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Volume 23 Issue 3

May  2006

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Article >  ADVANCES IN ATMOSPHERIC SCIENCES  > 2006 >  23(3): 449-460

Assimilated Tidal Results of Tide Gauge and TOPEX/POSEIDON Data over the China Seas Using a Variational Adjoint Approach with a Nonlinear Numerical Model

  • 1.

    National Marine Data and Information Service, State Oceanic Administration, Tianjin 300171, International Center for Climate and Environment Science, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029,National Marine Data and Information Service, State Oceanic Administration, Tianjin 300171, College of Physical and Environmental Oceanography, Ocean University of China, Qingdao 266003,National Marine Data and Information Service, State Oceanic Administration, Tianjin 300171, College of Physical and Environmental Oceanography, Ocean University of China, Qingdao 266003,National Marine Data and Information Service, State Oceanic Administration, Tianjin 300171,National Marine Data and Information Service, State Oceanic Administration, Tianjin 300171


doi: 10.1007/s00376-006-0449-8

  • In order to obtain an accurate tide description in the China Seas, the 2-dimensional nonlinear numerical Princeton Ocean Model (POM) is employed to incorporate in situ tidal measurements both from tide gauges and TOPEX/POSEIDON (T/P) derived datasets by means of the variational adjoint approach in such a way that unknown internal model parameters, bottom topography, friction coefficients and open boundary conditions, for example, are adjusted during the process. The numerical model is used as a forward model. After the along-track T/P data are processed, two classical methods, i.e. harmonic and response analysis, are implemented to estimate the tide from such datasets with a domain covering the model area extending from 0 to 41N in latitude and from 99E to 142E in longitude. And the results of these two methods are compared and interpreted. The numerical simulation is performed for 16 major constituents. In the data assimilation experiments, three types of unknown parameters (water depth, bottom friction and tidal open boundary conditions in the model equations) are chosen as control variables. Among the various types of data assimilation experiments, the calibration of water depth brings the most promising results. By comparing the results with selected tide gauge data, the average absolute errors are decreased from 7.9 cm to 6.8 cm for amplitude and from 13.0 to 9.0 for phase with respect to the semidiurnal tide M2 constituent, which is the largest tidal constituent in the model area. After the data assimilation experiment is performed, the comparison between model results and tide gauge observation for water levels shows that the RMS errors decrease by 9 cm for a total of 14 stations, mostly selected along the coast of Mainland China, when a one-month period is considered, and the correlation coefficients improve for most tidal stations among these stations.
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    [2] Yong-Hoon YOUN, Im Sang OH, Young-Hyang PARK, Ki-Hyun KIM, 2004: Climate Variabilities of Sea Level around the Korean Peninsula, ADVANCES IN ATMOSPHERIC SCIENCES, 21, 617-626.  doi: 10.1007/BF02915729
    [3] Xiangzhou SONG, Dexing WU, Xiaohui XIE, 2019: Tides and Turbulent Mixing in the North of Taiwan Island, ADVANCES IN ATMOSPHERIC SCIENCES, 36, 313-325.  doi: 10.1007/s00376-018-8098-2
    [4] HAN Guijun, LI Wei, ZHANG Xuefeng, WANG Xidong, WU Xinrong, FU Hongli, ZHANG Xiaoshuang, ZHANG Lianxin, and LI Dong, 2013: A new version of regional ocean reanalysis for coastal waters of China and adjacent seas, ADVANCES IN ATMOSPHERIC SCIENCES, 30, 974-982.  doi: 10.1007/s00376-012-2195-4
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    [6] WANG Bin, LIU Juanjuan, WANG Shudong, CHENG Wei, LIU Juan, LIU Chengsi, Qingnong XIAO, Ying-Hwa KUO, 2010: An Economical Approach to Four-dimensional Variational Data Assimilation, ADVANCES IN ATMOSPHERIC SCIENCES, 27, 715-727.  doi: 10.1007/s00376-009-9122-3
    [7] Cunde XIAO, Qi ZHANG, Jiao YANG, Zhiheng DU, Minghu DING, Tingfeng DOU, Binhe LUO, 2023: A Statistical Linkage between Extreme Cold Wave Events in Southern China and Sea Ice Extent in the Barents-Kara Seas from 1289 to 2017, ADVANCES IN ATMOSPHERIC SCIENCES, 40, 2154-2168.  doi: 10.1007/s00376-023-2227-2
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    • Manuscript History

    Manuscript received: 10 May 2006
    Manuscript revised: 10 May 2006
    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

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    Assimilated Tidal Results of Tide Gauge and TOPEX/POSEIDON Data over the China Seas Using a Variational Adjoint Approach with a Nonlinear Numerical Model

    • 1. National Marine Data and Information Service, State Oceanic Administration, Tianjin 300171, International Center for Climate and Environment Science, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029,National Marine Data and Information Service, State Oceanic Administration, Tianjin 300171, College of Physical and Environmental Oceanography, Ocean University of China, Qingdao 266003,National Marine Data and Information Service, State Oceanic Administration, Tianjin 300171, College of Physical and Environmental Oceanography, Ocean University of China, Qingdao 266003,National Marine Data and Information Service, State Oceanic Administration, Tianjin 300171,National Marine Data and Information Service, State Oceanic Administration, Tianjin 300171
    • Manuscript received: 2006-05-10
    • Manuscript revised: 2006-05-10

    Abstract: In order to obtain an accurate tide description in the China Seas, the 2-dimensional nonlinear numerical Princeton Ocean Model (POM) is employed to incorporate in situ tidal measurements both from tide gauges and TOPEX/POSEIDON (T/P) derived datasets by means of the variational adjoint approach in such a way that unknown internal model parameters, bottom topography, friction coefficients and open boundary conditions, for example, are adjusted during the process. The numerical model is used as a forward model. After the along-track T/P data are processed, two classical methods, i.e. harmonic and response analysis, are implemented to estimate the tide from such datasets with a domain covering the model area extending from 0 to 41N in latitude and from 99E to 142E in longitude. And the results of these two methods are compared and interpreted. The numerical simulation is performed for 16 major constituents. In the data assimilation experiments, three types of unknown parameters (water depth, bottom friction and tidal open boundary conditions in the model equations) are chosen as control variables. Among the various types of data assimilation experiments, the calibration of water depth brings the most promising results. By comparing the results with selected tide gauge data, the average absolute errors are decreased from 7.9 cm to 6.8 cm for amplitude and from 13.0 to 9.0 for phase with respect to the semidiurnal tide M2 constituent, which is the largest tidal constituent in the model area. After the data assimilation experiment is performed, the comparison between model results and tide gauge observation for water levels shows that the RMS errors decrease by 9 cm for a total of 14 stations, mostly selected along the coast of Mainland China, when a one-month period is considered, and the correlation coefficients improve for most tidal stations among these stations.

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