate the effectiveness of merging satellite precipitation products on rainfall estimates over different terrains in Iran. Four widely used satellite rainfall estimates (SREs) namely PERSIANN, TMPA-3B42, TMPA-3B42RT, and CMORPH were evaluated and blended in order to generate new combinations in which we expect to estimate rainfall more accurately than with using each individual product. For this purpose, first, the daily bias of each satellite algorithm was calculated at each pixel by comparing with gauge observed data for the time period of 2003–2008. Then, for each pixel, a linear combination of SREs was selected which resulted in the least bias for that pixel. This combination named as merged satellite rainfall estimate (MSRE). In order to test the proposed method, several statistical indices including bias, root mean square error (RMSE), and correlation coefficient (CC) were employed. The results indicate that the merged product used in this study can improve the accuracy of rainfall estimates compared with each individual satellite estimate over all the study regions for different seasons. However, the efficiency of improvement depends highly on the number of rain gauges distributed over the study area.


Satellite rainfall estimate (SRE) PERSIANN TMPA CMORPH Merging Bias 


  1. AghaKouchak A, Nasrollahi N, Habib E (2009) Accounting for uncertainties of the TRMM satellite estimates. J Remote Sens 1:606–619. doi: 10.3390/rs1030606 CrossRefGoogle Scholar
  2. AghaKouchak A, Mehran A, Norouzi H, Behrangi A (2012) Systematic and random error components in satellite precipitation data sets. Geophys Res Lett 39:L09406. doi: 10.1029/2012GL051592 CrossRefGoogle Scholar
  3. Chiang Y, Hsu K, Chang F, Hong Y, Sorooshian S (2007) Merging multiple precipitation sources for flash flood Forecasting. J Hydrol 340:183–196CrossRefGoogle Scholar
  4. Dinku T, Connor SJ, Ceccato P (2010) Comparison of CMORPH and TRMM-3B42 over mountainous regions of Africa and South America, in: Satellite Rainfall Applications for Surface Hydrology. In: Gebremichael M, Hossaim F (ed), 193– 204. Springer Science + Business Media B.V., ISBN 978-90-481-2914-0, e-ISBN 978-90-481-2915-7. doi: 10.1007/978-90-481-2915-7, Springer Dordrecht Heidelberg London New York, Library of Congress Control Number: 2009937296
  5. Hong Y, Gochis D, Cheng J, Hsu K, Sorooshian S (2007) Evaluation of PERSIANN-CCS rainfall measurement using the NAME event rain gauge network. J Hydrometeorol 8(3):469–482Google Scholar
  6. Huffman GJ, Adler RF, Bolvin DT, Nelkin EJ (2010) The TRMM multisatellite precipitation analysis (TMPA). Chapter in Satellite Applications for Surface Hydrology. In: Gebremichael M, Hossaim F (ed), 3–22. Springer Science + Business Media B.V., ISBN 978-90-481-2914-0, e-ISBN 978-90-481-2915-7. doi: 10.1007/978-90-481-2915-7, Springer Dordrecht Heidelberg London New York, Library of Congress Control Number: 2009937296
  7. Jiang S, Ren L, Hong Y, Yang X, Ma M, Zhang Y, Yuan F (2014) Improvement of multi-satellite real-time precipitation products for ensemble streamflow simulation in a middle latitude basin in south China. Water Resour Manag 28:2259–2278CrossRefGoogle Scholar
  8. Joyce RJ, Janowiak JE, Arkin PA, Xie P (2004) CMORPH: a method that produces global precipitation estimates from passive microwave and infrared data at high spatial ad temporal resolutions. J Hydrometeorol 5:487–503CrossRefGoogle Scholar
  9. Kazianka H, Pilz J (2010) Copula-based geostatistical modeling of continuous and discrete data including covariates. Stoch Env Res Risk A 24:661–673CrossRefGoogle Scholar
  10. Li M, Shao Q (2010) An improved statistical approach to merge satellite rainfall estimates and raingauge data. J Hydrol 385:51–64CrossRefGoogle Scholar
  11. Li L, Hong Y, Wang J, Adler RF, Policelli FS, Habib S, Irwn D, Korme T, Okello L (2009) Evaluation of the real-time TRMMbased multi-satellite precipitation analysis for an operational flood prediction system in Nzoia Basin, Lake Victoria. Africa Nat Hazards 50:109–123. doi: 10.1007/s11069-008-9324-5 CrossRefGoogle Scholar
  12. Moazami S, Golian S, Kavianpour MR, Hong Y (2013) Comparison of PERSIANN and V7 TRMM Multi-satellite Precipitation Analysis (TMPA) products with rain gauge data over Iran. Int J Remote Sens 34(22):8156–8171CrossRefGoogle Scholar
  13. Moazami S, Golian S, Hong Y, Chen S, Kavianpour MR (2014) Comprehensive evaluation of four high-resolution satellite precipitation products over diverse climate conditions in Iran. Hydrol Sci J. doi: 10.1080/02626667.2014.987675 Google Scholar
  14. Modarres R, Sarhadi A (2011) 2011, Statistically-based regionalization of rainfall climates of Iran. Global Planet Change 75:67–75CrossRefGoogle Scholar
  15. Romilly TG, Gebremichael M (2011) Evaluation of satellite rainfall estimates over Ethiopian river basins. Hydrol Earth Syst Sci 15:1505–1514CrossRefGoogle Scholar
  16. Tesfagiorgis K, Mahani SE, Krakauer NY, Khanbilvardi R (2011) Bias Correction of Satellite Rainfall Estimates Using a Radar-Gauge Product – A Case Study in Oklahoma (USA). Hydrol Earth Syst Sci 15: 2631–2647Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • Saeed Golian
    • 1
  • Saber Moazami
    • 2
  • Pierre-Emmanuel Kirstetter
    • 3
    • 4
  • Yang Hong
    • 3
  1. 1.Civil Engineering DepartmentUniversity of ShahroodShahroodIran
  2. 2.Department of Civil Engineering, College of Engineering, Islamshahr BranchIslamic Azad UniversityIslamshahrIran
  3. 3.School of Civil Engineering and Environmental ScienceUniversity of OklahomaNormanUSA
  4. 4.Advanced Radar Research CenterUniversity of OklahomaNormanUSA

Personalised recommendations