Advertisement

Theoretical and Applied Climatology

, Volume 135, Issue 3–4, pp 1449–1464 | Cite as

Performance of the CORDEX regional climate models in simulating offshore wind and wind potential

  • Sumeet KulkarniEmail author
  • M. C. Deo
  • Subimal Ghosh
Original Paper
  • 137 Downloads

Abstract

This study is oriented towards quantification of the skill addition by regional climate models (RCMs) in the parent general circulation models (GCMs) while simulating wind speed and wind potential with particular reference to the Indian offshore region. To arrive at a suitable reference dataset, the performance of wind outputs from three different reanalysis datasets is evaluated. The comparison across the RCMs and their corresponding parent GCMs is done on the basis of annual/seasonal wind statistics, intermodel bias, wind climatology, and classes of wind potential. It was observed that while the RCMs could simulate spatial variability of winds, well for certain subregions, they generally failed to replicate the overall spatial pattern, especially in monsoon and winter. Various causes of biases in RCMs were determined by assessing corresponding maps of wind vectors, surface temperature, and sea-level pressure. The results highlight the necessity to carefully assess the RCM-yielded winds before using them for sensitive applications such as coastal vulnerability and hazard assessment. A supplementary outcome of this study is in form of wind potential atlas, based on spatial distribution of wind classes. This could be beneficial in suitably identifying viable subregions for developing offshore wind farms by intercomparing both the RCM and GCM outcomes. It is encouraging that most of the RCMs and GCMs indicate that around 70% of the Indian offshore locations in monsoon would experience mean wind potential greater than 200 W/m2.

Notes

Acknowledgments

This study has been carried out under the aegis of ADB TA-8652 IND: Climate-Resilient Coastal Protection and Management Project (CRCPMP). Authors acknowledge the World Climate Research Program’s Working Group on Coupled Modeling for producing and making available the model outputs. Authors acknowledge the modeling groups viz. the Centre for Climate Change Research (CCCR-IITM) for RegCM4 and partner institutions Rossby Centre, Swedish Meteorological and Hydrological Institute (SMHI), Sweden, for RCA4, for generating and disseminating the CORDEX South Asia multi-model dataset. The authors sincerely thank Prof. Manas Behera, IIT Bombay for his constructive suggestions and comments. Thanks are also due to Ms. Swati Singh and Ms. Piyali Choudhary who provided great help in downloading the CORDEX data.

