Offshore Wind Energy Potential Assessment of India Based on the Synergetic Use of QuikSCAT, OSCAT and ASCAT Scatterometers Data

  • Surisetty V. V. Arun KumarEmail author
  • Jagdish Prajapati
  • Raj Kumar
Conference paper
Part of the Lecture Notes in Civil Engineering book series (LNCE, volume 23)


Wind energy is recognised as one of the economically viable and substantiate renewable energy technologies worldwide to expedient the rising electricity requirements in a sustainable way. Presently, India has onshore wind farms accounting the fifth largest wind power capacity in the world. The National Offshore Wind Energy Policy 2015 of the Government of India aims to harness the wind resources within the Exclusive Economic Zone (EEZ). Accordingly, a target of 60,000 MW power generation by 2022 has been set to realise from onshore and offshore wind farms. The traditional method of wind resource assessment for wind energy applications is carried out by analysing a large amount of in situ wind data at different heights. In Indian waters, there are few offshore wind buoys and meteorological masts and are limited to few locations. Satellite remote sensing helps in providing synoptic data due to its larger swaths, better repetivity and longer acquisition periods. Microwave scatterometers are one among them, which provide sufficient data over the entire globe. In this study, the offshore wind climatology in the Indian seas (the Bay of Bengal and the Arabian Sea) is generated from large datasets of QuikSCAT (1999–2009), OSCAT (2010–2014), ASCAT on-board Metop-A (2010–2016) and on Metop-B (2012–2016) scatterometers synergistically. Orbit-wise scatterometer wind products have been processed to generate long-term synoptic monthly means of wind speed and direction. Wind Power Density-WPD (W/m2) was calculated using Weibull distribution parameters at 10 m height. The wind speed, WPD, Power production have been estimated at different heights above the sea surface using a combination of logarithmic law and Weibull scale and shape parameters for few standard turbines of different capacities. Monthly and Annual wind energy potential has been estimated by considering the bathymetric variations and distance away from the coast for all the coastal states within the EEZ of Indian coast.


Wind energy Resource Offshore wind Scatterometer India 



The authors are grateful to Shri Tapan Misra, Director, Space Applications Centre (ISRO) for his constant support and encouragement. The first author is thankful to Dr. A. S. Rajawat, Group Director (GHCAG), Shri. Arun Kumar Sharma, Head (GSD) and Dr. Rashmi Sharma, Head (OSD) for their suggestions and encouragement. The second author is thankful to the HOD, Department of Mathematics, Gujarat University for his support. Authors are thankful to MOSDAC and JPL NASA for providing scatterometer wind data.


