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Evaluation of Surface Wind Forecast over the Gangwon Province using the Mesoscale WRF Model

중규모 수치모델 WRF를 이용한 강원 지방 하층 풍속 예측 평가

  • Seo, Beom-Keun (Applied Meteorological Research Division, National Institute of Meteorological Research) ;
  • Byon, Jae-Young (Global Environment System Research Division, National Institute of Meteorological Research) ;
  • Lim, Yoon-Jin (Applied Meteorological Research Division, National Institute of Meteorological Research) ;
  • Choi, Byoung-Choel (Forecast Research Division, National Institute of Meteorological Research)
  • 서범근 (국립기상과학원 응용기상연구과) ;
  • 변재영 (국립기상과학원 지구환경시스템연구과) ;
  • 임윤진 (국립기상과학원 응용기상연구과) ;
  • 최병철 (국립기상과학원 관측예보연구과)
  • Received : 2014.11.20
  • Accepted : 2015.02.18
  • Published : 2015.04.30

Abstract

This study evaluates the wind speed forecast near the surface layer using the Weather Research Forecasting with Large Eddy Simulation (WRF-LES) model in order to compare the planetary boundary layer (PBL) parameterization with the LES model in terms of different spatial resolution. A numerical simulation is conducted with 1-km and 333-m horizontal resolution over the Gangwon Province including complex mountains and coastal region. The numerical experiments with 1-km and 333-m horizontal resolution employ PBL parameterization and LES, respectively. The wind speed forecast in mountainous region shows a better forecast performance in 333-m experiment than in 1-km, while wind speed in coastal region is similar to the observation in 1-km spatial resolution experiment. Therefore, LES experiment, which directly simulates the turbulence process near the surface layer, contributes to more accurate forecast of surface wind speed in mountainous regions.

큰 에디 모의과정을 포함한 WRF 모델 (WRF-LES)을 이용하여 수치모델의 수평공간 규모에 따른 대기경계층 모수화 실험과 LES 모의 결과를 지표층 근처의 풍속 예측에 대하여 비교하였다. 수치실험은 복잡한 산악지형과 해안지역을 포함하는 강원도 지역에서 수평해상도 1 km와 333 m 실험을 수행하였다. 수평해상도 1 km 실험은 대기경계층 모수화 방안을 채택하였으며, 333 m 실험에서는 LES를 이용하였다. 복잡한 산악지역에서의 풍속 예측의 정확성은 수평해상도 1 km 실험 보다 333 m 실험에서 향상되었으며 해안지역에서는 1 km 실험에서 관측과 더 일치하였다. 지표층 근처의 큰 난류를 직접 계산하는 LES 실험은 산악지역의 풍속예측 개선에 기여하였다.

