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Temporal and Spatial Wind Information Production and Correction Algorithm Development by Land Cover Type over the Republic of Korea

한반도 시공간적 바람정보 생산과 토지피복별 보정 알고리즘 개발

  • 김도용 (부경대학교 BK21 지구환경시스템사업단) ;
  • 한경수 (부경대학교 환경해양대학 공간정보시스템공학과)
  • Received : 2012.05.23
  • Accepted : 2012.07.26
  • Published : 2012.09.30

Abstract

Wind is an important variable for various scientific communities such as meteorology, climatology, and renewable energy. In this study, numerical simulations using WRF mesoscale model were performed to produce temporal and spatial wind information over the Republic of Korea during 2006. Although the spatial features and monthly variations of the near-surface wind speed were well simulated in the model, the simulated results overestimated the observed values as a whole. To correct these simulated wind speeds, a regression-based statistical algorithm with different constants and coefficients by land cover type was developed using the satellite-derived LST and NDWI. The corrected wind speeds for the algorithm validation showed strong correlation and close agreement with the observed values for each land cover type, with nearly zero mean bias and less than 0.4 m/s RMSE. Therefore, the proposed algorithm using remotely sensed surface observations may be useful for correcting simulated near-surface wind speeds and producing more accurate wind information over the Republic of Korea.

바람은 기상, 기후, 신재생 에너지 등 다양한 분야에서 널리 활용되는 매우 중요한 요소 중 하나이다. 이 연구에서는 우선, 중규모 기상모델 WRF를 이용하여 우리나라의 전역에 대하여 2006년도를 대상으로 수치 시뮬레이션을 수행하여, 시공간적 상세 바람정보를 생산하였다. 수치모의 된 풍속은 관측풍속과 비교하여 공간적 및 계절적 특징을 비교적 잘 나타내었으나, 전반적으로 다소 과대 모의되는 경향을 보였다. 이러한 예측오차를 줄이기 위하여, 위성원격탐사로부터 생산된 지표특성 변수인 LST와 NDWI를 사용한 예측풍속의 통계적 보정 알고리즘을 개발하고, 다중회귀분석에 의하여 보정식의 토지피복별 상수와 계수를 도출하였다. 제안된 보정 알고리즘에 의하여 최종적으로 보정된 풍속은 관측풍속과 비교하여 높은 상관관계, 0.4 m/s 미만의 RMSE, 0에 가까운 BIAS로 매우 높은 일치성을 보였다. 따라서, 이 연구에서 제안한 위성원격탐사자료를 활용한 통계적 보정 알고리즘은 중규모 수치모의에 의한 예측오차를 개선하고 보다 정확한 한반도 바람정보를 생산하는데 있어서 간략하고 유용한 수단이 될 수 있으리라 기대된다.

