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Korean Meteorological Society (한국기상학회)
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Analyses of the Meteorological Characteristics over South Korea for Wind Power Applications Using KMAPP

고해상도 규모상세화 수치자료 산출체계를 이용한 남한의 풍력기상자원 특성 분석

  • 윤진아 (국립기상과학원 미래기반연구부) ;
  • 김연희 (국립기상과학원 미래기반연구부) ;
  • 최희욱 (국립기상과학원 미래기반연구부)
  • Received : 2020.09.10
  • Accepted : 2020.11.13
  • Published : 2021.03.31
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Abstract

High-resolution wind resources maps (maps, here after) with spatial and temporal resolutions of 100 m and 3-hours, respectively, over South Korea have been produced and evaluated for the period from July 2016 to June 2017 using Korea Meteorological Administration (KMA) Post Processing (KMAPP). Evaluation of the 10 m- and 80 m-level wind speed in the new maps (KMAPP-Wind) and the 1.5 km-resolution KMA NWP model, Local Data Assimilation and Prediction System (LDAPS), shows that the new high-resolution maps improves of the LDAPS winds in estimating the 10m wind speed as the new data reduces the mean bias (MBE) and root-mean-square error (RMSE) by 33.3% and 14.3%, respectively. In particular, the result of evaluation of the wind at 80 m which is directly related with power turbine shows that the new maps has significantly smaller error compared to the LDAPS wind. Analyses of the new maps for the seasonal average, maximum wind speed, and the prevailing wind direction shows that the wind resources over South Korea are most abundant during winter, and that the prevailing wind direction is strongly affected by synoptic weather systems except over mountainous regions. Wind speed generally increases with altitude and the proximity to the coast. In conclusion, the evaluation results show that the new maps provides significantly more accurate wind speeds than the lower resolution NWP model output, especially over complex terrains, coastal areas, and the Jeju island where wind-energy resources are most abundant.

