Skip to main content
SpringerLink
Log in

Comparative Study and Trend Analysis of Regional Climate Models and Reanalysis Wind Speeds at Rameshwaram

  • Conference paper
  • First Online:
Advances in Clean Energy Technologies

Part of the book series: Springer Proceedings in Energy ((SPE))

Abstract

Climate change may affect wind patterns. It will impact wind energy generation. Climate models will help to assess how wind speed is affected by climate change. Climate models have different boundary conditions, and the forcing variables lead to the uncertainty of data. The present study provides a comparison of six regional climate models (RCMs) with reanalysis data (ERA-Interim) and validated with measured data at Rameshwaram. Further quantile mapping technique has been used for the removal of bias from RCM models. Results show that all six RCMs have lesser correlation (~0.50), high bias (~1.4 m/s) with measured data before quantile mapping. However, after the quantile mapping with reanalysis data, the RCMs achieved a higher correlation (~0.63) and less bias (~0.45). Further, the trends of wind speeds for all RCMs have been analysed and checked the significance of trends with the t-test. Results show that wind speed trends are increasing with 0.03 m/s/decade at Rameshwaram.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Abbreviations

CCCR:

Centre for Climate Change Research.

CORDEX:

Coordinated Regional Climate Downscaling Experiment

ECMWF:

European Centre for Medium-Range Weather Forecasts

ERA:

ECMWF Reanalysis

GCM:

General circulation model

ICTP:

India with the help of the Abdus Salam International Centre for Theoretical Physics

IITM:

Indian Institute of Tropical Meteorology

NIWE:

National Institute of Wind Energy

RCM:

Regional climate model

RCP:

Representative concentration pathways

RCAO:

Rossby Centre coupled regional climate model

QM:

Quantile mapping

SD:

Standard deviation

Fs:

The cumulative distribution function (CDF)

Fo−1:

The inverse CDF function

M:

Measured wind speeds

S:

RCM model wind speeds

Qm(t):

Simulated data from the RCM

Qs(t):

Bias-corrected data

Rg:

Regression function of wind speed time series

t:

Year from 1979 to 2005

References

  1. P. De Jong, T.B. Barreto, C.A.S. Tanajura, D. Kouloukoui, K.P. Oliveira-esquerre, A. Kiperstok, E.A. Torres, Estimating the impact of climate change on wind and solar energy in Brazil using a South American regional climate model. Renew. Energy. (2019). https://doi.org/10.1016/j.renene.2019.03.086

    Article  Google Scholar 

  2. K. Solaun, E. Cerdá, Impacts of Climate Change on Wind Energy Power—Four Wind Farms in Spain (2019). https://doi.org/10.1016/j.renene.2019.06.129

  3. X. Costoya, D. Carvalho, M. Gómez-gesteira, On the suitability of offshore wind energy resource in the United States of America for the 21st century. Appl. Energy. 262, 114537 (2020). https://doi.org/10.1016/j.apenergy.2020.114537

    Article  Google Scholar 

  4. S. Kulkarni, M.C. Deo, S. Ghosh, Performance of the CORDEX regional climate models in simulating offshore wind and wind potential. Theor. Appl. Climatol. 135, 1449–1464 (2019). https://doi.org/10.1007/s00704-018-2401-0

    Article  Google Scholar 

  5. H. Koch, S. Vögele, F.F. Hattermann, S. Huang, The impact of climate change and variability on the generation of electrical power. Meteorol. Zeitschrift. 24, 173–188 (2015). https://doi.org/10.1127/metz/2015/0530

    Article  Google Scholar 

  6. D. Ganea, E. Mereuta, L. Rusu, Estimation of the near future wind power potential in the black sea. Energies 11 (2018). https://doi.org/10.3390/en11113198

  7. I. Tobin, S. Jerez, R. Vautard, F. Thais, E. Van Meijgaard, A. Prein, Climate change impacts on the power generation potential of a European mid-century wind farms scenario. Environ. Res. Lett. 11 (n.d.) 34013. https://doi.org/10.1088/1748-9326/11/3/034013

  8. T. Janes, F. Mcgrath, I. Macadam, R. Jones, Science of the total environment high-resolution climate projections for South Asia to inform climate impacts and adaptation studies in the Ganges-Brahmaputra-Meghna and Mahanadi deltas. Sci. Total Environ. 650, 1499–1520 (2019). https://doi.org/10.1016/j.scitotenv.2018.08.376

    Article  Google Scholar 

  9. Y. Gao, S. Ma, T. Wang, The impact of climate change on wind power abundance and variability in China. Energy 189, 116215 (2019). https://doi.org/10.1016/j.energy.2019.116215

