Asia-Pacific Journal of Atmospheric Sciences

Korean Meteorological Society (한국기상학회)
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Mesoscale Simulations of Multi-Decadal Variability in the Wind Resource over Korea

  • Kim, Do-Yong (Brain Korea 21 Graduate School of Earth Environmental System, Pukyong National University) ;
  • Kim, Jin-Young (Department of Environmental Atmospheric Sciences, Pukyong National University) ;
  • Kim, Jae-Jin (Department of Environmental Atmospheric Sciences, Pukyong National University)
  • Published : 2013.02.28
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Abstract

This study investigated multi-decadal variability in the wind resource over the Republic of Korea using the Weather Research and Forecasting (WRF) mesoscale meteorological model. Mesoscale simulations were performed for the period from November 1981 to November 2010. The typical wind climatology over the Korean Peninsula, which is influenced by both continental and oceanic features, was represented by the physics-based mesoscale simulations. Winter had windier conditions with northwesterly flows, whereas less windy with southwesterly flows appeared in summer. The annual mean wind speeds over the Republic of Korea were approximately $2ms^{-1}$ with strong wind in mountainous areas, coastal areas, and islands. The multi-decadal variability in wind speed during the study period was characterized by significant increases (positive trend) over many parts of the study area, even though the various local trends appeared depending on the station locations. The longterm trend in the spatially averaged wind speed was approximately $0.002ms^{-1}yr^{-1}$. The annual frequency of daily mean wind speeds over $5ms^{-1}$ at the turbine hub height also increased during the study period throughout the Republic of Korea. The present study demonstrates that multi-decadal mesoscale simulations can be useful for climatological assessment of wind energy potential.

