서남권 해상풍력단지 유지보수 활동을 위한 중기 파고 예보 개선
Improvement of Wave Height Mid-term Forecast for Maintenance Activities in Southwest Offshore Wind Farm
0
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
In order to secure the safety of increasing offshore activities such as offshore wind farm maintenance and fishing, IMPACT, a mid-term marine weather forecasting system, was established by predicting marine weather up to 7 days in advance. Forecast data from the Korea Hydrographic and Oceanographic Agency (KHOA), which provides the most reliable marine meteorological service in Korea, was used, but wind speed and wave height forecast errors increased as the leading forecast period increased, so improvement of the accuracy of the model results was needed. The Model Output Statistics (MOS) method, a post-correction method using statistical machine learning, was applied to improve the prediction accuracy of wave height, which is an important factor in forecasting the risk of marine activities. Compared with the observed data, the wave height prediction results by the model before correction for 6 to 7 days ahead showed an RMSE of 0.692 m and R of 0.591, and there was a tendency to underestimate high waves. After correction with the MOS technique, RMSE was 0.554 m and R was 0.732, confirming that accuracy was significantly improved.
Abstract
In order to secure the safety of increasing offshore activities such as offshore wind farm maintenance and fishing, IMPACT, a mid-term marine weather forecasting system, was established by predicting marine weather up to 7 days in advance. Forecast data from the Korea Hydrographic and Oceanographic Agency (KHOA), which provides the most reliable marine meteorological service in Korea, was used, but wind speed and wave height forecast errors increased as the leading forecast period increased, so improvement of the accuracy of the model results was needed. The Model Output Statistics (MOS) method, a post-correction method using statistical machine learning, was applied to improve the prediction accuracy of wave height, which is an important factor in forecasting the risk of marine activities. Compared with the observed data, the wave height prediction results by the model before correction for 6 to 7 days ahead showed an RMSE of 0.692 m and R of 0.591, and there was a tendency to underestimate high waves. After correction with the MOS technique, RMSE was 0.554 m and R was 0.732, confirming that accuracy was significantly improved.
참고문헌 (14)
- 유승협, "지역 파랑 예측시스템과 해양기상 부이의 파랑 특성 비교 연구", 한국해양공학회지, 24(6), pp.7~15 (2010), 한국해양공학회
- 도기덕, "기상청 현업 예보 바람자료를 이용한 동해안 동계 파랑 예측 재현도 연구", 한국해안·해양공학회논문집, 30(5), pp.223~233 (2018), 한국해안,해양공학회[DOI]
- Test Bed for 2.5GW Offshore Wind Farm at Yellow Sea - Design Basis Final Report, "Korea Electric Power Research Institute", ~, pp.TR.70B9.P2016.0299 (2016)
- 김지영, "해상풍력터빈 기초 구조물 설치로 인한 파랑거동 변화 검토", 한국해안·해양공학회논문집, 22(5), pp.306~315 (2010), 한국해안,해양공학회
- Suh, I. -S., "Integrated Operation System Development for Offshore Wind Power and Aquaculture Facility", The 2022 Spring Conference of Korea Wind Energy Association, pp.113~115 (2022)
- Glahn, H. R., "The use of Model Output Statistics(MOS)in objective weather forecasting", Journal of Applied Meteorology, 11(8), pp.1203~1211 (1972)
- Glahn, H. R., "TDL Office Note 00-1:MOS-2000", Techniques Development Laboratory, ~ (2000)
- Carter, G. M., "Statistical Forecasts Based on the National Meteorological Center's Numerical Weather Prediction System", Weather and Forecasting, 4(3), pp.401~412 (1989)
- Schmeits, M. J., "Probabilistic Forecasting of(Severe)Thunderstorms in the Netherlands Using Model Output Statistics", Weather and Forecasting, 20(2), pp.134~148 (2005)
- Investigating MSG-SEVIRI data as an additional predictor source in the KNMI probabilistic(severe)thunderstorm forecasting system, "van Gastel, V.", Royal Netherlands Meteorological Institute, ~ (2012)
- Wilson, L., "The Canadian Updateable Model Output Statistics(UMOS)System : Design and Development Tests", Weather and Forecasting, 17(2), pp.206~222 (2002)
- 강전호, "Development of Updateable Model Output Statistics (UMOS) System for Air Temperature over South Korea", Asia-Pacific Journal of Atmospheric Sciences, 47(2), pp.199~211 (2011), 한국기상학회
- "XGBoost Documentations"[URL]
- "Buoyweather service"[URL]