자료동화 기법에 따른 황·동중국해 지역 해양순환모델 결과 비교
Comparison of Data Assimilation Methods in a Regional Ocean Circulation Model for the Yellow and East China Seas
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초록
The present study aims to evaluate the effects of satellite-based SST (OSTIA) assimilation on a regional ocean circulation model for the Yellow and East China Seas (YECS), using three different assimilation methods: the Ensemble Optimal Interpolation (EnOI), Ensemble Kalman Filter (EnKF), and 4-Dimensional Variational (4DVAR) techniques, which are widely used in the ocean modeling communities. The model experiments show that an improved initial condition by assimilating the SST affects the seasonal water temperature and water mass distributions of the YECS. In particular, the SST data assimilation influences the temperature structures horizontally and vertically in winter, thereby improving the behavior of the YS warm current water. This is due to the fact that during wintertime the water column is well mixed, which is directly updated by the SST assimilation. The model comparisons indicate that the SST assimilation can improve the model performance in resolving the subsurface structures in wintertime, but has a relatively small impact in summertime due to the strong stratification. The differences among the different assimilation experiments are obvious when the SST was sharply changed due to a typhoon passage. Overall, the EnKF and 4DVAR show better agreement with the observations than the EnOI. The relatively low performance of EnOI under storm conditions may be related with a limitation of EnOI method whereby an analysis is obtained from a number of climatological fields, and thus the typhoon-induced SST changes in short-time scales may not be adequately reflected in the data assimilation.
Abstract
The present study aims to evaluate the effects of satellite-based SST (OSTIA) assimilation on a regional ocean circulation model for the Yellow and East China Seas (YECS), using three different assimilation methods: the Ensemble Optimal Interpolation (EnOI), Ensemble Kalman Filter (EnKF), and 4-Dimensional Variational (4DVAR) techniques, which are widely used in the ocean modeling communities. The model experiments show that an improved initial condition by assimilating the SST affects the seasonal water temperature and water mass distributions of the YECS. In particular, the SST data assimilation influences the temperature structures horizontally and vertically in winter, thereby improving the behavior of the YS warm current water. This is due to the fact that during wintertime the water column is well mixed, which is directly updated by the SST assimilation. The model comparisons indicate that the SST assimilation can improve the model performance in resolving the subsurface structures in wintertime, but has a relatively small impact in summertime due to the strong stratification. The differences among the different assimilation experiments are obvious when the SST was sharply changed due to a typhoon passage. Overall, the EnKF and 4DVAR show better agreement with the observations than the EnOI. The relatively low performance of EnOI under storm conditions may be related with a limitation of EnOI method whereby an analysis is obtained from a number of climatological fields, and thus the typhoon-induced SST changes in short-time scales may not be adequately reflected in the data assimilation.
참고문헌 (43)
- 김영호, "Double Gyre 모형 해양에서 앙상블 칼만필터를 이용한 자료동화와 쌍둥이 실험들을 통한 민감도 시험", Ocean and Polar Research, 30(2), pp.129~140 (2008), 한국해양과학기술원
- 김지혜, "지역 해양순환예측시스템에 대한 OSTIA 해수면온도 자료동화효과에 관한 연구", 바다, 20(1), pp.1~15 (2015), 한국해양학회
- 백유현, "한반도 연안 위성합성 및 수치모델 재분석 해수면온도 자료의 정확도", Ocean and Polar Research, 41(4), pp.213~232 (2019), 한국해양과학기술원[DOI]
- 배효식, "대기질 예보를 위한 최적내삽 자료동화의 고속화", 박사, pp.2017
- Bennett AF, "Inverse modeling of the ocean and atmosphere", pp.260~ (2005), Cambridge University Press
- Blumberg AF, "Modeling vertical structure of open-channel flows", J Hydraul Eng, 118, pp.1119~1134 (1992)
- Broquet G, "Application of 4D-variational data assimilation to the California current system", Dynam Atmos Oceans, 48, pp.69~91 (2009)
- Buehner M, "Intercomparison of variational data assimilation and the ensemble Kalman filter for global deterministic NWP. Part II: One-month experiments with real observations", Mon Wea Rev, 138(5), pp.1567~1586 (2010)
- Burchard H, "On the q2l equation by Mellor and Yamada (1982). Notes and correspondence", J Phys Oceanogr, 31, pp.1377~1387 (2001)
- Courtier P, "A strategy for operational implementation of 4D-var using an incremental approach", Q J Roy Meteor Soc, 120, pp.1367~1388 (1994)
- Di Lorenzo E, "Weak and strong constraint data assimilation in the inverse Regional Ocean Modeling System(ROMS) : Development and application for a baroclinic coastal upwelling system", Ocean Model, 16, pp.160~187 (2007)
