한국해안·해양공학회논문집 (Journal of Korean Society of Coastal and Ocean Engineers)

  • 제30권2호
  • /
  • Pages.84-94
  • /
  • 2018
  • /
  • 1976-8192(pISSN)
  • /
  • 2288-2227(eISSN)
한국해안·해양공학회 (Korean Society of Coastal and Ocean Engineers)
권호 표지
DOI QR코드

DOI QR Code

앙상블 지역 파랑예측시스템 구축 및 검증

Development and Evaluation of an Ensemble Forecasting System for the Regional Ocean Wave of Korea

  • 박종숙 (국립기상과학원 지구시스템연구과) ;
  • 강기룡 (국립기상과학원 지구시스템연구과) ;
  • 강현석 (국립기상과학원 지구시스템연구과)
  • Park, JongSook (Earth System Research Division, National Institute of Meteorological Sciences) ;
  • Kang, KiRyong (Earth System Research Division, National Institute of Meteorological Sciences) ;
  • Kang, Hyun-Suk (Earth System Research Division, National Institute of Meteorological Sciences)
  • 투고 : 2018.04.04
  • 심사 : 2018.04.18
  • 발행 : 2018.04.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

해양파랑 예측에 있어 단일 수치모델의 불확실성을 보완하기 위하여 앙상블 기법을 적용한 지역 파랑예측시스템을 구축하였다. 기상청 전지구 대기 수치모델의 확률예측시스템에서 생산되는 24개 앙상블 해상풍을 입력자료로 이용, 87시간까지 파랑 예측자료를 생산하였으며, 기상청 계류부이 관측자료와 다양한 통계방법을 적용하여 검증을 수행하였다. 2일예측 이후의 앙상블 예측평균의 평균제곱근오차(RMSE)는 단일모델예측에 비하여 향상된 결과를 보였으며, 특히 3일예측의 경우 단일모델예측 대비 RMSE가 약 15% 정도 향상되었다. 이것은 앙상블 기법이 수치모델의 불확실성을 감소시켜 예측정확도 향상에 크게 기여한 것으로 보인다. ROC(Relative Operating Characteristic) 분석결과, 전체 예측시간에 대하여 ROC 영역이 모두 0.9 이상을 보여 확률예측 성능이 뛰어남을 보였으며, 앙상블 파랑예측 결과가 해상 확률예보에 유용하게 활용될 수 있을 것으로 판단된다.

In order to overcome the limitation of deterministic forecast, an ensemble forecasting system for regional ocean wave is developed. This system predicts ocean wind waves based on the meteorological forcing from the Ensemble Prediction System for Global of the Korea Meteorological Administration, which is consisted of 24 ensemble members. The ensemble wave forecasting system is evaluated by using the moored buoy data around Korea. The root mean squared error (RMSE) of ensemble mean showed the better performance than the deterministic forecast system after 2 days, especially RMSE of ensemble mean is improved by 15% compared with the deterministic forecast for 3-day lead time. It means that the ensemble method could reduce the uncertainty of the deterministic prediction system. The Relative Operating Characteristic as an evaluation scheme of probability prediction was bigger than 0.9 showing high predictability, meaning that the ensemble wave forecast could be usefully applied.

