Reliability of reanalyses products in simulating precipitation and temperature characteristics over India

  • Nikhil Ghodichore
  • R Vinnarasi
  • C T DhanyaEmail author
  • Somnath Baidya Roy


Various reanalyses have been utilized in numerous climate related researches around the globe, however, there exists considerable biasedness in these products, especially in precipitation and temperature data. The ability of these reanalysis products to simulate the precipitation and temperature patterns is observed to be satisfactory at global scale, while it differs significantly at regional scale, especially over regions of high spatio-temporal heterogeneity such as India. Therefore, it is essential to evaluate the applicability and robustness of reanalyses in climate related research. The annual and seasonal variability in spatio-temporal patterns and trends of precipitation and temperature data, with respect to the IMD gridded data over 34 yrs, are evaluated for six global reanalyses namely, NCEP/NCAR Reanalysis (NCEP R1), NCEP-DOE AMIP-2 Reanalysis (NCEP R2), Climate Forecast System Reanalysis (CFSR), ECMWF Interim Reanalysis (ERA-Interim), Modern Era Retrospective Analysis for Research and Application Land only model (MERRA-Land) and JMA 55-year Reanalysis (JRA-55). The ability of the reanalyses was tested based on several factors such as statistical and categorical indices, spells and trends, for annual and seasonal daily values. Several regional and seasonal differences were observed, particularly over high rainfall regions such as Western Ghats and northeastern India. MERRA-Land is found to give the best results for precipitation over India, which is attributed to the updated forcing data using gauge-based precipitation observations. Similarly, ERA-Interim and JRA-55 exhibit better performance for temperature than other datasets. All reanalyses failed to correctly reproduce the trends in IMD data, for both precipitation and temperature. These observations will provide a better perception on the reliability and applicability of reanalyses for climate and hydrological studies over India.


Reanalyses IMD precipitation temperature seasonal spell trend 



The authors would like to thank ECMWF, JMA, NASA and NCEP for providing access to their corresponding reanalyses datasets. The authors would also like to thank Indian Institute of Technology, Delhi (IITD) for supporting this study. They are also thankful to the anonymous reviewers for their valuable comments.

Supplementary material

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Supplementary material 1 (pdf 5448 KB)


