Assimilating operational SST and sea ice analysis data into an operational circulation model for the coastal seas of China

JI Qiyan; ZHU Xueming; WANG Hui; LIU Guimei; GAO Shan; JI Xuanliang; XU Qing

doi:10.1007/s13131-015-0691-y

OSTIA数据在中国近海业务化环流模型中的同化应用

doi: 10.1007/s13131-015-0691-y

1.
河海大学海岸灾害及防护教育部重点实验室, 南京, 210098
2.
国家海洋局海洋灾害预报技术研究重点实验室, 国家海洋环境预报中心, 北京, 100081
3.
河海大学海岸灾害及防护教育部重点实验室, 南京, 210098;国家海洋局海洋灾害预报技术研究重点实验室, 国家海洋环境预报中心, 北京, 100081

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- 收稿日期: 2015-01-23
- 修回日期: 2015-02-28

Assimilating operational SST and sea ice analysis data into an operational circulation model for the coastal seas of China

1.
Key Laboratory of Ministry of Education for Coastal Disaster and Defence, Hohai University, Nanjing 210098, China
2.
Key Laboratory of Research on Marine Hazards Forecasting, National Marine Environmental Forecasting Center, State Oceanic Administration, Beijing 100081, China
3.
Key Laboratory of Ministry of Education for Coastal Disaster and Defence, Hohai University, Nanjing 210098, China;Key Laboratory of Research on Marine Hazards Forecasting, National Marine Environmental Forecasting Center, State Oceanic Administration, Beijing 100081, China

摘要

摘要: 海表面温度(SST)的预报是业务化海洋环流模型的一个基本业务。海表面热通量、初始温度场、边界条件直接影响SST的模拟精度。两种快速且方便的方法被用来改进渤海、黄海、东海的SST模拟:一个基于海表面热通量的校正(QC), 将通量的校正量松弛逼近到模型方程中;另一个是采用集合最优插值(EnOI)法优化初始场。基于以上两种方法, 将从业务化海表面温度和海冰分析(OSTIA)系统获得的高分辨率SST数据同化到中国近海业务化环流模型中, 以2011年为例开展四个数值模拟试验。将模拟结果与OSTIA的SST数据进行对比分析, 结果表明:四个试验的区域平均均方根误差分别为1.74、1.16、1.30和0.91℃, 同化试验2、3、4的改进效果分别为33.3%、25.3% 和 47.7%;两种方法都能使模拟的SST得到改进, 其中EnOI法比QC法效果更为明显, 而将两种方法结合使用效果最佳。与近岸浮标站点的观测数据对比, 表明同化高分辨率的卫星SST产品也可以有效地提高在近岸区域的SST预报能力。
- 海表面温度 /
- 数据同化 /
- 集合最优插值 /
- 快速校正 /
- 渤海、黄海、东海
Abstract: The prediction of sea surface temperature (SST) is an essential task for an operational ocean circulation model. A sea surface heat flux, an initial temperature field, and boundary conditions directly affect the accuracy of a SST simulation. Here two quick and convenient data assimilation methods are employed to improve the SST simulation in the domain of the Bohai Sea, the Yellow Sea and the East China Sea (BYECS). One is based on a surface net heat flux correction, named as Qcorrection (QC), which nudges the flux correction to the model equation; the other is ensemble optimal interpolation (EnOI), which optimizes the model initial field. Based on such two methods, the SST data obtained from the operational SST and sea ice analysis (OSTIA) system are assimilated into an operational circulation model for the coastal seas of China. The results of the simulated SST based on four experiments, in 2011, have been analyzed. By comparing with the OSTIA SST, the domain averaged root mean square error (RMSE) of the four experiments is 1.74, 1.16, 1.30 and 0.91℃, respectively; the improvements of assimilation experiments Exps 2, 3 and 4 are about 33.3%, 25.3%, and 47.7%, respectively. Although both two methods are effective in assimilating the SST, the EnOI shows more advantages than the QC, and the best result is achieved when the two methods are combined. Comparing with the observational data from coastal buoy stations, show that assimilating the high-resolution satellite SST products can effectively improve the SST prediction skill in coastal regions.
- sea surface temperature /
- data assimilation /
- ensemble optimal interpolation /
- quick correction /
- Bohai Sea

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文章访问数: 1620
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OSTIA数据在中国近海业务化环流模型中的同化应用

doi: 10.1007/s13131-015-0691-y

计量

出版历程

Assimilating operational SST and sea ice analysis data into an operational circulation model for the coastal seas of China

计量

出版历程

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