키워드

참고문헌

  1. Anderson, J.L. (1996). A method for producing and evaluating probabilistic forecasts from ensemble model integrations. Journal of Climate, 9(7), 1518-1530. https://doi.org/10.1175/1520-0442(1996)009<1518:AMFPAE>2.0.CO;2
  2. Ardhuin, F., Rogers, E., Babanin, A.V., Filipot, J.-F., Magne, R., Roland, A., Westhuysen, A., Queffeulou, P., Lefevre, J.-M., Aouf, L. and Collard, F. (2010). Semiempirical dissipation source functions for ocean waves. Part I: Definition, calibration, and validation. Journal of Physical Oceanography, 40(9), 1917-1941. https://doi.org/10.1175/2010JPO4324.1
  3. Barker, T.W. (1991). The relationship between spread and forecast error in extended-range forecasts. Journal of Climate, 4(7), 733-742. https://doi.org/10.1175/1520-0442(1991)004<0733:TRBSAF>2.0.CO;2
  4. Berner, J., Shutts, G.J., Leutbecher, M. and Palmer, T.N. (2009). A spectral stochastic kinetic energy backscatter scheme and its impact on flow-dependent predictability in the ECMWF ensemble prediction system. Journal of the Atmospheric Sciences, 66(3), 603-626. https://doi.org/10.1175/2008JAS2677.1
  5. Buizza, R., Houtekamer, P.L., Pellerin, G., Toth, Z., Zhu, Y. and Wei, M. (2005). A comparison of the ECMWF, MSC, and NCEP global ensemble prediction systems. Monthly Weather Review, 133(5), 1076-1097. https://doi.org/10.1175/MWR2905.1
  6. Bunney, C.C., Li, J.-G. and Saulter, A. (2012). Met Office Wave Model ensemble Prediction Systems in the 'Atlantic-Euro Zone', MyWave Report D3.1. December 2012. Met Office, U.K.
  7. Bunney, C. and Saulter, A. (2015). An ensemble forecast system for prediction of Atlantic and UK wind waves. Ocean Modelling, 96, 103-116. https://doi.org/10.1016/j.ocemod.2015.07.005
  8. Cao, D., Tolman, H.L., Chen, H.S., Chawla, A. and Gerald, V.M. (2009). Performance of the ocean wave ensemble forecast system at NCEP. In The 11th International Workshop on Wave Hindcasting & Forecasting and 2nd Coastal Hazards Symposium.
  9. Flowerdew, J., Horsburgh, K., Wilson, C. and Mylne, K. (2010). Development and evaluation of an ensemble forecasting system for coastal storm surges. Quarterly Journal of the Royal Meteorological Society, 136(651), 1444-1456. https://doi.org/10.1002/qj.648
  10. Hamill, T.M. and Colucci, S.J. (1996). Random and systematic error in NMC's short-range Eta ensembles. In Preprints, 13th Conf. on Probability and Statistics in the Atmospheric Sciences, San Francisco, CA, Amer. Meteor. Soc., 51-56.
  11. Hamill, T.M. and Colucci, S.J. (1997). Verification of Eta-RSM short-range ensemble forecasts. Monthly Weather Review, 125(6), 1312-1327. https://doi.org/10.1175/1520-0493(1997)125<1312:VOERSR>2.0.CO;2
  12. Hasselmann, K., Barnett, T.P., Bouws, E., Carlson, H., Cartwright, D.E., Enke, K., Ewing, A., Gienapp, H., Hasselmann, D.E., Kruseman, P., Meerburg, A., Muller, P., Olbers, D.J., Richter, K., Sell, W. and Walden, H. (1973). Measurements of windwave growth and swell decay during the Joint North Sea Wave Project (JONSWAP). Erganzungsheft 8-12.
  13. Lee, B.C., Fan, Y.M., Chuang, L.Z.H. and Kao, C.C. (2009). Parametric sensitivity analysis of the WAVEWATCH III model. Terrestrial, Atmospheric & Oceanic Sciences, 20(2).
  14. Moon, I.-J. and Oh, I.S. (2003). A study on the effect of waves and tides on storm surge using a coupled ocean wave-circulation model. Asia-Pacific Journal of Atmospheric Sciences, 39(5), 563-574 (in Korean).
  15. Palmer, T.N. (2001). A nonlinear dynamical perspective on model error: A proposal for non-local stochastic-dynamic parametrization in weather and climate prediction models. Quarterly Journal of the Royal Meteorological Society, 127(572), 279-304. https://doi.org/10.1002/qj.49712757202
  16. Park, J.S. and Kang, K.R. (2012). Sensitivity analysis of global wind-wave model. Journal of Korean Society of Coastal and Ocean Engineers, 24(5), 333-342 (in Korean). https://doi.org/10.9765/KSCOE.2012.24.5.333
  17. Saetra, O. and Bidlot, J.R. (2004). Potential benefits of using probabilistic forecasts for waves and marine winds based on the ECMWF ensemble prediction system. Weather and Forecasting, 19(4), 673-689. https://doi.org/10.1175/1520-0434(2004)019<0673:PBOUPF>2.0.CO;2
  18. Tolman, H.L. and Chalikov, D. (1996). Source terms in a third-generation wind wave model. Journal of Physical Oceanography, 26(11), 2497-2518. https://doi.org/10.1175/1520-0485(1996)026<2497:STIATG>2.0.CO;2
  19. You, S.H. and Park, J.S. (2010). Research on wind waves characteristics by comparison of regional wind wave prediction system and ocean buoy data. J. of Korean Society of Ocean Engineers, 24(6), 7-15 (in Korean).
  20. Wang, J., Zhang, J., Yang, J., Bao, W., Wu, G. and Ren, Q. (2017). An evaluation of input/dissipation terms in WAVEWATCH III using in situ and satellite significant wave height data in the South China Sea. Acta Oceanologica Sinica, 36(3), 20-25.