  1. Bosilovich M G, Chen J, Robertson F R and Adler R F 2008 Evaluation of global precipitation in reanalyses; J. Appl. Meteor. Climatol. 47 2279–2299.CrossRefGoogle Scholar
  2. Blacutt L A, Herdies D L, de Concalves L G G, Vila D A and Andrade M 2015 Precipitation comparison of CFSR, MERRA, TRMM3B42 and combined scheme datasets in Bolivia; Atmos. Res. 163 117–131.CrossRefGoogle Scholar
  3. Chaudhary S, Dhanya C T and Vinnarasi R 2017 Dry and wet spell variability during monsoon in gauge-based gridded daily precipitation datasets over India; J. Hydrol. 546 204–218.CrossRefGoogle Scholar
  4. Chen G, Iwasaki T, Qin H and Sha W 2014 Evaluation of the warm-season diurnal variability over East Asia in recent reanalyses JRA-55, ERA-Interim, NCEP CFSR, and NASA MERRA; J. Clim. 27 5517–5537.CrossRefGoogle Scholar
  5. Dash S K and Kjellstrom T 2011 Workplace heat stress in the context of rising temperature in India; Curr. Sci. 101 496–503.Google Scholar
  6. Dash S K, Kulkarni M A, Mohanty U C and Prasad K 2009 Changes in the characteristics of rain events in India; J. Geophys. Res. 114 D10109, Scholar
  7. Dee D P, Uppala S M, Simmons A J, Berrisford P, Poli P and Kobayashi S et al. 2011 The ERA-Interim reanalysis: Configuration and performance of the data assimilation system; Quart. J. Roy. Meteor. Soc. 137(656) 553–597.CrossRefGoogle Scholar
  8. Deshpande N R, Kothawale D R and Kulkarni A 2016 Changes in climate extremes over major river basins of India; Int. J. Climatol, Scholar
  9. Dhanya C T and Villarini G 2017 An investigation of predictability dynamics of temperature and precipitation in reanalysis datasets over the continental United States; Atmos. Res. 183(1) 341–350.CrossRefGoogle Scholar
  10. Ebita A, Kobayashi S, Ota Y,Moriya M, Kumabe R, Onogi K, Harada Y, Yasui S, Miyaoka K, Takahashi K and Kamahori H 2011 The Japanese 55-year Reanalysis “JRA-55”: An interim report; Sola 7 149–152.CrossRefGoogle Scholar
  11. Epifani C, Esposito S and Vento D 2004 Persistence of wet and dry spells in Italy: First results in Milano from 1858 to 2000; Proceedings of the 14th International Conference on Clouds and Precipitation and International Association of Meteorology and Atmospheric Sciences, Bologna, pp. 18–24.Google Scholar
  12. Hodges K I, Lee R W and Bengtsson L 2011 A comparison of extra tropical cyclones in recent reanalysis ERA-Interim, NASA MERRA, NCEP CFSR, and JRA-25; J. Clim. 24 4888–4906.CrossRefGoogle Scholar
  13. Kalnay E, Kanamitsu M, Kistler R, Collins W, Deaven D, Gandin L, Iredell M, Saha S, White G, Woollen J and Zhu Y 1996 The NCEP/NCAR 40-year reanalysis project; Bull. Am. Meteorol. Soc. 77(3) 437–471.CrossRefGoogle Scholar
  14. Kanamitsu M, Ebisuzaki W, Woollen J, Yang S K, Hnilo J J, Fiorino M and Potter G L 2002 NCEP–DOE AMIP-ii reanalysis (r-2); Bull. Am. Meteor. Soc. 83(11) 1631–1643.CrossRefGoogle Scholar
  15. Kang S and Ahn J 2015 Global energy and water balances in latest reanalyses; Asia-Pac. J. Atmos. Sci. 51(4) 293–302.CrossRefGoogle Scholar
  16. Kar S C and Rana S 2014 Interannual variability of winter precipitation over northwest India and adjoining region: Impact of global forcings; Theor. Appl. Climatol. 116 609–623.CrossRefGoogle Scholar
  17. Kishore P, Jyothi S, Basha G, Rao S V B, Rajeevan M, Velicogna I and Sutterley T C 2016 Precipitation climatology over India: Validation with observations and reanalysis datasets and spatial trends; Clim. Dyn. 46(1–2) 541–556.CrossRefGoogle Scholar
  18. Kobayashi S, Yukinari O T A, Harada Y, Ebita A, Moriya M, Onoda H, Onogi K, Kamahori H, Kobayashi C, Miyaoka K and Takahashi K 2015 The JRA-55 reanalysis: General specifications and basic characteristics; J. Meteorol. Soc. Japan. Ser. II 93(1) 5–48.CrossRefGoogle Scholar
  19. Lin R, Zhou T and Qian Y 2014 Evaluation of global monsoon precipitation changes based on five reanalysis datasets; J. Clim. 27 1271–1289.CrossRefGoogle Scholar
  20. Lorenz C and Kunstmann H 2012 The hydrological cycle in three state-of-the-art reanalyses: Intercomparison and performance analysis; J. Hydrometeor. 13 1397–1420.CrossRefGoogle Scholar
  21. Manzanas R, Amekudzi L K, Preco K, Herrera S and Gutierrez J M 2014 Precipitation variability and trends in Ghana: An intercomparison of observational and reanalysis products; Clim. Change 124 805–819.CrossRefGoogle Scholar
  22. Misra V, Pantina P, Chan S C and DiNapoli S 2012 A comparative study of the Indian monsoon hydroclimate and its variations in three reanalyses; Clim. Dyn. 39 1149–1168.CrossRefGoogle Scholar
