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

基于ROMS模式利用集合最优插值同化沿轨海表面高度异常数据的研究

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

基金项目: The Major State Basic Research Development Program of China under contract Nos 201-1CB403606 and 2011CB403500;the National Natural Science Foundation of China under contract Nos 41222038, 41076011and 41206023;the National Marine Environmental Forecasting Center Operational Development Foundation of the State Oceanic Administration of China under contract No. 2013002.

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- 收稿日期: 2013-02-28
- 修回日期: 2013-05-22

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

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

摘要

摘要: 本文基于ROMS海洋模式利用集合最优插值同化沿轨海表面高度异常数据。我们将该系统应用于基于ROMS建立的1/10分辨率的模式中进行同化实验。为了体现南海季节性变化特征，背景误差通过一个季节滑动的集合方案进行选取。同时我们选取了一个5阶的函数进行局地化并用250千米作为局地化半径。同化实验从2004年到2006年。结果显示，同化之后海表面高度异常的均方根差从10.57厘米降到6.70厘米，降低了36.6%。数据同化也降低了800米以上的温度以及200米以上的盐度，尽管在200米一下，盐度稍有变坏。同化之后海表面流场也与表面浮子的轨迹吻合更好。同时，同化之后海表面高度的变率与观测吻合很好。高变率与低变率的位置及强度吻合很好。另外，我们比较了考虑FGAT与不考虑FGAT的同化效果。对于温度及盐度来说，考虑FGAT要优于不考虑FGAT。最后，我们探究了高分辨率模式中对于南海北部模拟的海表面高度变率偏大的原因。结果显示该区域海表面高度变率偏大主要是由于过强的黑潮入侵引起的。因此为了更好的利用高分辨率模式模拟南海，同化沿轨海表面高度异常数据是必要的。
- 集合最优插值,沿轨海表面高度异常,南海,海表面高度变率
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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参考文献(22)

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文章访问数: 1957
HTML全文浏览量: 48
PDF下载量: 1151
被引次数: 0

基于ROMS模式利用集合最优插值同化沿轨海表面高度异常数据的研究

doi: 10.1007/s13131-014-0469-7

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出版历程

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

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出版历程

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