- Donlon CJ, "The operational sea surface temperature and sea ice analysis(OSTIA)system", Remote Sens Environ, 116, pp.140~158 (2012)
- Edwards CA, "Regional ocean data assimilation", Ann Rev Mar Sci, 7, pp.21~42 (2015)
- Egbert GD, "Efficient inverse modeling of barotropic ocean tides", J Atmo Ocean Technol, 19, pp.183~204 (2002)
- Evensen G, "The ensemble Kalman filter : Theoretical formulation and practical implementation", Ocean Dyn, 53, pp.343~367 (2003)
- Fairall CW, "Bulk parameterization of air-sea fluxes in TOGA COARE", J Geophys Res, 101, pp.3747~3767 (1996)
- Fisher M, "Minimization algorithms for variational data assimilation", Proceedings of the ECMWF Seminar on Recent Developments in Numerical Methods for Atmospheric Modelling, pp.364~385 (1998)
- Gentemann CL, "Near real t ime global optimum interpolated microwave SSTs : Applications to hurricane intensity forecasting", Proceedings of the 27th Conference on Hurricanes and Tropical Meteorology (2006)
- Ghil M, "Data assimilation in meteorology and oceanography", Adv Geophys, 33, pp.141~266 (1991)
- Gopalakrishnan G, "Comparison of 4DVAR and EnKF state estimates and forecasts in the Gulf of Mexico", Q J R Meteorol Soc, 145, pp.1354~1376 (2019)
- Hoteit I, "New frontiers in operational oceanography, ch. 17: Data assimilation in oceanography: Current status and new directions", GODAE Ocean View, pp.465~512 (2018)
- Hunt BR, "Efficient data assimilation for spatiotemporal chaos : A local ensemble transform Kalman filter", Physica D, 230, pp.112~126 (2007)
- Janekovic I, "4D-var data assimilation in a nested, coastal ocean model : A Hawaiian case study", J Geophys Res, 118(10), pp.5022~5035 (2013)
- Ji X, "Assimilating OSTIA SST into regional modeling systems for the Yellow Sea using ensemble methods", Acta Oceanol Sin, 36, pp.37~51 (2017)
- Kalnay E, "Atmospheric modeling, data assimilation and predictability", pp.368~ (2003), Cambridge University Press
- Kyungman Kwon, "Assessment and Improvement of Global Gridded Sea Surface Temperature Datasets in the Yellow Sea Using In Situ Ocean Buoy and Research Vessel Observations", Remote Sensing, 12(5), pp.759~ (2020), MDPI AG[DOI]
- Kwon KM, "Effect of model error representation in the Yellow and East China Sea modeling system based on the ensemble Kalman filter", Ocean Dynam, 66, pp.263~283 (2016)
- 이준호, "4DVAR Data Assimilation with the Regional Ocean Modeling System (ROMS): Impact on the Water Mass Distributions in the Yellow Sea", Ocean Science Journal, 53(2), pp.165~178 (2018), 한국해양과학기술원
- Lee JH, "Contribution of the Yellow Sea bottom cold water to the abnormal cooling of sea surface temperature in the summer of 2011", J Geophys Res-Oceans, 121, pp.3777~3789 (2016)
- Li Y, "Variational data assimilative modeling of the Gulf of Maine in spring and summer 2010", J Geophys Res-Oceans, 120(5), pp.3522~3541 (2015)
- Lyu G, "Assimilating the along-track sea level anomaly into the regional ocean modeling system using the ensemble optimal interpolation", Acta Oceanol Sin, 33(7), pp.72~82 (2014)
- Mellor GL, "Development of a turbulence closure model for geophysical fluid problems", Rev Geophys, 20, pp.851~875 (1982)
- Miyoshi T, "Ensemble Kalman Filter and 4D-var intercomparison with the Japanese operational global analysis and prediction system", Mon Wea Rev, 138(7), pp.2846~2866 (2010)
- Moore AM, "The Regional Ocean Modeling System (ROMS) 4-dimensional variational data assimilation systems. I: System overview and formulation", Prog Oceanogr, 91, pp.34~49 (2011)
- Moore AM, "The Regional Ocean Modeling System(ROMS)4-dimensional variational data assimilation systems : Part III – Observation impact and observation sensitivity in the California Current System", Prog Oceanogr, 91, pp.74~94 (2011)
- Ngodock H, "A 4DVAR system for the navy coastal ocean model. Part I: System description and assimilation of synthetic observations in Monterey Bay", Mon Wea Rev, 142, pp.2085~2107 (2014)
- Powell BS, "4DVAR data assimilation in the Intra-Americas Sea with the Regional Ocean Modeling System(ROMS)", Ocean Model, 25, pp.173~188 (2008)
- Shchepetkin AF, "The regional oceanic modeling system(ROMS) : A split-explicit, free-surface, topography-following-coordinate oceanic model", Ocean Model, 9, pp.347~404 (2005)
- Shen H, "Health of the Yellow Sea", pp.129~148 (1998), The Earth Love Publisher
- 서승남, "한국 주변해역 30초 격자수심 - KorBathy30s", 한국해안·해양공학회논문집, 20(1), pp.110~120 (2008), 한국해안,해양공학회
- Warner JC, "Performance of four turbulence closure models implemented using a generic length scale method", Ocean Model, 8, pp.81~113 (2005)
- Wunsch C, "The ocean circulation inverse problem", pp.442~ (1996), Cambridge University Press
- Yang SC, "Comparison of local ensemble transform Kalman filter, 3DVAR, and 4DVAR in a quasigeostrophic model", Mon Wea Rev, 137(2), pp.693~709 (2009)