  23. Outten S, Davy R and Isau I 2013 Eurasian winter cooling: Intercomparison of reanalyses and CMIP5 data sets; Atmos. Oceanic Sci. Lett. 6(5) 324–331.CrossRefGoogle Scholar
  24. Pai D S and Rajeevan M 2007 Indian summer monsoon onset: Variability and prediction, Nat. Clim. Centre, India Meteorological Department.Google Scholar
  25. Pai D S, Sridhar L, Rajeevan M, Sreejith O P, Satbhai N S and Mukhopadhyay B 2014 Development of a new high spatial resolution (\(0.25\times \)0.25) long period (1901–2010) daily gridded rainfall data set over India and its comparison with existing data sets over the region; Mausam 65(1) 1–18.Google Scholar
  26. Rajeevan M, Gadgil S and Bhate J 2010 Active and break spells of the Indian summer monsoon; J. Earth Syst. Sci.  119(3) 229–247.CrossRefGoogle Scholar
  27. Rana S, Mcgregor J and Renwick J 2015 Precipitation seasonality over India subcontinent: An evaluation of gauge, reanalysis and satellite retrievals; J. Hydrometeor. 16 631–651.CrossRefGoogle Scholar
  28. Reichle R H, Koster R D, Lannoy G J, Forman B A, Liu Q, Mahanama S P P and Toure A 2011 Assessment and enhancement of MERRA-land surface hydrology estimates; J. Clim. 24 6322–6338.CrossRefGoogle Scholar
  29. Rienecker M M, Suarez M J, Gelaro R, Todling R, Bacmeister J, Liu E, Bosilovich M G, Schubert S D, Takacs L, Kim G K and Bloom S 2011 MERRA: NASA’s modern-era retrospective analysis for research and applications; J. Clim. 24 3624–3648.CrossRefGoogle Scholar
  30. Robertson F R, Bosilovich M G, Chen J and Miller T L 2011 The effect of satellite observing system changes on MERRA water and energy fluxes; J. Clim. 24(20) 5197–5217.CrossRefGoogle Scholar
  31. Saha S, Moorthi S, Pan H L, Wu X, Wang J, Nadiga S, Tripp P, Kistler R, Woollen J, Behringer D and Liu H 2010 The NCEP Climate Forecast System Reanalysis; Bull. Am. Meteor. Soc. 91 1015–1057.CrossRefGoogle Scholar
  32. Santer B D, Wigley T M, Simmons A J, Kållberg P W, Kelly G A, Uppala S M, Ammann C, Boyle J S, Brüggemann W, Doutriaux C and Fiorino M 2004 Identification of anthropogenic climate change using a second-generation reanalysis; J. Geophys. Res. (Atmos.) 109 (D21).CrossRefGoogle Scholar
  33. Shah R and Mishra V 2014 Evaluation of the reanalysis products for the monsoon season droughts in India; J. Hydrometeor. 15 1575–1591.CrossRefGoogle Scholar
  34. Shepard D 1968 A two-dimensional interpolation function for irregularly-spaced data. In: Proceedings of the 1968 23rd ACM National Conference, ACM, pp. 517–524.Google Scholar
  35. Soares P M, Cardoso R M, Miranda P M, de Medeiros J, Belo-Pereira M and Espirito-Santo F 2012 WRF high resolution dynamical downscaling of ERA-Interim for Portugal; Clim. Dyn. 39 2497–2522.CrossRefGoogle Scholar
  36. Srivastava A K, Rajeevan M and Kshirsagar S R 2009 Development of a high resolution daily gridded temperature data set (1969–2005) for the Indian region; Atmos. Sci. Lett. 10 249–254.Google Scholar
  37. Sushama L, Said S B, Khaliq M N, Kumar D N and Laprise R 2014 Dry spell characteristics over India based on IMD and APHRODITE datasets; Clim. Dyn. 43(12) 3419–3437.CrossRefGoogle Scholar
  38. Taylor K E 2001 Summarizing multiple aspects of model performance in a single diagram; J. Geophys. Res. 106(D7) 7183–7192.CrossRefGoogle Scholar
  39. Tilya F and Mhita M 2007 Frequency of wet and dry spells in Tanzania. Clim. and Land Degradation, Springer, pp. 197–204.Google Scholar
  40. Uppala S M, Kållberg P W, Simmons A J, Andrae U, Bechtold V D, Fiorino M, Gibson J K, Haseler J, Hernandez A, Kelly G A and Li X 2005 The ERA-40 re-analysis; Quart. J. Roy. Meteorol. Soc.  131(612)2961–3012.CrossRefGoogle Scholar
  41. Vinnarasi R and Dhanya C T 2016 Changing characteristics of extreme wet and dry spells of Indian monsoon rainfall; J. Geophys. Res. (Atmos.) 121(5).Google Scholar
  42. Vose R S, Applequist S, Menne M J, Williams C N and Thorne P 2012 An intercomparison of temperature trends in the U.S. historical climatology network and atmospheric reanalyses; Geophys. Res. Lett. 39 L10703.CrossRefGoogle Scholar
  43. Wang A and Zeng X 2013 Development of global hourly \(0.5^{\circ }\) land surface air temperature datasets; J. Clim. (26) 7676–7691.CrossRefGoogle Scholar
  44. Wilks D S 2011 Statistical methods in the atmospheric sciences; Vol. 100, Academic Press.Google Scholar

Copyright information

© Indian Academy of Sciences 2018

Authors and Affiliations

  1. 1.Department of Civil EngineeringIndian Institute of Technology DelhiNew DelhiIndia
  2. 2.Centre for Atmospheric SciencesIndian Institute of Technology DelhiNew DelhiIndia

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