Keywords

References

  1. Bykjedal, O., and Berge, E., 2009, The use of WRF for wind resource mapping in Norway. 9th WRF user's workshop, National center for atmospheric research, Boulder, CO, USA, 9-18.
  2. Byon, J.-Y., Choi, Y.-J., and Seo, B.-K., 2009, Numerical simulation of local circulation over the Deachung lake area by using the mesoscale model. Journal of Korean Earth Science Society, 30(4), 464-477. (in Korean) https://doi.org/10.5467/JKESS.2009.30.4.464
  3. Byon, J.-Y., Kang, M.-S., and, Jung, H.-S., 2013, Evaluation of wind turbine efficiency of Haengwon wind farm in Jeju island based on Korean wind map. Journal of Korean Earth Science Society, 34(7), 633-633. (in Korean) https://doi.org/10.5467/JKESS.2013.34.7.633
  4. Chou, M.-D., and Suarez, M.J., 1994, An efficient thermal infrared radiation parameterization for use in general circulation model. National Aeronautics Space Administration Technical Memo.104606, 3, Greenbelt, MD, USA, 85 p.
  5. Cultler, N., and Kay, M., 2007, Detecting, categorizing and forecasting large ramps in wind farm power output using meteorological observations and WPPT. Wind Energy, 10, 453-470. https://doi.org/10.1002/we.235
  6. Halpern D., Hollingsworth, A., and Wentz, F., 1994, ECMWF and SSM/I global surface wind speed. Journal of Atmospheric and Oceanic Technology, 11, 779-788. https://doi.org/10.1175/1520-0426(1994)011<0779:EASGSW>2.0.CO;2
  7. Ferreira, C., Game, J., Matias, L., Botterud, A., Wang, J., 2010, A survey on wind power ramp forecasting. Report ANL/DIS-10-13. Argonne National Laboratory, Chicago, IL, USA, 40 p.
  8. Iacono, M.J., Delamere, J.S., Mlawer, E.J., Shephard, M.W., Clough, S.A., and Collins, W.D., 2008, Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models. Journal of Geophysical Research, 113, D13103, doi:10.1029/2008JD009944.
  9. IEA, 2013, Tracking clean energy progress 2013, Paris, France, 154p.
  10. Janjic Z.I. 2002, Nonsingular implementation of Mellor-Yamada Level 2.5 scheme in the NCEP meso model. National Centers for Environment Prediction Office Note, 437, Greenbelt, MD, USA, 61 p.
  11. Kain, J.S., 2004, The Kain-fritsch convective parameterization an update. Journal of Applied Meteorology, 43, 17-181. https://doi.org/10.1175/1520-0450(2004)043<0017:AGBTPI>2.0.CO;2
  12. Kim, H.J., and Noh Y., 1999, Simulation of convective boundary layer using a new large eddy simulation model with the analysis on the effects of subgrid parameterization. Journal of Korean Meteorological Society, 35(4), 587-598. (in Korean)
  13. Lee, S.-H., 2011, A Numerical study on the characteristics of high resolution wind resource in mountainous areas using computational fluid dynamic analysis. Journal of Korean Earth Science Society, 32(1), 46-56. (in Korean) https://doi.org/10.5467/JKESS.2011.32.1.46
  14. Lim, K.-S.S., and Hong, S.-Y., 2010, Development of effective double-moment cloud microphysics scheme with prognostic cloud condensation nuclei (CCN) for weather and climate models. Monthly Weather Review, 138, 1587-1612. https://doi.org/10.1175/2009MWR2968.1
  15. Liu, Y., Warner, T., Liu Y., Vincent C., Wu, Wanli, Mahoney, b., Swerdlin, S., Parks, K., and Boehnert, J., 2011, Simultaneous nested modeling from the synoptic scale to the LES scale for wind energy applications. Journal of Wind Engineering and Industrial Aerodynamics, 99, 308-319. https://doi.org/10.1016/j.jweia.2011.01.013
  16. Mellor, G.L., and Yamada, T., 1974, A hierarchy of turbulence closure models for planetary boundary layers. Journal of Atmospheric Science, 31, 1791-1806. https://doi.org/10.1175/1520-0469(1974)031<1791:AHOTCM>2.0.CO;2
  17. Moeng, C.-H., Dudhia, J., and Sullvian P. 2007, Examining two-way grid nesting for large eddy simulation of the PBL using the WRF model. Monthly Weather Review, 135, 2295-2311. https://doi.org/10.1175/MWR3406.1
  18. Seo, B.-K., Byon, J.-Y., Choi, Y.-J., 2010, Sensitivity evaluation of wind fields in surface layer by WRF-PBL and LSM parameterizations. Atmosphere, 20(3), 219-332. (in Korean)
  19. Skamarock, W.C., Klemp, J.B., Dudhia, J., Gill, D.O., Barker, D.M., Duda, M.G., Huang, X.-Y., Wang, W., Powers, J.G., 2008, A description of the advanced research WRF version 3. NCAR/TN-475+STR, National Center for Atmospheric Research, Bounder, CO, USA, 113 p.
  20. Smirnova, T.G., Brown, J.M., Benjamin, S.G., and Kim, D.S., 2000, Parameterization of cold-season processes in the MAPS land-surface scheme. Journal of Geophysical Research, 105(D03), 4077-4086, doi:10.1029/1999JD901047.
  21. Talbot, C., Bouzeld, E. and Smith, J., Nested mesoscale large-eddy simulation with WRF: performance in real test cases. Journal of Hydrometeorology, 13, 1421-1441.
  22. Wang, W., Bruyere, C., Duda, M., Dudhia, J., Gill, D., Lin, H.C. Michalakes, J., Rizvi, S. and Zhang X., 2010, Weather research & WRF ARW version 3 modeling system user's guide. National Center for Atmospheric Research, Boulder, CO, USA, 350 p.
  23. Yang, Q., Berg, L.K., Pekour, M., Fast J.D., and Newsom R.K., 2013, Evaluation of WRF-predicted near-hubheight winds and ramp events over a pacific northwest site with complex terrain. American Meteorological Society, 52, 1753-1763.
  24. Yim, S.H.L., Fung, J.C.H., Lau, A.K.H., and Kot, S.C., 2007, Developing a high-resolution wind map for a complex terrain with a coupled MM5/CALMET system. Journal of Geophysical Research. 112, D05106, doi:10.1029/2006JD007752.
  25. Yin, X., 2000, Surface wind speed over land: A global view. Journal of Applied Meteorology, 39, 1861-1865. https://doi.org/10.1175/1520-0450(2000)039<1861:SWSOLA>2.0.CO;2

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