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References

  1. Allouhi, A., O. Zamzoum, M. R. Islam, R. Saidur, T. Kousksou, A. Jamil, and A. Derouich, 2017: Evaluation of wind energy potential in Morocco's coastal regions. Renew. Sustain. Energy Rev., 72, 311-324, doi:10.1016/j.rser.2017.01.047.
  2. Carvalho, D., A. Rocha, M. Gomez-Gesteira, and C. Santos, 2012: A sensitivity study of the WRF model in wind simulation for an area of high wind energy. Environ. Modell. Softw., 33, 23-34, doi:10.1016/j.envsoft.2012. 01.019.
  3. Choi, D., Y. Hwang, and Y. H. Lee, 2020: Observing sensitivity experiment based on convective scale model for upper-air observation data on GISANG 1 (KMA Research Vessel) in summer 2018. Atmosphere, 30, 17-30, doi:10.14191/Atmos.2020.30.1.017 (in Korean with English abstract).
  4. Giannaros, T. M., D. Melas, and I. Ziomas, 2017: Performance evaluation of the Weather Research and Forecasting (WRF) model for assessing wind resource in Greece. Renew. Energy, 102, 190-198, doi:10.1016/j.renene.2016.10.033.
  5. Goncalves-Ageitos, M., A. Barrera-Escoda, J. M. Baldasano, and J. Cunillera, 2015: Modelling wind resources in climate change scenarios in complex terrains. Renew. Energy, 76, 670-678, doi:10.1016/j.renene.2014.11.066.
  6. Gualtieri, G., and S. Secci, 2011: Wind shear coefficients, roughness length and energy yield over coastal locations in Southern Italy. Renew. Energy, 36, 1081-1094, doi:10.1016/j.renene.2010.09.001.
  7. GWEC, 2017: Global wind report 2017. Global Wind Energy Council, 72 pp.
  8. Hernandez-Escobedo, Q., 2016: Wind energy assessment for small urban communities in the Baja California peninsula, Mexico. Energies, 9, 805, doi:10.3390/en9100805.
  9. Howard, T., and P. Clark, 2007: Correction and downscaling of NWP wind speed forecasts. Meteor. Appl., 14, 105-116. https://doi.org/10.1002/met.12
  10. Kim, D.-W., and H.-R. Byun, 2008: Spatial and temporal distribution of wind resources over Korea. Atmosphere, 18, 171-182 (in Korean with English abstract).
  11. Kim, D.-W., J. Kim, Y.-H. Kang, and H.-D. Park, 2016: Classification of wind sector in Pohang region using similarity of time-series wind vectors. J. Korean Solar Energy, 36, 11-18, doi:10.7836/kses.2016.36.1.011 (in Korean with English abstract).
  12. Kim, D.-W., M.-S. Jang, and E.-J. Lee, 2008: Meteor-statistical analysis for establishment of Jejudo wind resource database. J. Environ. Sci. Int., 17, 591-599 (in Korean with English abstract). https://doi.org/10.5322/JES.2008.17.6.591
  13. Kim, S., H.-J. Song, and H. Lee, 2019: Mesoscale features and forecasting guidance of heavy rain types over the Korean peninsula. Atmosphere, 29, 463-480, doi:10.14191/Atmos.2019.29.4.463 (in Korean with English abstract).
  14. Kwon, S.-D., 2008: Assessment of wind energy potential based on short-term measurement. J. Wind Eng. Inst. Korea, 12, 165-171 (in Korean with English abstract).
  15. Lee, S.-H., and G.-S. Park, 2004: Study on evolution of mesoscale vortex induced by isolated mountain - case of Hanla mountain in Jeju island. J. Korean Meteor. Soc., 40, 191-201 (in Korean with English abstract).
  16. Lim, K.-S. S., J.-M. Lim, H.-H. Shin, J. Hong, Y.-Y. Ji, and W. Lee, 2019: Impacts of subgrid-scale orography parameterization on simulated atmospheric fields over Korea using a high-resolution atmospheric forecast model. Meteorol. Atmos. Phys., 131, 975-985, doi:10.1007/s00703-018-0615-4.
  17. Liu, J., C. Y. Gao, J. Ren, Z. Gao, H. Liang, and L. Wang, 2018: Wind resource potential assessment using a long term tower measurement approach: A case study of Beijing in China. J. Clean. Prod., 174, 917-926, doi:10.1016/j.jclepro.2017.10.347.
  18. Martin Mederos, A. C., J. F. Medina Padron, and A. E. Feijoo Lorenzo, 2011: An offshore wind atlas for the Canary Islands. Renew. Sustain. Energy Rev., 15, 612-620, doi:10.1016/j.rser.2010.08.005.
  19. Moon, S. J., J. W. Ko, and B. G. Lee, 2013: Power law exponent in coastal area of northeastern Jeju island for the investigation of wind resource. J. Korean Soc. GIS, 21, 65-71, doi:10.7319/kogsis.2013.21.4.065 (in Korean with English abstract).
  20. Nawri, N., G. N. Petersen, H. Bjornsson, A. N. Hahmann, K. Jonasson, C. B. Hasager, and N.-E. Clausen, 2014: The wind energy potential of Iceland. Renew. Energy, 69, 290-299, doi:10.1016/j.renene.2014.03.040.
  21. Oh, K.-Y., J.-Y. Kim, J.-K. Lee, M.-S. Ryu, and J.-S. Lee, 2012: An assessment of wind energy potential at the demonstration offshore wind farm in Korea. Energy, 46, 555-563, doi:10.1016/j.energy.2012.07.056.
  22. Oke, T. R., 1987: Boundary layer climates. Psychology Press, 435 pp.
  23. Ouammi, A., H. Dagdougui, R. Sacile, and A. Mimet, 2010: Monthly and seasonal assessment of wind energy characteristics at four monitored locations in Liguria region (Italy). Renew. Sustain. Energy Rev., 14, 1959-1968, doi:10.1016/j.rser.2010.04.015.
  24. Ryu, G. H., J. S. Ko, M. G. Baek, and J. K. Jang, 2018: Analysis for vertical wind shear change at coastal area according to the sea surface temperature. J. Korean Soc. Hazard Mitig., 18, 505-514, doi:10.9798/KOSHAM.2018.18.2.505 (in Korean with English abstract).
  25. Seo, B.-K., Y.-H., Kim, J.-H. Kim, and B.-J. Kim, 2017: Comparison of wind resource characteristics in Korea according to different mapping method. New. Renew. Energy, 13, 23-38, doi:10.7849/ksnre.2017.12.13.4.023 (in Korean with English abstract).
  26. Seol, D.-I., 2006: Climatological characteristics of monthly wind distribution in a greater coasting area of Korea. J. Korean Soc. Marine Environ. Safety, 12, 185-192 (in Korean with English abstract).
  27. Sim, H.-N., and Y.-H. Lee, 2017: Influence of local wind on occurrence of fog at inland areas. Atmosphere, 27, 213-224, doi:10.14191/Atmos.2017.27.2.213 (in Korean with English abstract).
  28. Ushiyama, I., 2013: Wind energy. Ohmsha, Ltd. And Seong An Dang Publishing Co., I, 74 pp.
  29. WNA, 2018: World nuclear performance report 2018. World Nuclear Association, Rep. No. 2018/004, 36 pp. [Available online at https://www.world-nuclear.org/getmedia/b392d1cd-f7d2-4d54-9355-9a65f71a3419/performance-report.pdf.aspx].
  30. Yim, S. H. L., J. C. H. Fung, A. K. H. Lau, and S. C. Kot, 2007: Developing a high-resolution wind map for a complex terrain with a coupled MM5/CALMET system. J. Geophys. Res. Atmos., 112, D05106.