    Article  Google Scholar 

  10. P.M.M. Soares, D.C.A. Lima, R.M. Cardoso, M.L. Nascimento, A. Semedo, Western Iberian offshore wind resources: More or less in a global warming climate? Appl. Energy. 203, 72–90 (2017). https://doi.org/10.1016/j.apenergy.2017.06.004

    Article  Google Scholar 

  11. Y. Wang, L.R. Leung, D. Lee, W. Wang, Y. Ding, Rcm_Review03, J. Meteorol. Soc. Japan. 82, 1–30 (2004). papers2://publication/uuid/56A6022E-970F-40C8–8E96-F624F1D6AA1A

    Google Scholar 

  12. P. Lucas-Picher, J.H. Christensen, F. Saeed, P. Kumar, S. Asharaf, B. Ahrens, A.J. Wiltshire, D. Jacob, S. Hagemann, Can regional climate models represent the Indian monsoon? J. Hydrometeorol. 12, 849–868 (2011). https://doi.org/10.1175/2011JHM1327.1

    Article  Google Scholar 

  13. S.C. Pryor, R.J. Barthelmie, E. Kjellström, Potential climate change impact on wind energy resources in northern Europe: Analyses using a regional climate model. Clim. Dyn. 25, 815–835 (2005). https://doi.org/10.1007/s00382-005-0072-x

    Article  Google Scholar 

  14. D. Carvalho, A. Rocha, M. Gómez-Gesteira, C. Silva Santos, Potential impacts of climate change on European wind energy resource under the CMIP5 future climate projections, Renew. Energy. 101, 29–40 (2017). https://doi.org/10.1016/j.renene.2016.08.036.

  15. D.W. Pierce, T.P. Barnett, B.D. Santer, P.J. Gleckler, Selecting global climate models for regional climate change studies. Proc. Natl. Acad. Sci. U. S. A. 106, 8441–8446 (2009). https://doi.org/10.1073/pnas.0900094106

    Article  Google Scholar 

  16. H. Li, J. Sheffield, E.F. Wood, Bias correction of monthly precipitation and temperature fields from Intergovernmental Panel on Climate Change AR4 models using equidistant quantile matching, J. Geophys. Res. Atmos. 115 (2010). https://doi.org/10.1029/2009JD012882

Download references

Acknowledgements

“The World Climate Research Programme's Working Group on Regional Climate and the Working Group on Coupled Modelling, the former coordinating body of CORDEX and responsible panel for CMIP5 are gratefully acknowledged. The climate modelling groups, ICTP are sincerely thanked for producing and making available their model output. The authors thank the Earth System Grid Federation (ESGF) infrastructure and the Climate Data Portal hosted at the Centre for Climate Change Research (CCCR), Indian Institute of Tropical Meteorology (IITM) for providing CORDEX South Asia data”. We would like to thank the ECMWF for making available the ERA-Interim reanalysis product at https://www.ecmwf.int/datasets. We would also thank NIWE for making the measured wind speeds available at Rameshwaram.

Author information

Authors and Affiliations

  1. School of Technology, Pandit Deendayal Energy University, Gandhinagar, 382007, India

    B. Abhinaya Srinivas, Garlapati Nagababu, Hardik Jani & Surendra Singh Kachhwaha

Authors
  1. B. Abhinaya Srinivas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Garlapati Nagababu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hardik Jani
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Surendra Singh Kachhwaha
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Energy, Energy Centre, Maulana Azad National Institute of Technology, Bhopal, Madhya Pradesh, India

    Prashant V. Baredar

  2. Department of Mechanical Engineering, Dr. B. R. Ambedkar National Institute of Technology Jalandhar, Jalandhar, Punjab, India

    Srinivas Tangellapalli

  3. Department of Energy Science and Engineering, Indian Institute of Technology Bombay, Mumbai, Maharashtra, India

    Chetan Singh Solanki

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Abhinaya Srinivas, B., Nagababu, G., Jani, H., Singh Kachhwaha, S. (2021). Comparative Study and Trend Analysis of Regional Climate Models and Reanalysis Wind Speeds at Rameshwaram. In: Baredar, P.V., Tangellapalli, S., Solanki, C.S. (eds) Advances in Clean Energy Technologies . Springer Proceedings in Energy. Springer, Singapore. https://doi.org/10.1007/978-981-16-0235-1_61

Download citation

  • DOI: https://doi.org/10.1007/978-981-16-0235-1_61

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-0234-4

  • Online ISBN: 978-981-16-0235-1

  • eBook Packages: EnergyEnergy (R0)

Publish with us

Policies and ethics

Search

Navigation