Keywords

References

  1. 김기홍, 윤준희, 김백석, 2010, 강원도 기상데이터를 이용한 풍속 지도 제작, 한국지형공간정보학회지, 한국지형공간정보학회, 제18권, 1호, pp. 31-39.
  2. 김현구, 장문석, 이은정, 2008, 제주도 풍력자원 데이터 베이스 구축을 위한 기상통계분석, 한국환경과학회지, 한국환경과학회, 제17권, 6호, pp. 591-599. https://doi.org/10.5322/JES.2008.17.6.591
  3. 김현구, 최재우, 이화운, 정우식, 2005, 한반도 바람지도 구축에 관한 연구: I. 원격탐사자료자료에 의한 종관바람지도 구축, 한국신재생에너지학회지, 한국신재생에너지학회, 제1권, 1호, pp. 44-53.
  4. 변재영, 최영진, 서범근, 2010, 중규모 모델 WRF로부터 모의된 한반도 풍력‐기상자원 특성, 대기, 한국기상학회, 제20권, 2호, pp. 195-210.
  5. 이수갑, 2005, 풍력발전의 기술현황 및 전망, 한국신재생에너지학회지, 한국신재생에너지학회, 제1권, 1호, pp. 15-23.
  6. 이순환, 이화운, 김동혁, 김민정, 김현구, 2009, 한반도 풍력 자원 지도의 공간 해상도가 풍력자원 예측 정확도에 미치는 영향에 관한 수치연구, 한국환경과학회지, 한국환경과학회, 제18권, 8호, pp. 885-897. https://doi.org/10.5322/JES.2009.18.8.885
  7. 장문석, 방형준, 2009, 풍력발전기술의 현황과 전망, 한국환경과학회지, 한국환경과학회, 제18권, 8호, pp. 933-940. https://doi.org/10.5322/JES.2009.18.8.933
  8. Bentamy, A. and Fillon, D.C., 2012, Gridded surface wind fields from Metop/ASCAT measurements, International Journal of Remote Sensing, Taylor & Francis, Vol. 33, No. 6, pp. 1729-1754. https://doi.org/10.1080/01431161.2011.600348
  9. Burlando, M., Podesta, A., Villa, L., Ratto, C.F. and Cassulo, G., 2009, Preliminary estimate of the largescale wind energy resource with few measurements available: The case of Montenegro, Journal of Wind Engineering and Industrial Aerodynamics, Elsevier Ltd., Vol. 97, No. 11-12, pp. 497-511. https://doi.org/10.1016/j.jweia.2009.07.011
  10. Damousis, I.G., Alexiadis, M.C., Theocharis, J.B. and Dokopoulos, P.S., 2004, A fuzzy model for wind speed prediction and power generation in wind parks using spatial correlation, IEEE Transactions on Energy Conversion, IEEE PES, Vol. 19, No. 2, pp. 352-361. https://doi.org/10.1109/TEC.2003.821865
  11. Darecki, M. and Stramski, D., 2004, An evaluation of MODIS and SeaWiFS bio‐optical algorithms in the Baltic Sea, Remote Sensing of Environment, Elsevier Inc., Vol. 89, No. 3, pp. 326-350. https://doi.org/10.1016/j.rse.2003.10.012
  12. Dudhia, J., 1989, Numerical study of convection observed during the winter monsoon experiment using a mesoscale two‐dimensional model, Journal of the Atmospheric Sciences, AMS, Vol. 46, No. 20, pp. 3077-3107. https://doi.org/10.1175/1520-0469(1989)046<3077:NSOCOD>2.0.CO;2
  13. Dudhia, J., 1996, A multi‐layer soil temperature model for MM5, 6th Annual PSU/NCAR Mesoscale Model (MM5) Users Workshop, Pennsylvania State University, Boulder, CO, USA.
  14. Hong, S.‐Y., Dudhia, J. and Chen, S.‐H., 2004, A revised approach to ice microphysical processes for the bulk parameterization of cloud and precipitation, Monthly Weather Review, AMS, Vol. 132, No. 1, pp. 103-120. https://doi.org/10.1175/1520-0493(2004)132<0103:ARATIM>2.0.CO;2
  15. Hong, S.‐Y., Moon, N.‐K, Lim, K.‐S. and Kim, J.‐W., 2010, Future climate change scenarios over Korea using a multi‐nested downscaling system: A pilot study, Asia‐Pacific Journal of Atmospheric Sciences, KMS, Vol. 46, No. 4, pp. 425-435. https://doi.org/10.1007/s13143-010-0024-1
  16. Hong, S.‐Y., Noh, Y. and Dudhia, J., 2006, A new vertical diffusion package with an explicit treatment of entrainment processes, Monthly Weather Review, AMS, Vol. 134, No. 9, pp. 2318-2341. https://doi.org/10.1175/MWR3199.1
  17. J.S. and Fritsch, J.M., 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, American Meteorological Society, Boston, MA, USA. 246pp.
  18. Kim, D.‐H., Lee, H.‐W. and Lee, S.‐H., 2010, Evaluation of wind resource using numerically optimized data in the southwestern Korean Peninsula, Asia-Pacific Journal of Atmospheric Sciences, KMS, Vol. 46, No. 4, pp. 393-403. https://doi.org/10.1007/s13143-010-0021-4
