Assimilating the along-track sea level anomaly into the regional ocean modeling system using the ensemble optimal interpolation

LYU Guokun; WANG Hui; ZHU Jiang; WANG Dakui; XIE Jiping; LIU Guimei

doi:10.1007/s13131-014-0469-7

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Article Navigation > Acta Oceanologica Sinica > 2014 > 33(7): 72-82

LYU Guokun, WANG Hui, ZHU Jiang, WANG Dakui, XIE Jiping, LIU Guimei. Assimilating the along-track sea level anomaly into the regional ocean modeling system using the ensemble optimal interpolation[J]. Acta Oceanologica Sinica, 2014, 33(7): 72-82. doi: 10.1007/s13131-014-0469-7

Citation:

LYU Guokun, WANG Hui, ZHU Jiang, WANG Dakui, XIE Jiping, LIU Guimei. Assimilating the along-track sea level anomaly into the regional ocean modeling system using the ensemble optimal interpolation[J]. Acta Oceanologica Sinica, 2014, 33(7): 72-82. doi: 10.1007/s13131-014-0469-7

Citation:

LYU Guokun, WANG Hui, ZHU Jiang, WANG Dakui, XIE Jiping, LIU Guimei. Assimilating the along-track sea level anomaly into the regional ocean modeling system using the ensemble optimal interpolation[J]. Acta Oceanologica Sinica, 2014, 33(7): 72-82. doi: 10.1007/s13131-014-0469-7

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Assimilating the along-track sea level anomaly into the regional ocean modeling system using the ensemble optimal interpolation

doi: 10.1007/s13131-014-0469-7

1.
Physical Oceanography Laboratory, Ocean University of China, Qingdao 266100, China;Key Laboratory of Research on Marine Hazards Forecasting, National Marine Environmental Forecasting Center, State Oceanic Administration, Beijing 100081, China
2.
College of Environmental Science and Engineering, Ocean University of China, Qingdao 266100, China;Key Laboratory of Research on Marine Hazards Forecasting, National Marine Environmental Forecasting Center, State Oceanic Administration, Beijing 100081, China
3.
State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
4.
Key Laboratory of Research on Marine Hazards Forecasting, National Marine Environmental Forecasting Center, State Oceanic Administration, Beijing 100081, China
5.
International Center for Climate and Environmental Sciences, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China

Received Date: 2013-02-28
Rev Recd Date: 2013-05-22

Abstract

Abstract

The ensemble optimal interpolation (EnOI) is applied to the regional ocean modeling system (ROMS) with the ability to assimilate the along-track sea level anomaly (TSLA). This system is tested with an eddy-resolving system of the South China Sea (SCS). Background errors are derived from a running seasonal ensemble to account for the seasonal variability within the SCS. A fifth-order localization function with a 250 km localization radius is chosen to reduce the negative effects of sampling errors. The data assimilation system is tested from January 2004 to December 2006. The results show that the root mean square deviation (RMSD) of the sea level anomaly decreased from 10.57 to 6.70 cm, which represents a 36.6% reduction of error. The data assimilation reduces error for temperature within the upper 800 m and for salinity within the upper 200 m, although error degrades slightly at deeper depths. Surface currents are in better agreement with trajectories of surface drifters after data assimilation. The variance of sea level improves significantly in terms of both the amplitude and position of the strong and weak variance regions after assimilating TSLA. Results with AGE error (AGE) perform better than no AGE error (NoAGE) when considering the improvements of the temperature and the salinity. Furthermore, reasons for the extremely strong variability in the northern SCS in high resolution models are investigated. The results demonstrate that the strong variability of sea level in the high resolution model is caused by an extremely strong Kuroshio intrusion. Therefore, it is demonstrated that it is necessary to assimilate the TSLA in order to better simulate the SCS with high resolution models.
- ensemble optimal interpolation

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References(22)

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