Keywords

References

  1. ABS Energy Research, 2010: The wind power report. 7th ed., UK, 695 pp.
  2. Ahmet, A. S., and O Guler, 2010: Evaluation of wind energy investment interest and electricity generation cost analysis for Turkey. Applied Energy, 87, 2574-2580. https://doi.org/10.1016/j.apenergy.2010.03.015
  3. Ayotte, K. W., 2008: Computational modelling for wind energy assessment. J. Wind Eng. Ind. Aerodyn., 96, 1571-1590. https://doi.org/10.1016/j.jweia.2008.02.002
  4. Breslow, P. B., and D. J. Sailor, 2002: Vulnerability of wind power resources to climate change in the continental United States. Renewable Energy, 27, 585-598. https://doi.org/10.1016/S0960-1481(01)00110-0
  5. Burlando, M., A. Podesta L. Villa, C. F. Ratto, and G. Cassulo, 2009: Preliminary estimate of the large-scale wind energy resource with few measurements available: The case of Montenegro. J. Wind Eng. Ind. Aerodyn., 97, 497-511. https://doi.org/10.1016/j.jweia.2009.07.011
  6. Cadenas, E., and W. Rivera, 2009: Short term wind speed forecasting in La Venta, Oaxaca, Mexico, using artificial neural networks. Renewable Energy, 34, 274-278. https://doi.org/10.1016/j.renene.2008.03.014
  7. Chotamonsak, C., E. P. Salathe J. Kreasuwan, S. Chantara, and K. Siriwitayakom, 2011: Projected climate change over Southeast Asia simulated using a WRF regional climate model. Atmos. Sci. Lett., 129, 213-219.
  8. Damousis, I. G., M. C. Alexiadis, J. B. Theocharis, and P. S. Dokopoulos, 2004: A fuzzy model for wind speed prediction and power generation in wind parks using spatial correlation. IEEE Transactions on Energy Conversion, 19, 352-361. https://doi.org/10.1109/TEC.2003.821865
  9. Dudhia, J., 1989: Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model. J. Atmos. Sci., 46, 3077-3107. https://doi.org/10.1175/1520-0469(1989)046<3077:NSOCOD>2.0.CO;2
  10. Dudhia, J., 1996: A multi-layer soil temperature model for MM5. 6th Annual PSU/NCAR Mesoscale Model (MM5) Users Workshop, Penn. State Univ., Boulder, CO., USA.
  11. Frank, H. P., and L. Landberg, 1997: Modelling the wind climate of Ireland. Boundary-Layer Meteorol., 85, 359-378. https://doi.org/10.1023/A:1000552601288
  12. Gokcek, M., A. Bayulken, and S. Bekdemir, 2007: Investigation of wind characteristics and wind energy potential in Kirklareli, Turkey. Renewable Energy, 32, 1739-1752. https://doi.org/10.1016/j.renene.2006.11.017
  13. Gokcek, M., and G. M. Serdar, 2009: Evaluation of electricity generation and energy cost of wind energy conversion systems (WECSs) in Central Turkey. Applied Energy, 86, 2731-2739. https://doi.org/10.1016/j.apenergy.2009.03.025
  14. Guo, H., M. Xu, and Q. Hu, 2011: Changes in near-surface wind speed in China: 1969-2005. Int. J. Climatol., 31, 349-358. https://doi.org/10.1002/joc.2091
  15. Hong, S.-Y., J. Dudhia, and S.-H. Chen, 2004: A revised approach to ice microphysical processes for the bulk parameterization of cloud and precipitation. Mon. Wea. Rev., 132, 103-120. https://doi.org/10.1175/1520-0493(2004)132<0103:ARATIM>2.0.CO;2
  16. Hong, S.-Y., N.-K Moon, K.-S. Lim, and J.-W. Kim, 2010: Future climate change scenarios over Korea using a multi-nested downscaling system: A pilot study. Asia-Pacific J. Atmos. Sci., 46, 425-435. https://doi.org/10.1007/s13143-010-0024-1
  17. Hong, S.-Y., Y. Noh, and J. Dudhia, 2006: A new vertical diffusion package with an explicit treatment of entrainment processes. Mon. Wea. Rev., 134, 2318-2341. https://doi.org/10.1175/MWR3199.1
  18. Ian, M., 1996: The status and prospects for wind energy. Renewable Energy, 8, 29-33. https://doi.org/10.1016/0960-1481(96)88815-X
  19. IPCC (Intergovernmental Panel on Climate Change), 2007: Climate Change 2007, The physical science basis - Contribution of working group I to the fourth assessment report of the IPCC. Cambridge University Press, UK, 996 pp.
  20. Jimenez, B., F. Durante, B. Lange, T. Kreutzer, and J. Tambke, 2007: Offshore wind resource assessment with WAsP and MM5: Comparative study for the German Bight. Wind Energy, 10, 121-134. https://doi.org/10.1002/we.212
  21. Kain, J. S., and J. M. Fritsch, 1993: Convective parameterization for mesoscale models: The Kain-Fritsch scheme, The representation of cumulus convection in numerical models. edited by K. A. Emanuel and D. J. Raymond, Am. Meteorol. Soc., Boston, MA., USA, 246 pp.
  22. Kanamitsu, M., W. Ebisuzaki, J. Woollen, S.-K. Yang, J. J. Hnilo, M. Fiorino, and G. L. Potter, 2002: NCEP-DOE AMIP-II Reanalysis (R-2). Bull. Amer. Meteor. Soc., 83, 1631-1643. https://doi.org/10.1175/BAMS-83-11-1631
  23. Klink, K., 1999: Trends in mean monthly maximum and minimum surface wind speeds in the coterminous United States, 1961 to 1990. Climate Res., 13, 193-205. https://doi.org/10.3354/cr013193
  24. Koo, M.-S., and S.-Y. Hong, 2010: Diurnal variations of simulated precipitation over East Asia in two regional climate models. J. Geophys. Res., 115, D05105, doi:10.1029/2009JD012574.
  25. Koo, M.-S., S.-Y. Hong, and J. Kim, 2009: An evaluation of the tropical rainfall measuring mission (TRMM) multi-satellite precipitation analysis (TMPA) data over South Korea. Asia-Pacific J. Atmos. Sci., 45, 265-282.
