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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
引用本文:纪棋严,朱学明,王辉,刘桂梅,高姗,季轩梁,徐青.OSTIA数据在中国近海业务化环流模型中的同化应用[J].海洋学报(英文版),2015,34(7):54-64.
作者姓名:纪棋严  朱学明  王辉  刘桂梅  高姗  季轩梁  徐青
作者单位: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,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,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,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,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,河海大学海岸灾害及防护教育部重点实验室, 南京, 210098
摘    要: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(En OI), 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°C, 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 En OI 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.

关 键 词: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
收稿时间:2015/1/23 0:00:00
修稿时间:2015/2/28 0:00:00

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 and XU Qing.Assimilating operational SST and sea ice analysis data into an operational circulation model for the coastal seas of China[J].Acta Oceanologica Sinica,2015,34(7):54-64.
Authors:JI Qiyan  ZHU Xueming  WANG Hui  LIU Guimei  GAO Shan  JI Xuanliang and XU Qing
Affiliation: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,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,Key Laboratory of Research on Marine Hazards Forecasting, National Marine Environmental Forecasting Center, State Oceanic Administration, Beijing 100081, China,Key Laboratory of Research on Marine Hazards Forecasting, National Marine Environmental Forecasting Center, State Oceanic Administration, Beijing 100081, China,Key Laboratory of Research on Marine Hazards Forecasting, National Marine Environmental Forecasting Center, State Oceanic Administration, Beijing 100081, China and Key Laboratory of Ministry of Education for Coastal Disaster and Defence, Hohai University, Nanjing 210098, China
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.
Keywords:sea surface temperature  data assimilation  ensemble optimal interpolation  quick correction  Bohai Sea  Yellow Sea  East China Sea
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