| 集合同化方法在黄海海域海表面温度同化中的应用 |
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| 引用本文： | 季轩梁,权径万,崔炳周,刘桂梅,朴光淳,王辉,卞道成,李云,纪棋严,朱学明.集合同化方法在黄海海域海表面温度同化中的应用[J].海洋学报(英文版),2017,36(3):37-51. |
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| 作者姓名： | 季轩梁 权径万 崔炳周 刘桂梅 朴光淳 王辉 卞道成 李云 纪棋严 朱学明 |
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| 作者单位： | 国家海洋环境预报中心, 北京, 100081, 中国;国家海洋局海洋灾害预报技术研究重点实验室, 北京, 100081, 中国,群山国立大学海洋室, 群山, 54150, 韩国,群山国立大学海洋室, 群山, 54150, 韩国;全南国立大学海洋室, 广州, 61186, 韩国,国家海洋环境预报中心, 北京, 100081, 中国;国家海洋局海洋灾害预报技术研究重点实验室, 北京, 100081, 中国,韩国海洋科学技术研究所, 安山, 15627, 韩国,国家海洋环境预报中心, 北京, 100081, 中国;国家海洋局海洋灾害预报技术研究重点实验室, 北京, 100081, 中国,韩国水文和海洋机构, 釜山, 49111, 韩国,国家海洋环境预报中心, 北京, 100081, 中国;国家海洋局海洋灾害预报技术研究重点实验室, 北京, 100081, 中国,海洋声学与遥感应用科学技术实验室, 浙江海洋大学, 舟山, 316000, 中国,国家海洋环境预报中心, 北京, 100081, 中国;国家海洋局海洋灾害预报技术研究重点实验室, 北京, 100081, 中国 |
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| 基金项目： | The National Key Research and Development Program of China under contract Nos 2016YFC1401800 and 2016YFC1401605; the Cooperation on the Development of Basic Technologies for the Yellow Sea and East China Sea Operational Oceanographic System (YOOS); the project of Development of Korea Operational Oceanographic System (KOOS), Phase 2 funded by the Ministry of Oceans and Fisheries; the National Natural Science Foundation of China under contract Nos 41076011, 41206023 and 41222038; the National Basic Research Program (973 Program) of China under contract No. 2011CB403606; the Public Science and Technology Research Funds Project of Ocean under contract No. 201205018; the Strategic Priority Research Program of the Chinese Academy of Sciences under contract No. XDA1102010403; Producing map of ocean currents for the neighboring seas of Korea funded by the Ministry of Oceans and Fisheries under contract No. 2033-307-210-13. |
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| 摘 要： | The effects of sea surface temperature(SST) data assimilation in two regional ocean modeling systems were examined for the Yellow Sea(YS). The SST data from the Operational Sea Surface Temperature and Sea Ice Analysis(OSTIA) were assimilated. The National Marine Environmental Forecasting Center(NMEFC) modeling system uses the ensemble optimal interpolation method for ocean data assimilation and the Kunsan National University(KNU) modeling system uses the ensemble Kalman filter. Without data assimilation, the NMEFC modeling system was better in simulating the subsurface temperature while the KNU modeling system was better in simulating SST. The disparity between both modeling systems might be related to differences in calculating the surface heat flux, horizontal grid spacing, and atmospheric forcing data. The data assimilation reduced the root mean square error(RMSE) of the SST from 1.78°C(1.46°C) to 1.30°C(1.21°C) for the NMEFC(KNU) modeling system when the simulated temperature was compared to Optimum Interpolation Sea Surface Temperature(OISST) SST dataset. A comparison with the buoy SST data indicated a 41%(31%) decrease in the SST error for the NMEFC(KNU) modeling system by the data assimilation. In both data assimilative systems, the RMSE of the temperature was less than 1.5°C in the upper 20 m and approximately 3.1°C in the lower layer in October. In contrast, it was less than 1.0°C throughout the water column in February. This study suggests that assimilations of the observed temperature profiles are necessary in order to correct the lower layer temperature during the stratified season and an ocean modeling system with small grid spacing and optimal data assimilation method is preferable to ensure accurate predictions of the coastal ocean in the YS.
