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고해상도 기후예측시스템의 표층해류 예측성능 평가

Assessment of Ocean Surface Current Forecasts from High Resolution Global Seasonal Forecast System version 5

이효미|장필훈|강기룡|강현석|김윤재
한국해양과학기술원 (Korea Institute of Ocean Science & Technology)|2018.09|Ocean and Polar Research|제40권 제3호|pp. 99-114

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참고문헌

키워드

validationocean currentGloSea5forecastssurface drifter
주제분류해양학

초록

In the present study, we assess the GloSea5 (Global Seasonal Forecasting System version 5) near-surface ocean current forecasts using globally observed surface drifter dataset. Annual mean surface current fields at 0-day forecast lead time are quite consistent with drifter-derived velocity fields, and low values of root mean square (RMS) errors distributes in global oceans, except for regions of high variability, such as the Antarctic Circumpolar Current, Kuroshio, and Gulf Stream. Moreover a comparison with the global high-resolution forecasting system, HYCOM (Hybrid Coordinate Ocean Model), signifies that GloSea5 performs well in terms of short-range surface-current forecasts. Predictions from 0-day to 4-week lead time are also validated for the global ocean and regions covering the main ocean basins. In general, the Indian Ocean and tropical regions yield relatively high RMS errors against all forecast lead times, whilst the Pacific and Atlantic Oceans show low values. RMS errors against forecast lead time ranging from 0-day to 4-week reveal the largest increase rate between 0-day and 1-week lead time in all regions. Correlation against forecast lead time also reveals similar results. In addition, a strong westward bias of about 0.2 m s-1 is found along the Equator in the western Pacific on the initial forecast day, and it extends toward the Equator of the eastern Pacific as the lead time increases.

Abstract

In the present study, we assess the GloSea5 (Global Seasonal Forecasting System version 5) near-surface ocean current forecasts using globally observed surface drifter dataset. Annual mean surface current fields at 0-day forecast lead time are quite consistent with drifter-derived velocity fields, and low values of root mean square (RMS) errors distributes in global oceans, except for regions of high variability, such as the Antarctic Circumpolar Current, Kuroshio, and Gulf Stream. Moreover a comparison with the global high-resolution forecasting system, HYCOM (Hybrid Coordinate Ocean Model), signifies that GloSea5 performs well in terms of short-range surface-current forecasts. Predictions from 0-day to 4-week lead time are also validated for the global ocean and regions covering the main ocean basins. In general, the Indian Ocean and tropical regions yield relatively high RMS errors against all forecast lead times, whilst the Pacific and Atlantic Oceans show low values. RMS errors against forecast lead time ranging from 0-day to 4-week reveal the largest increase rate between 0-day and 1-week lead time in all regions. Correlation against forecast lead time also reveals similar results. In addition, a strong westward bias of about 0.2 m s-1 is found along the Equator in the western Pacific on the initial forecast day, and it extends toward the Equator of the eastern Pacific as the lead time increases.

참고문헌 (31)

