| 基于适应性观测误差和温盐偏差控制的EnKF温盐廓线资料同化试验 |
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| 引用本文： | 刘大年,施平,舒业强,姚景龙,王东晓,孙璐.基于适应性观测误差和温盐偏差控制的EnKF温盐廓线资料同化试验[J].海洋学报(英文版),2016,35(1):30-37. |
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| 作者姓名： | 刘大年 施平 舒业强 姚景龙 王东晓 孙璐 |
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| 作者单位： | 热带海洋环境国家重点实验室(中国科学院南海海洋研究所), 广东广州 510301,热带海洋环境国家重点实验室(中国科学院南海海洋研究所), 广东广州 510301,热带海洋环境国家重点实验室(中国科学院南海海洋研究所), 广东广州 510301,热带海洋环境国家重点实验室(中国科学院南海海洋研究所), 广东广州 510301,热带海洋环境国家重点实验室(中国科学院南海海洋研究所), 广东广州 510301,南海监测中心(国家海洋局南海分局), 广东 广州 510301 |
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| 摘 要： | 通过开展2008年夏季南海北部开放航次CTD的温盐廓线数据资料同化试验,本文采取了观测误差适应的方法来防止EnKF滤波发散问题;同时,从背景误差协方差和温盐模式偏差关系入手,在同化中引入温盐控制来减小模式偏差对同化结果的影响。对于改进的同化方案进行了试验验证,并用卫星高度计观测数据,OSCAR流速数据,走航ADCP数据作为独立观测数据检验。结果证明新的EnKF同化策略能够有效地减小温盐均方根误差。同时整个同化系统能有效地改善高度场和流场的模拟。
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| 关 键 词： | 集合卡曼滤波 适应性观测误差 温盐约束 |
| 收稿时间： | 2015/1/23 0:00:00 |
| 修稿时间： | 2015/5/11 0:00:00 |
Assimilating temperature and salinity profiles using Ensemble Kalman Filter with an adaptive observation error and T-S constraint |
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| LIU Danian,SHI Ping,SHU Yeqiang,YAO Jinglong,WANG Dongxiao and SUN Lu.Assimilating temperature and salinity profiles using Ensemble Kalman Filter with an adaptive observation error and T-S constraint[J].Acta Oceanologica Sinica,2016,35(1):30-37. |
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| Authors: | LIU Danian SHI Ping SHU Yeqiang YAO Jinglong WANG Dongxiao and SUN Lu |
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| Institution: | 1.State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou 510301, China2.South China Sea Monitoring Center, South China Sea Branch, State Oceanic Administration, Guangzhou 510301, China |
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| Abstract: | Temperature (T) and salinity (S) profiles from conductivity-temperature-depth data collected during the Northern South China Sea Open Cruise from August 16 to September 13, 2008 are assimilated using Ensemble Kalman Filter (EnKF). An adaptive observational error strategy is used to prevent filter from diverging. In the meantime, aiming at the limited improvement in some sites caused by the T and S biases in the model, a T-S constraint scheme is adopted to improve the assimilation performance, where T and S are separately updated at these locations. Validation is performed by comparing assimilated outputs with independent in situ data (satellite remote sensing sea level anomaly (SLA), the OSCAR velocity product and shipboard ADCP). The results show that the new EnKF assimilation scheme can significantly reduce the root mean square error (RMSE) of oceanic T and S compared with the control run and traditional EnKF. The system can also improve the simulation of circulations and SLA. |
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| Keywords: | Ensemble Kalman Filter adaptive observation error T-S constraint |
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