References

  1. Balog I, Ruti PM, Tobin I, Armenio V, Vautard R (2016) A numerical approach for planning offshore wind farms from regional to local scales over the Mediterranean. Renew Energy 85:395–405.  https://doi.org/10.1016/j.renene.2015.06.038 CrossRefGoogle Scholar
  2. Beniston M, Stephenson DB, Christensen OB, Ferro C, Frei C, Goyette S, Halsnaes K, Holt T, Jylhä K, Koffi B, Palutikof J, Schöll R, Semmler T, Woth K (2007) Future extreme events in European climate: an exploration of regional climate projections. Clim Chang 81(S1):71–95.  https://doi.org/10.1007/s10584-006-9226-z CrossRefGoogle Scholar
  3. Carolin Mabel M, Fernandez E (2008) Analysis of wind power generation and prediction using ANN: a case study. Renew Energy 33(5):986–992.  https://doi.org/10.1016/j.renene.2007.06.013 CrossRefGoogle Scholar
  4. Christensen JH et al (2007) Regional climate projections. In: Solomon S et al (eds) Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge Univ. Press, Cambridge, U. K., pp 847–940Google Scholar
  5. Cliff WC (1977) Effect of generalized wind characteristics on annual power estimates from wind turbine generators (No. PNL-2436). Battelle Pacific Northwest Labs., RichlandGoogle Scholar
  6. Crossley JF, Polcher J, Cox PM, Gedney N, Planton S (2000) Uncertainties linked to land-surface processes in climate change simulations. ClimDyn 16:949–961Google Scholar
  7. Endris HS, Omondi P, Jain S, Lennard C, Hewitson B, Chang’a L et al (2013) Assessment of the performance of CORDEX regional climate models in simulating East African rainfall. J Clim 26(21):8453–8475.  https://doi.org/10.1175/JCLI-D-12-00708.1 CrossRefGoogle Scholar
  8. Gao X, Shi Y, Zhang D, Wu J, Giorgi F, Ji Z, Wang Y (2012) Uncertainties in monsoon precipitation projections over China: results from two high-resolution RCM simulations. Clim Res 52:213–226.  https://doi.org/10.3354/cr01084 CrossRefGoogle Scholar
  9. Giorgi F, Francisco R (2000) Uncertainties in regional climate change prediction: a regional analysis of ensemble simulations with the HADCM2 coupled AOGCM. ClimDyn 16:169–182Google Scholar
  10. Glotter M, Elliott J, McInerney D, Best N, Foster I, Moyer EJ (2014) Evaluating the utility of dynamical downscaling in agricultural impacts projections. Proc Natl AcadSci USA 111(24):8776–8781.  https://doi.org/10.1073/pnas.1314787111 CrossRefGoogle Scholar
  11. Hofherr T, Kunz M (2010) Extreme wind climatology of winter storms in Germany. Clim Res 41:105–123.  https://doi.org/10.3354/cr00844 CrossRefGoogle Scholar
  12. International Energy Agency, IEA Wind, Annu Rep, 2011Google Scholar
  13. IPCC Fifth Assessment Report: Climate Change, (2013), p. AR5.Google Scholar
  14. Justus CG, Hargraves WR, Mikhail A (1976a) Reference wind speed distributions and height profiles for wind turbine design and performance evaluation applications. Technical Report, AugustCrossRefGoogle Scholar
  15. Justus CG, Hargraves WR, & Mikhail A (1976b) Reference wind speed distributions and height profiles for wind turbine design and performance evaluation applications.[USA] (No. ORO-5108-76/4). Georgia Inst. of Tech., Atlanta (USA). School of Aerospace EngineeringGoogle Scholar
  16. Kulkarni S, Deo MC, Ghosh S (2016) Evaluation of wind extremes and wind potential under changing climate for Indian offshore using ensemble of 10 GCMs. Ocean & Coastal Management 121:141–152.  https://doi.org/10.1016/j.ocecoaman.2015.12.008 CrossRefGoogle Scholar
  17. Kunz M, Mohr S, Rauthe M, Lux R, Kottmeier C (2010) Assessment of extreme wind speeds from regional climate models—part 1: estimation of return values and their evaluation. Nat Hazards Earth Syst Sci 10(4):907–922.  https://doi.org/10.5194/nhess-10-907-2010 CrossRefGoogle Scholar
  18. Leckebusch GC, Renggli D, Ulbrich U (2008) Development and application of an objective storm severity measure for the Northeast Atlantic Region. Meteorol Z 17(5):575–587.  https://doi.org/10.1127/0941-2948/2008/0323 CrossRefGoogle Scholar
  19. Leung, L. R., L. O. Mearns, F. Giorgi, and R. L. Wilby (2003), Regional climate research—needs and opportunities, Bull Am Meteorol Soc 84(1), 89–95, doi: https://doi.org/10.1175/BAMS-84-1-89., Regional Climate Research
  20. Leung LR, Kuo YH, Tribbia J (2006) Research needs and directions of regional climate modeling using WRF and CCSM. Bull AmMeteorol Soc 87(12):1747–1751.  https://doi.org/10.1175/BAMS-87-12-1747 CrossRefGoogle Scholar
  21. Lileo, S., Petrik, O., 2000.Investigation on the use of NCEP/NCAR, MERRA and NCEP/ CFSR reanalysis data in wind resource analysis.sigma 1 (2).Google Scholar
  22. Lucas-Picher P, Christensen JH, Saeed F, Kumar P, Asharaf S, Ahrens B, Wiltshire AJ, Jacob D, Hagemann S (2011) Can regional climate models represent the Indian monsoon? J Hydrometeorol 12(5):849–868.  https://doi.org/10.1175/2011JHM1327.1 CrossRefGoogle Scholar
  23. Neetu S, Shetye S, Chandramohan P (2006) Impact of sea breeze on wind-seas off Goa, west coast of India. J Earth System Sci 115(2):229–234.  https://doi.org/10.1007/BF02702036 CrossRefGoogle Scholar
  24. Nikulin G, Jones C, Giorgi F, Asrar G, Büchner M, Cerezo-Mota R et al (2012) Precipitation climatology in an ensemble of CORDEX-Africa regional climate simulations. J Clim 25(18):6057–6078.  https://doi.org/10.1175/JCLI-D-11-00375.1 CrossRefGoogle Scholar
  25. Pryor SC, Barthelmie RJ, Kjellström E (2005) Potential climate change impact on wind energy resources in northern Europe: analyses using a regional climate model. Clim Dyn 25(7–8):815–835.  https://doi.org/10.1007/s00382-005-0072-x CrossRefGoogle Scholar
  26. Räisänen J, Hansson U, Ullerstig A, Döscher R, Graham LP, Jones C, Meier M, Samuelsson P, Willén U (2003) SMHI reports meteorology and climatology. No 101:61Google Scholar
  27. Rockel B, Woth K (2007) Extremes of near-surface wind speed over Europe and their future changes as estimated from an ensemble of RCM simulations. Clim Chang 81(S1):267–280.  https://doi.org/10.1007/s10584-006-9227-y CrossRefGoogle Scholar
  28. Sharp E, Dodds P, Barrett M, Spataru C (2015) Evaluating the accuracy of CFSR reanalysis hourly wind speed forecasts for the UK, using in situ measurements and geographical information. Renew Energy 77:527e538CrossRefGoogle Scholar
  29. Singh S, Ghosh S, Sahana AS, Vittal H, Karmakar S (2016) Do dynamic regional models add value to the global model projections of Indian monsoon? Clim Dyn:1–23Google Scholar
  30. The Wind Energy Resource Atlas of the United States (Prepared for the U.S. Department of Energy, 2006Google Scholar
  31. Torma C, Giorgi F, Coppola E (2015) Added value of regional climate modeling over areas characterized by complex terrain—precipitation over the Alps. J Geophys Res: Atmos 120(9):3957–3972.  https://doi.org/10.1002/2014JD022781 Google Scholar
  32. Wang YQ, Leung LR, McGregor JL, Lee DK, Wang WC, Ding YH, Kimura F (2004) Regional climate modeling: progress, challenges, and prospects. J Meteorol Soc Jpn 82(6):1599–1628.  https://doi.org/10.2151/jmsj.82.1599 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Civil EngineeringIndian Institute of Technology BombayMumbaiIndia

Personalised recommendations