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| 关 键 词： | 集合最优插值 集合卡曼滤波 海表面温度 黄海 同化 |
| 收稿时间： | 2015/10/13 0:00:00 |
| 修稿时间： | 2016/3/3 0:00:00 |
Assimilating OSTIA SST into regional modeling systems for the Yellow Sea using ensemble methods |
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| JI Xuanliang,KWON Kyung Man,CHOI Byoung-Ju,LIU Guimei,PARK Kwang-Soon,WANG Hui,BYUN Do-Seong,LI Yun,JI Qiyan and ZHU Xueming.Assimilating OSTIA SST into regional modeling systems for the Yellow Sea using ensemble methods[J].Acta Oceanologica Sinica,2017,36(3):37-51. |
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| Authors: | JI Xuanliang KWON Kyung Man CHOI Byoung-Ju LIU Guimei PARK Kwang-Soon WANG Hui BYUN Do-Seong LI Yun JI Qiyan and ZHU Xueming |
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| Institution: | National Marine Environmental Forecasting Center, State Oceanic Adminstration, Beijing 100081, China;Key Laboratory of Research on Marine Hazards Forecasting, National Marine Environmental Forecasting Center, State Oceanic Adminstration, Beijing 100081, China,Department of Oceanography, Kunsan National University, Gunsan 54150, Republic of Korea,Department of Oceanography, Kunsan National University, Gunsan 54150, Republic of Korea;Department of Oceanography, Chonnam National University, Gwangju 61186, Republic of Korea,National Marine Environmental Forecasting Center, State Oceanic Adminstration, Beijing 100081, China;Key Laboratory of Research on Marine Hazards Forecasting, National Marine Environmental Forecasting Center, State Oceanic Adminstration, Beijing 100081, China,Korea Institute of Ocean Science and Technology, Ansan 15627, Republic of Korea,National Marine Environmental Forecasting Center, State Oceanic Adminstration, Beijing 100081, China;Key Laboratory of Research on Marine Hazards Forecasting, National Marine Environmental Forecasting Center, State Oceanic Adminstration, Beijing 100081, China,Korea Hydrographic and Oceanographic Agency, Busan 49111, Republic of Korea,National Marine Environmental Forecasting Center, State Oceanic Adminstration, Beijing 100081, China;Key Laboratory of Research on Marine Hazards Forecasting, National Marine Environmental Forecasting Center, State Oceanic Adminstration, Beijing 100081, China,Marine Acoustics and Remote Sensing Laboratory, Zhejiang Ocean University, Zhoushan 316000, China and National Marine Environmental Forecasting Center, State Oceanic Adminstration, Beijing 100081, China;Key Laboratory of Research on Marine Hazards Forecasting, National Marine Environmental Forecasting Center, State Oceanic Adminstration, Beijing 100081, China |
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| Abstract: | The effects of sea surface temperature (SST) data assimilation in two regional ocean modeling systems were examined for the Yellow Sea (YS). The SST data from the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) were assimilated. The National Marine Environmental Forecasting Center (NMEFC) modeling system uses the ensemble optimal interpolation method for ocean data assimilation and the Kunsan National University (KNU) modeling system uses the ensemble Kalman filter. Without data assimilation, the NMEFC modeling system was better in simulating the subsurface temperature while the KNU modeling system was better in simulating SST. The disparity between both modeling systems might be related to differences in calculating the surface heat flux, horizontal grid spacing, and atmospheric forcing data. The data assimilation reduced the root mean square error (RMSE) of the SST from 1.78°C (1.46°C) to 1.30°C (1.21°C) for the NMEFC (KNU) modeling system when the simulated temperature was compared to Optimum Interpolation Sea Surface Temperature (OISST) SST dataset. A comparison with the buoy SST data indicated a 41% (31%) decrease in the SST error for the NMEFC (KNU) modeling system by the data assimilation. In both data assimilative systems, the RMSE of the temperature was less than 1.5°C in the upper 20 m and approximately 3.1°C in the lower layer in October. In contrast, it was less than 1.0°C throughout the water column in February. This study suggests that assimilations of the observed temperature profiles are necessary in order to correct the lower layer temperature during the stratified season and an ocean modeling system with small grid spacing and optimal data assimilation method is preferable to ensure accurate predictions of the coastal ocean in the YS. |
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| Keywords: | ensemble optimal interpolation ensemble Kalman filter SST Yellow Sea assimilation |
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