  1. 1.
    IPCC (2007) Intergovernmental panel on climate change. Climate change 2007: The physical science basis. Summary for policymakers. Working Group I of the IPCC, Paris, February 2007Google Scholar
  2. 2.
    Yan Q, Chen YC, Wang AJ, Yu WJ, Chen QS (2010) Development obstacles of new energies in China and countermeasures: a review of the global current situation. Acta Geosci Sin 31(5):759–767Google Scholar
  3. 3.
    Ministry of New and Renewable Energy Physical Progress (Achievements).
  4. 4.
    Xin HL (2010) Aspect on the development of offshore wind energy in China. Period Ocean Univ China 40(6):147–152Google Scholar
  5. 5.
    Sheridan B, Baker SD, Pearre NS, Firestone J, Kemption W (2012) Calculating the offshore wind power resource: robust assessment methods applied to the U.S. Atlantic Coast. Renew Energy. Scholar
  6. 6.
    Hasager CB, Peña A, Christiansen MB, Astrup P, Nielsen M, Monaldo, FM et al. (2008) Remote sensing observation used in offshore wind energy. IEEE J Sel Topics Appl Earth Obs Remote Sens 1(1):67–79CrossRefGoogle Scholar
  7. 7.
    Pimenta F, Kempton W, Garvine R (2008) Combining meteorological stations and satellite data to evaluate the offshore wind power resource of Southeastern Brazil. Renew Energy 11.
  8. 8.
    Furevik BR, Sempreviva AM, Cavaleri L, Lefèvre JM, Transerici C (2011) Eight years of wind measurements from scatterometer for wind resource mapping in the Mediterranean Sea. Wind Energy 14(3):355–372. Scholar
  9. 9.
    Karagali I, Peña A, Badger M, Hasager C (2014) Wind characteristics in the North and Baltic Seas from the QuikSCAT satellite. Wind Energy 17(1):123–140. Scholar
  10. 10.
    Jiang D, Zhuang DF, Huang YH, Wang JH, Fu JY (2013) Evaluating the spatiotemporal variation of China’s offshore wind resources based on remotely sensed wind field data. Renew Sust Energy Rev 24:142–148CrossRefGoogle Scholar
  11. 11.
    Nghiem SV, Leshkevich GA, Stiles BW (2004) Wind fields over the Great Lakes measured by the SeaWinds scatterometer on the QuikSCAT satellite. J Great Lakes Res 30(1):148–165CrossRefGoogle Scholar
  12. 12.
    Capps SB, Zender CS (2010) Estimated global ocean wind power potential from QuikSCAT observations, accounting for turbine characteristics and siting. J Geophys Res 115:D09101. Scholar
  13. 13.
    Arun Kumar SVV, Prajapati J, Kumar R (2016) Evaluation of offshore wind energy resources for power generation based on scatterometer and SAR data along the Indian coast. In: Frouin RJ, Shenoi SC, Rao KH (eds) Proceedings of the SPIE—In remote sensing of the oceans and inland waters: techniques, applications, and challenges, vol 9878, pp 1–8Google Scholar
  14. 14.
    Garlapati N, Surendra SK, Natansh KN, Vimal S (2016) Application of reanalysis data to estimate offshore wind potential in EEZ of India based on marine ecosystem considerations. Elsevier Ltd, pp 0360–5442.
  15. 15.
    Gadad S, Paresh CD (2016) Offshore wind power resource assessment using Oceansat-2 scatterometer data at a regional scale. Elsevier LtdGoogle Scholar
  16. 16.
    Vogelzang J, Stoffelen A, Verhoef A, Figa-Saldana J (2011) On the quality of high resolution scatterometer winds. J Geophys Res 116.
  17. 17.
    Hasagar CB, Mouche A, Badger M, Bingol F, Karagali I, Driesenaar T, Stoffelen Ad, Pena A, Longepe N (2015) Offshore wind climatology based on synergetic use of Envisat ASAR, ASCAT and QuikSCAT. Remote Sens Environ 156:247–263CrossRefGoogle Scholar
  18. 18.
    Verhoef A, Jur V, Verspeek J, Stoffelen A (2017). Long-term scatterometer wind climate data records. IEEE J Sel Top Appl Earth Observ Remote Sens 10(5). Scholar
  19. 19.
    Chakraborty A, Kumar R, Stoffelen Ad (2013) Validation of ocean surface winds from the OCEANSAT-2 scatterometer using triple collocation. Remote Sens Lett 4(1):84–93. Scholar
  20. 20.
    Ulgen K, Hepbasli A (2002) Determination of Weibull parameters for wind energy analysis in of Izmir Turkey. Int J Energy Res 26:495–506CrossRefGoogle Scholar
  21. 21.
    Justus CG, Hargraves R, Mikhail A, Graber D (1977) Methods for estimating wind speed frequency distributions. J Appl Meteorol 17:350–353CrossRefGoogle Scholar
  22. 22.
    Celik AN (2003) Weibull representative compressed wind speed data for energy and performance calculations of wind energy systems. Energy Convers Manag 44:3057–3072CrossRefGoogle Scholar
  23. 23.
    Akpinar EK, Akpinar S (2005) An assessment of seasonal analysis of wind energy characteristics and wind turbine characteristics. Energy Convers Manag 46:1848–1867CrossRefGoogle Scholar
  24. 24.
    Peixoto JP, Oort AH (1992) Physics of climate. American Institute of Physics & Springer, Verlag, 520 ppCrossRefGoogle Scholar
  25. 25.
    Safari B, Gasore J (2010) A statistical investigation of wind characteristics and wind energy potential based on the Weibull and Rayleigh models in Rwanda. Renew Energy 35:2874–2880CrossRefGoogle Scholar
  26. 26.
    Mathew S (2006) Wind energy: fundamentals, resource analysis and economics. Springer-Verlang, Berlin, Heidelberg, p 2006CrossRefGoogle Scholar
  27. 27.
    Garlapati N, Ravi SR, Natansh KN, Surendra SK, Vimal S (2015) Application of OSCAT satellite data for offshore wind power potential assessment of India. Energy Procedia 90:89–98Google Scholar
  28. 28.
    Central Electricity Authority. Load generation balance report 2017–18. New Delhi: 2017.

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Surisetty V. V. Arun Kumar
    • 1
    Email author
  • Jagdish Prajapati
    • 2
  • Raj Kumar
    • 1
  1. 1.Earth, Ocean, Atmosphere, Planetary Sciences and Applications Area (EPSA)Space Applications Centre (ISRO)AhmedabadIndia
  2. 2.Department of MathematicsGujarat UniversityAhmedabadIndia

Personalised recommendations