| 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 |
| |
| 作者姓名： | LYU Guokun WANG Hui ZHU Jiang WANG Dakui XIE Jiping LIU Guimei |
| |
| 作者单位： | 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;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;State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China;Key Laboratory of Research on Marine Hazards Forecasting, National Marine Environmental Forecasting Center, State Oceanic Administration, Beijing 100081, China;International Center for Climate and Environmental Sciences, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China;Key Laboratory of Research on Marine Hazards Forecasting, National Marine Environmental Forecasting Center, State Oceanic Administration, Beijing 100081, 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. |
| |
| 摘 要： | 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.
|
| 关 键 词： | 平均海平面 建模系统 资料同化 异常 集成优化 海洋 高分辨率模式 季节性变化 |
| 收稿时间： | 2013/2/28 0:00:00 |
| 修稿时间： | 3/4/2014 12:00:00 AM |
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.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. |
| |
| Authors: | LYU Guokun WANG Hui ZHU Jiang WANG Dakui XIE Jiping and LIU Guimei |
| |
| Institution: | 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;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;State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China;Key Laboratory of Research on Marine Hazards Forecasting, National Marine Environmental Forecasting Center, State Oceanic Administration, Beijing 100081, China;International Center for Climate and Environmental Sciences, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China;Key Laboratory of Research on Marine Hazards Forecasting, National Marine Environmental Forecasting Center, State Oceanic Administration, Beijing 100081, China |
| |
| 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. |
| |
| Keywords: | ensemble optimal interpolation regional ocean modeling system along-track sea level anomaly South China Sea variability |
| 本文献已被 CNKI 维普 SpringerLink 等数据库收录！ |
| 点击此处可从《海洋学报(英文版)》浏览原始摘要信息 |
| 点击此处可从《海洋学报(英文版)》下载免费的PDF全文 |
|