  26. Lennon, J. J., and J. R. G. Turner, 1995: Predicting the spatial distribution of climate: Temperature in Great-Britain. J. Animal Ecology, 64, 370-392. https://doi.org/10.2307/5898
  27. Li, Z., Z. W. Yan, K. Tu, W. D. Liu, and Y. C. Wang, 2011: Changes in wind speed and extremes in Beijing during 1960-2008 based on homogenized observations. Adv. Atmos. Sci., 28, 408-420. https://doi.org/10.1007/s00376-010-0018-z
  28. Lindzen, R. S., and S. Nigam, 1987: On the role of sea surface temperature gradients in forcing low-level winds and convergence in the tropics. J. Atmos. Sci., 44, 2418-2436. https://doi.org/10.1175/1520-0469(1987)044<2418:OTROSS>2.0.CO;2
  29. Liu, H., H.-Q. Tian, C. Chen, and Y.-F. Li, 2010: A hybrid statistical method to predict wind speed and wind power. Renewable Energy, 35, 1857-1861. https://doi.org/10.1016/j.renene.2009.12.011
  30. Mari, R., L. Bottai, C. Busillo, F. Calastrini, B. Gozzini, and G. Gualtieri, 2011: A GIS-based interactive web decision support system for planning wind farms in Tuscany (Italy). Renewable Energy, 36, 754-763. https://doi.org/10.1016/j.renene.2010.07.005
  31. Migoya, E., A. Crespo, A. Jimenez, J. Garcia, and F. Manuel, 2007: Wind energy resource assessment in Madrid region. Renewable Energy, 32, 1467-1483. https://doi.org/10.1016/j.renene.2006.06.015
  32. Mlawer, E. J., S. J. Taubman, P. D. Brown, M. J. Iacono, and S. A. Clough, 1997: Radiative transfer for inhomogeneous atmosphere: RRTM, a validated correlated-k model for the long-wave. J. Geophys. Res., 102(D14), 16663-16682. https://doi.org/10.1029/97JD00237
  33. Ohunakin, O. S., 2011: Wind resource evaluation in six selected high altitude locations in Nigeria. Renewable Energy, 36, 3273-3281. https://doi.org/10.1016/j.renene.2011.04.026
  34. Pirazzoli, P. A., and A. Tomasin, 2003: Recent near-surface wind changes in the central Mediterranean and Adriatic areas. Int. J. Climatol., 23, 963-973. https://doi.org/10.1002/joc.925
  35. Raisanen, J., U. Hansson, A. Ullerstig, R. Doscher, L. P. Graham, C. Jones, H. E. M. Meier, P. Samuelsson, and U. Willen, 2004: European climate in the late 21st century: regional simulations with two driving global models and two forcing scenarios. Climate Dyn., 22, 13-31. https://doi.org/10.1007/s00382-003-0365-x
  36. Renolds, R. W., and T. M. Smith, 1994: Improved global sea surface temperature analyses using optimum interpolation. J. Climate, 7, 929-948. https://doi.org/10.1175/1520-0442(1994)007<0929:IGSSTA>2.0.CO;2
  37. Salathe, E. P., L. R. Leung, Y. Qian, and Y. Zhang, 2010: Regional climate model projections for the State of Washington. Climatic Change, 102, 51-75. https://doi.org/10.1007/s10584-010-9849-y
  38. Salathe, E. P., R. Steed, C. F. Mass, and P. H. Zahn, 2008: A high-resolution climate model for the U. S. Pacific Northwest: Mesoscale feedbacks and local responses to climate change. J. Climate, 21, 5708-5726. https://doi.org/10.1175/2008JCLI2090.1
  39. Salcedo-Sanz, S., A. M. Perez-Bellido, E. G. Ortiz-Garcia, A. Portilla- Figueras, L. Prieto, and D. Paredes, 2009: Hybridizing the fifth generation mesoscale model with artificial neural networks for shortterm wind speed prediction. Renewable Energy, 34, 1451-1457. https://doi.org/10.1016/j.renene.2008.10.017
  40. Sideratos, G., and N. D. Hatziargyriou, 2007: An advanced statistical method for wind power forecasting. IEEE Transactions on Power Systems, 22, 258-265. https://doi.org/10.1109/TPWRS.2006.889078
  41. Skamarock, W. C., J. B. Klemp, J. Dudhia, D. O. Gill, D. M. Barker, M. G. Duda, X.-Y. Huang, W. Wang, and J. G. Powers, 2008: A description of the advanced research WRF version 3, NCAR Tech. Note NCAR/TN- 475+STR, Natl. Cent. for Atmos. Res., Boulder, CO., USA, 125 pp.
  42. Storm, B., J. Dudhia, S. Basu, A. Swift, and I. Giammarco, 2009: Evaluation of the weather research and forecasting model on forecasting lowlevel jets: Implications for wind energy. Wind Energy, 12, 81-90. https://doi.org/10.1002/we.288
  43. Tuller, S., 2004: Measured wind speed trends on the west coast of Canada. Int. J. Climatol., 24, 1359-1374. https://doi.org/10.1002/joc.1073
  44. Vancutsem, C., P. Ceccato, T. Dinku, and S. J. Connor, 2010: Evaluation of MODIS land surface temperature data to estimate air temperature in different ecosystems over Africa. Remote Sens. Environ., 114, 449-465. https://doi.org/10.1016/j.rse.2009.10.002
  45. Wan, H., X. L. Wang, and V. R. Swail, 2010: Homogenization and trend analysis of Canadian near-surface wind speeds. J. Climate, 23, 1209-1225. https://doi.org/10.1175/2009JCLI3200.1
  46. Wang, W., C. Bruyere, M. Duda, J. Dudhia, D. Gill, H.-C. Lin, J. Michalakes, S. Rizvi, and X. Zhang, 2011: Weather Research & Forecasting, ARW version 3 modeling system user's guide. [Available online at http://www.mmm.ucar.edu/wrf/users/docs/user_guide_V3]
  47. Yim, S. H. L., J. C. H. Fung, and A. K. H. Lau, 2009: Mesoscale simulation of year-to-year variation of wind power potential over southern China. Energies, 2, 340-361. https://doi.org/10.3390/en20200340

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