Inter-Comparison of High Resolution Satellite Estimates for Cloudburst Events in the Northwest Himalaya

  • Garima Dahiya
  • Pravat Jena
  • Sourabh Garg
  • Sarita AzadEmail author


The Tropical Rainfall Measuring Mission (TRMM) 3B42 version 7 precipitation data has been extensively used for inter-comparison with observations and model validation. The rain distribution over the Northwest Himalaya (NWH) were found to be accurate with a strong positive correlation of 0.88 between TRMM and India Meteorological Department (IMD) station data, supporting the use of 3B42 V7 for the study of extreme rainfall events (ERE’s) over the region. However, many high resolution satellite data sets were made available in the recent past and their potential have not been evaluated for ERE’s like cloudbursts in the NWH. The present endeavor aims to provide guidance to the choice of global precipitation data sets (GPDs). In particular, this study is conducted to evaluate three recent satellite-based rainfall products, i.e. Global Precipitation Measurements (GPM), Indian National Satellite System (INSAT 3D), and CPC Morphing Technique (CMORPH), against the highly used TRMM-RT 3B42 V7 precipitation data for the estimation of rainfall episodes in the recent years (2014–2016). Our results reveal that the magnitude of precipitation and location of peak rainfall are biased in INSAT 3D, whereas CMORPH and the high resolution GPM product capture it with relatively higher values of the employed statistical metrics. Also, the rainfall estimates from GPM and CMORPH are in good agreement with TRMM for cloudbursts events. Particularly, high resolution GPM is useful for monitoring the extreme rainfall event in the region.


Cloudburst event Precipitation NWH TRMM CMORPH GPM INSAT 3D 


  1. Bharti VIDHI (2015) Investigation of extreme rainfall events over the northwest Himalaya region using satellite data. University of Twente Faculty of Geo-Information and Earth Observation (ITC), EnschedeGoogle Scholar
  2. Bharti V, Singh C, Ettema J, Turkington TAR (2016) Spatiotemporal characteristics of extreme rainfall events over the northwest Himalaya using satellite data. Int J Climatol 36(12):3949–3962CrossRefGoogle Scholar
  3. Huffman GJ, Bolvin DT, Nelkin EJ, Wolff DB, Adler RF, Gu G, Stocker EF (2007) The TRMM multi-satellite precipitation analysis (TMPA): quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J Hydrometeorol 8(1):38–55CrossRefGoogle Scholar
  4. Huffman GJ, Bolvin DT, Nelkin EJ, Adler RF (2010) Highlights of version 7 TRMM multi-satellite precipitation analysis (TMPA). In: 5th international precipitation working group workshop, workshop program and proceedings, pp 11–15Google Scholar
  5. 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 and temporal resolution. J Hydrometeorol 5(3):487–503CrossRefGoogle Scholar
  6. Kumar P, Varma AK (2017) Assimilation of INSAT-3D hydro-estimator method retrieved rainfall for short-range weather prediction. Q J R Meteorol Soc 143(702):384–394CrossRefGoogle Scholar
  7. Mantas VM, Liu Z, Caro C, Pereira AJSC (2014) Validation of TRMM multi-satellite precipitation analysis (TMPA) products in the Peruvian Andes. Atmos Res 163:132–145. Scholar
  8. McCabe MF, Wood EF (2006) Scale influences on the remote estimation of evapotranspiration using multiple satellite sensors. Remote Sens Environ 105(4):271–285CrossRefGoogle Scholar
  9. Nandargi S, Dhar ON (2011) Extreme rainfall events over the Himalayas between 1871 and 2007. Hydrol Sci J 56(6):930–945CrossRefGoogle Scholar
  10. Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models part I—a discussion of principles. J Hydrol 10(3):282–290CrossRefGoogle Scholar
  11. Pant GB, Kumar KR (1997) Climates of South Asia. Wiley, ChichesterGoogle Scholar
  12. Prakash S, Mitra AK, Momin IM, Pai DS, Rajagopal EN, Basu S (2015) Comparison of TMPA-3B42 versions 6 and 7 precipitation products with gauge-based data over India for the southwest monsoon period. J Hydrometerol 16(1):346–362CrossRefGoogle Scholar
  13. Prakash S, Mitra AK, AghaKouchak A, Liu Z, Norouzi H, Pai DS (2016a) A preliminary assessment of GPM-based multi-satellite precipitation estimates over a monsoon dominated region. J Hydrol. Scholar
  14. Prakash S, Mitra AK, Pai DS, AghaKouchak A (2016b) From TRMM to GPM: how well can heavy rainfall be detected from space? Adv Water Resour 88:1–7CrossRefGoogle Scholar
  15. Qiao L, Hong Y, Chen S, Zou CB, Gourley JJ, Yong B (2014) Performance assessment of the successive version 6 and version 7 TMPA products over the climate-transitional zone in the southern Great Plains, USA. J Hydrol 513:446–456CrossRefGoogle Scholar
  16. Rahman SH, Sengupta D, Ravichandran M (2009) Variability of Indian summer monsoon rainfall in daily data from gauge and satellite. J Geophys Res 114(D17)Google Scholar
  17. Tawde SA, Singh C (2015) Investigation of orographic features influencing spatial distribution of rainfall over the Western Ghats of India using satellite data. Int J Climatol 35(9):2280–2293CrossRefGoogle Scholar
  18. Thayyen RJ, Dimri AP, Kumar P, Agnihotri G (2013) Study of cloudburst and flash floods around Leh, India, during August 4–6, 2010. Nat Hazards 65(3):2175–2204CrossRefGoogle Scholar
  19. Zulkafli Z, Buytaert W, Onof C, Manz B, Tarnavsky E, Lavado W, Guyot JL (2014) A comparative performance analysis of TRMM 3B42 (TMPA) versions 6 and 7 for hydrological applications over Andean–Amazon river basins. J Hydrometeorol 15(2):581–592CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Garima Dahiya
    • 1
  • Pravat Jena
    • 1
  • Sourabh Garg
    • 1
  • Sarita Azad
    • 1
    Email author
  1. 1.School of Basic SciencesIndian Institute of Technology MandiKamandIndia

Personalised recommendations