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Investigation of the Assimilated Surface Wind Characteristics for the Evaluation of Wind Resources

풍력자원 평가를 위한 바람자료 동화 특성 평가

  • Lee, Hwa-Woon (Department of Earth Environment System, Pusan National University) ;
  • Kim, Min-Jung (Department of Earth Environment System, Pusan National University) ;
  • Kim, Dong-Hyeuk (Department of Earth Environment System, Pusan National University) ;
  • Kim, Hyun-Goo (Korea Institute of Energy Research) ;
  • Lee, Soon-Hwan (BK21 Coastal Environment System School, Pusan National University)
  • 이화운 (부산대학교 지구환경시스템학부) ;
  • 김민정 (부산대학교 지구환경시스템학부) ;
  • 김동혁 (부산대학교 지구환경시스템학부) ;
  • 김현구 (한국에너지기술연구원) ;
  • 이순환 (부산대학교 BK2l 연안환경시스템 사업단)
  • Published : 2009.02.28

Abstract

Wind energy has been recognized as one of the most important and fastest growing energy resources without emission of air pollutant. Thus, it is necessary to predict wind speed and direction accurately both in time and space toward the efficient usage of wind energy. Numerical simulation experiments using the Fifth-Generation Mesoscale Model (MM5) are carried out to clarify the impact of surface observation data assimilation on the estimation of wind energy resources. The EXP_Radius run was designed with respect to the radius of influence in the Four-Dimensional Data Assimilation (FDDA), and the EXP_Impact run was made by changing the nudging coefficient that determines the relative magnitude of the nudging term. The simulation period covers a clear-sky event on 3 - 5 June 2007 and another is on 2 - 4 December 2006. It is found that the simulated results are very sensitive to the radius of influence and nudging parameters in the FDDA. The further analysis of the results shows that the impact of the radius of influence tends to be stronger in weak synoptic flow episode than that in strong synoptic flows episode. The nudging factor is also sensitive to the intensity of the synoptic flows.

Keywords

References

  1. 김철희, 송창근 (2002) 4차원 자료동화 기법을 이용한 해안가 대기 순환의 수치 실험, 한국환경영향평가학회지, 11(2), 79-93
  2. 김현구, 최재우 (2002) 풍력에너지 이용 및 개발현황, RIST 연구논문, 16(4), 479-485
  3. 류찬수, 신유미, 이순환(2004) 해안지형 복잡성이 중규모 순환장에 미치는 영향에 관한 수치실험, 한국기상학회지, 40(1), 71-86
  4. 이순환, 김민정, 이화운(2007a) 국지규모 풍력에너지 평가를 위한 기상 관측 자료의 영향 반경 특성, 한국대기환경학회지, 23(5), 585-595 https://doi.org/10.5572/KOSAE.2007.23.5.585
  5. 이순환, 이화운, 김동혁, 김현구 (2007b) 한반도 풍력자원 평가를 위한 초기 공간해상도와 위성자료 동화의 관계 분석, 한국대기환경학회지, 23(6), 653-665 https://doi.org/10.5572/KOSAE.2007.23.6.653
  6. 이화운, 원혜영, 최현정, 김현구 (2005) 광양만권에서의 자료동화된 대기유동장이 대기오염 물질의 확산장에 미치는 영향에 관한 수치모의, 한국대기환경학회지, 21(2), 169-178
  7. Grell, G.A., J. Dudhia, and D.R. Stauffer (1994) A description of the fifth-Generation Penn State/NCAR mesoscale model (MM5). NCAR Technical Note, NCAR/TN-398+ST, 117pp
  8. Hong, S.-Y. and H.-L. Pan (1996) Nonlocal boundary layer vertical diffusion in a Medium-Range Forecast model. Mon. Wea. Rev., 124, 2322-2339 https://doi.org/10.1175/1520-0493(1996)124<2322:NBLVDI>2.0.CO;2
  9. Lee, S.-H., Y.-K. Kim, H.-S. Kim, and H.-W. Lee (2007) Influence of dense surface meteorological data assimilation on the prediction accuracy of ozone pollution in the southeastern coastal area of the Korean Peninsula, Atmospheric Environment, 41, 4451-4465 https://doi.org/10.1016/j.atmosenv.2007.01.050
  10. Lee, S.-H., D.-H. Kim, and H.-W. Lee (2008) Satellite-based assessment of the impact of sea-surface winds on regional atmospheric circulations over the Korean Peninsula, International Journal of Remote Sensing, 29(2), 331-354 https://doi.org/10.1080/01431160701241928
  11. Lindskog, M. (2004) Doppler radar wind data assimilation with HIRLAM 3DVAR, Mon. Wea. Rev., 132, 1081-1092 https://doi.org/10.1175/1520-0493(2004)132<1081:DRWDAW>2.0.CO;2
  12. 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 correlatedk model for the longwave, Journal of Geophysical Research, 102(D14), 1663-1682 https://doi.org/10.1029/97JD00237
  13. National Center for Atmospheric Research (NCAR) (1994) Terrain and Land Use for the Fifth-Generation Penn State/NCAR Mesoscale Modeling System (MM5) Program TERRAIN, NCAR Technical Notes, NCAR/TN-397+IA
  14. National Center for Atmospheric Research (NCAR) (2003) PSU/NCAR Mesoscale Modeling System, Tutorial class notes and user’s guide : MM5 modeling system version 3
  15. Pielke, R.A., Sr. (2002), Mesoscale Meteorological Modeling, 2nd ed., 676 pp., Elsevier, New York
  16. Reisner, J., R.J. Rasmussen, and R.T. Bruintjes (1998) Explicit forecasting of supercooled liquid water in winter storms using the MM5 mesoscale model. Quarterly Journal of the Royal Meteorological Society, 124B, 1071-1107 https://doi.org/10.1002/qj.49712454804
  17. Talagrand, O. (1997) Assimilation of observation, an introduction, J. Meteo. Soc. Japan, 75(1), 191-209

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