  19. Kim, D.‐Y., Oh, J.‐H., Kim, J.‐Y., Sen, P. and Kim, T.‐K., 2009, An attempt of estimation of annual fog frequency over gyeongsangbuk‐do of Korea using weather generator MM5, Environmental Engineering Research, KSEE, Vol. 14, No. 2, pp. 88-94. https://doi.org/10.4491/eer.2009.14.2.088
  20. Liu, H., Tian, H.‐Q., Chen, C. and Li, Y.‐F., 2010, A hybrid statistical method to predict wind speed and wind power, Renewable Energy, Elsevier Ltd., Vol. 35, No. 8, pp. 1857-1861. https://doi.org/10.1016/j.renene.2009.12.011
  21. Mlawer, E.J., Taubman, S.J., Brown, P.D., Iacono M.J. and Clough, S.A., 1997, Radiative transfer for inhomogeneous atmosphere: RRTM, a validated correlated‐k model for the long‐wave, Journal of Geophysical Research, AGU, Vol. 102, No. D14, pp. 16663-16682. https://doi.org/10.1029/97JD00237
  22. Oh, J.‐H., Kim, T., Kim, M.‐K., Lee, S.‐H., Min S.‐K. and Kwon, W.‐T., 2004, Regional climate simulation for Korea using dynamic downscaling and statistical adjustment, Journal of the Meteorological Society of Japan, MSJ, Vol. 82, No. 6, pp. 1629-1643. https://doi.org/10.2151/jmsj.82.1629
  23. Salcedo‐Sanz, S., Perez‐Bellido, A.M., Ortiz‐García, E.G., Portilla‐Figueras, A., Prieto, L. and Paredes, D., 2009, Hybridizing the fifth generation mesoscale model with artificial neural networks for short‐term wind speed prediction, Renewable Energy, Elsevier Ltd., Vol. 34, No. 6, pp. 1451-1457. https://doi.org/10.1016/j.renene.2008.10.017
  24. Sideratos, G. and Hatziargyriou, N.D., 2007, An advanced statistical method for wind power forecasting, IEEE Transactions on Power Systems, IEEE PES, Vol. 22, No. 1, pp. 258-265. https://doi.org/10.1109/TPWRS.2006.889078
  25. Skamarock, W.C., Klemp, J.B., Dudhia, J., Gill, D.O., Barker, D.M., Duda, M.G., Huang, X.‐Y., Wang, W. and Powers, J.G., 2008, A description of the advanced research WRF version 3, NCAR Tech. Note NCAR/TN‐475+STR, National Center for Atmospheric Research, Boulder, CO, USA, 125pp.
  26. Storm, B., Dudhia, J., Basu, S., Swift, A. and Giammarco, I., 2009, Evaluation of the weather research and forecasting model on forecasting lowlevel jets: Implications for wind energy, Wind Energy, John Wiley & Sons, Ltd., Vol. 12, No. 1, pp. 81-90. https://doi.org/10.1002/we.288
  27. Wang, W., Bruyere, C., Duda, M., Dudhia, J., Gill, D., Lin, H.‐C., Michalakes, J., Rizvi, S. and Zhang, X., 2011, Weather Research & Forecasting, ARW version 3 modeling system user's guide, http://www.mmm.ucar.edu/wrf/users/docs/user_guide_V3.
  28. Xu, Q., Lin, H., Li, X., Zuo, J., Zheng, Q., Pichel, W.G. and Liu, Y., 2010, Assessment of an analytical model for sea surface wind speed retrieval from spaceborne SAR, International Journal of Remote Sensing, Taylor & Francis, Vol. 31, No. 4, pp. 993-1008. https://doi.org/10.1080/01431160902922870
  29. Yu, Y., Tarpley, D., Privette, J.L., Goldberg, M.D., Raja, M.K.R.V., Vinnikov, K.Y. and Xu, H., 2009, Developing algorithm for operational GOES‐R land surface temperature product, IEEE Transactions on Geoscience and Remote Sensing, IEEE GRSS, Vol. 47, No. 3, pp. 936-951. https://doi.org/10.1109/TGRS.2008.2006180
  30. Zhao, T.X.‐P., Stowe, L.L., Smirnov, A., Crosby, D., Sapper, J. and Mcclain, C.R., 2002, Development of a global validation package for satellite oceanic aerosol optical thickness retrieval based on AERONET observation and its application to NOAA/NESDIS operational aerosol retrievals, Journal of the Atmospheric Sciences, AMS, Vol. 59, No. 3, pp. 294-312. https://doi.org/10.1175/1520-0469(2002)059<0294:DOAGVP>2.0.CO;2