  1. Korea Meteorological Administration, "Development of the diagnostic system for the Korea-UK joint climate prediction system (III)", pp.329~ (2014)
  2. 고은별, "기상청 전지구 해양순환예측시스템(NEMO", NEMOVAR)과 미해군 해양자료동화시스템(HYCOM, 대기, pp.2018, 28(1)[DOI]
  3. 김영호, "한국의 해양예측, 오늘과 내일", 바다, 18(2), pp.89~103 (2013), 한국해양학회
  4. 이상민, "2014년 계절예측시스템과 중기예측모델의 예측성능 비교 및 검증", 대기, 26(1), pp.59~72 (2016), 한국기상학회
  5. 정명일, "GloSea5 모형의 6개월 장기 기후 예측성 검증", 대기, 25(2), pp.323~337 (2015), 한국기상학회
  6. 정영윤, "기상청 계절예측시스템(GloSea5)의 해양성층 강화시기단기 해양예측 정확도 및 대기 - 해양 접합효과", 대기, 26(4), pp.599~615 (2016), 한국기상학회
  7. 함현준, "고해상도 장기예측시스템의 주별 앙상블 예측자료 성능 평가", 대기, 27(3), pp.261~276 (2017), 한국기상학회[DOI]
  8. Adcroft A, "Simulations of underwater plumes of dissolved oil in the Gulf of Mexico", Geophys Res Lett, 37(18) (2010)[DOI]
  9. Best MJ, "The Joint UK Land Environment Simulator (JULES), model description-part 1: energy and water fluxes", Geosci Model Dev, 4(3), pp.677~699 (2011)[DOI]
  10. Blockley EW, "Validation of FOAM near-surface ocean current forecasts using Lagrangian drifting buoys", Ocean Sci, 8(4), pp.551~565 (2012)[DOI]
  11. Blockley EW, "Recent development of the Met Office operational ocean forecasting system: an overview and assessment of the new global FOAM forecasts", Geoscientific Model Dev, 7(6), pp.2613~2638 (2014)[DOI]
  12. Bonjean F, "Diagnostic model and analysis of the surface currents in the tropical Pacific Ocean", J Phys Oceanogr, 32(10), pp.2938~2954 (2002)[DOI]
  13. Bowler N, "The local ETKF and SKEB: upgrades to the MOGREPS short-range ensemble prediction system", Q J Roy Meteor Soc, 135(640), pp.767~776 (2009)[DOI]
  14. Davidson FJM, "Applications of GODAE ocean current forecasts to search and rescue and ship routing", Oceanography, 22(3), pp.176~181 (2009)[DOI]
  15. Huckerby J, "Operational oceanography in the 21st century", pp.695~720 (2011), Springer
  16. CICE: the Los Alamos sea ice model documentation and software user's manual version 4.1 LA-CC-06-012. T-3 fluid dynamics group. Los Alamos National Laboratory Technical Report 675, "Hunke EC", 76~ (2010)
  17. Kawamura H, "Preliminary numerical experiments on oceanic dispersion of <TEX>$^{131}I$<", TEX> and <TEX>$^{137}Cs$<, J Nucl Sci Technol, pp.2011, 48(11)[DOI]
  18. Lee T, "Evaluation of CMIP3 and CMIP5 wind stress climatology using satellite measurements and atmospheric reanalysis products", J Climate, 26, pp.5810~5826 (2013)[DOI]
  19. Lumpkin R, "Removing spurious low-frequency variability in drifter velocities", J Atmos Ocean Tech, 30(2), pp.353~360 (2013)[DOI]
  20. MacLachlan C, "Global Seasonal forecast system version 5 (GloSea5): a high-resolution seasonal forecast system", Q J Roy Meteor Soc, 141(689), pp.1072~1084 (2015)[DOI]
  21. Madec G, "NEMO ocean engine, Note du Pole de modelisation", pp.386~ (2008)
  22. Maltrud M, "On the possible long-term fate of oil released in the Deepwater Horizon incident, estimated using ensembles of dye release simulations", Environ Res Lett, 5(3), pp.035301~ (2010)[DOI]
  23. Mulholland DP, "Origin and impact of initialization shocks in coupled atmosphere-ocean forecasts", Mon Weather Rev, 143(11), pp.4631~4644 (2015)[DOI]
  24. Nakano M, "Long-term simulations of the <TEX>$^{137}Cs$<", TEX> dispersion from the Fukushima accident in the world ocean, 2012, pp.111, 109~115
  25. National Academies of Sciences, "Next generation Earth system prediction: strategies for subseasonal to seasonal forecasts", pp.350~ (2016), The National Academies Press
  26. 박상욱, "Comparison of Extended Medium-Range Forecast Skill Between KMA Ensemble, Ocean Coupled Ensemble, and GloSea5", Asia-Pacific Journal of Atmospheric Sciences, 53(3), pp.393~401 (2017), 한국기상학회
  27. Rossi V, "Multi-decadal projection of surface and interior pathways of the Fukushima Cesium-137 radioactive plume", Deep-Sea Res Part I, 80, pp.37~46 (2013)[DOI]
  28. Walters DN, "The Met Office unified model global atmosphere 3.0", 3.1 and JULES global land 3.0, Geosci Model Dev, pp.2011, 4(4)[DOI]
  29. Waters J, "Implementing a variational data assimilation system in an operational 1", 4 degree global ocean model, 2014, pp.141(687), 333~349
  30. Williams KD, "The Met Office global coupled model 2.0 (GC2) configuration", Geosci Model Dev, 8, pp.1509~1524 (2015)[DOI]
  31. Xu H, "Atmospheric effects of the Kuroshio large meander during 2004-05", J Climate, 23, pp.4704~4715 (2010)[DOI]

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