Advanced Search
Article Contents

Assimilation of Satellite Altimetry into a Western North Pacific Operational Model

  • An ocean data assimilation system, COMPASS-K (the Comprehensive Ocean Modeling, Prediction, Analysis and Synthesis System in the Kuroshio-region), has been developed at the Meteorological Research Institute (MRI). The purposes of the development are understanding ocean variability in the Kuroshio re gion as a local response to a global climate change with assimilated four-dimensional data sets, develop ment of an operational system in the Japan Meteorological Agency, and for the GODAE (Global Ocean Data Assimilation Experiment) project. The model is an eddy permitting version of an MRI-OGCM. Space-time decorrelation scales of ocean variability are estimated with TOPEX/POSEIDON (T/P) altimeter data. Subsurface temperature and salinity fields are projected from the T / P altimeter data with a statistical correlation method and are assim ilated into the model with a time-retrospective nudging scheme. Seasonal variation in the western North Pacific is investigated. Realistic space-time distribution of the physical quantities, the path of Kuroshio and its separation from Honshu are captured well. The Kuroshio volume transport is well reproduced in a reanalysis experiment of 1993. Preliminary predictability experi ments are done in February and March, 1994. Predictability diagram shows the time scale of the predictability for temperature field is about 17 days in the Kuroshio south of Japan. This time scale is smal ler than that in the North Atlantic.
  • [1] Se-Hwan YANG, LI Chaofan, and LU Riyu, 2014: Predictability of Winter Rainfall in South China as Demonstrated by the Coupled Models of ENSEMBLES, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 779-786.  doi: 10.1007/s00376-013-3172-2
    [2] Zhiyong MENG, Eugene E. CLOTHIAUX, 2022: Contributions of Fuqing ZHANG to Predictability, Data Assimilation, and Dynamics of High Impact Weather: A Tribute, ADVANCES IN ATMOSPHERIC SCIENCES, 39, 676-683.  doi: 10.1007/s00376-021-1362-x
    [3] BEI Naifang, Fuqing ZHANG, 2014: Mesoscale Predictability of Moist Baroclinic Waves: Variable and Scale-dependent Error Growth, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 995-1008.  doi: 10.1007/s00376-014-3191-7
    [4] WANG Qiang, MU Mu, Henk A. DIJKSTRA, 2012: Application of the Conditional Nonlinear Optimal Perturbation Method to the Predictability Study of the Kuroshio Large Meander, ADVANCES IN ATMOSPHERIC SCIENCES, 29, 118-134.  doi: 10.1007/s00376-011-0199-0
    [5] Mingkui LI, Shaoqing ZHANG, Lixin WU, Xiaopei LIN, Ping CHANG, Gohkan DANABASOGLU, Zhiqiang WEI, Xiaolin YU, Huiqin HU, Xiaohui MA, Weiwei MA, Haoran ZHAO, Dongning JIA, Xin LIU, Kai MAO, Youwei MA, Yingjing JIANG, Xue WANG, Guangliang LIU, Yuhu CHEN, 2020: An Examination of the Predictability of Tropical Cyclone Genesis in High-Resolution Coupled Models with Dynamically Downscaled Coupled Data Assimilation Initialization, ADVANCES IN ATMOSPHERIC SCIENCES, 37, 939-950.  doi: 10.1007/s00376-020-9220-9
    [6] LIU Qinyu, WEN Na, YU Yongqiang, 2006: The Role of the Kuroshio in the Winter North Pacific Ocean-Atmosphere Interaction: Comparison of a Coupled Model and Observations, ADVANCES IN ATMOSPHERIC SCIENCES, 23, 181-189.  doi: 10.1007/s00376-006-0181-4
    [7] li liu, Xueen Chen, 2024: A Spatial-dependent Nudging Method and Its Application to Global Tide Assimilation, ADVANCES IN ATMOSPHERIC SCIENCES.  doi: 10.1007/s00376-024-4062-5
    [8] WANG Huijun, FAN Ke, SUN Jianqi, LI Shuanglin, LIN Zhaohui, ZHOU Guangqing, CHEN Lijuan, LANG Xianmei, LI Fang, ZHU Yali, CHEN Hong, ZHENG Fei, 2015: A Review of Seasonal Climate Prediction Research in China, ADVANCES IN ATMOSPHERIC SCIENCES, 32, 149-168.  doi: 10.1007/s00376-014-0016-7
    [9] Xia LIU, Qiang WANG, Mu MU, 2018: Optimal Initial Error Growth in the Prediction of the Kuroshio Large Meander Based on a High-resolution Regional Ocean Model, ADVANCES IN ATMOSPHERIC SCIENCES, 35, 1362-1371.  doi: 10.1007/s00376-018-8003-z
    [10] Yunyun LIU, Zeng-Zhen HU, Renguang WU, Xing YUAN, 2022: Causes and Predictability of the 2021 Spring Southwestern China Severe Drought, ADVANCES IN ATMOSPHERIC SCIENCES, 39, 1766-1776.  doi: 10.1007/s00376-022-1428-4
    [11] Feifan ZHOU, Wansuo DUAN, He ZHANG, Munehiko YAMAGUCHI, 2018: Possible Sources of Forecast Errors Generated by the Global/Regional Assimilation and Prediction System for Landfalling Tropical Cyclones. Part II: Model Uncertainty, ADVANCES IN ATMOSPHERIC SCIENCES, 35, 1277-1290.  doi: 10.1007/s00376-018-7095-9
    [12] Niels BORMANN, David DUNCAN, Stephen ENGLISH, Sean HEALY, Katrin LONITZ, Keyi CHEN, Heather LAWRENCE, Qifeng LU, 2021: Growing Operational Use of FY-3 Data in the ECMWF System, ADVANCES IN ATMOSPHERIC SCIENCES, 38, 1285-1298.  doi: 10.1007/s00376-020-0207-3
    [13] Mu Mu, Duan Wansuo, Wang Jiacheng, 2002: The Predictability Problems in Numerical Weather and Climate Prediction, ADVANCES IN ATMOSPHERIC SCIENCES, 19, 191-204.  doi: 10.1007/s00376-002-0016-x
    [14] Wansuo DUAN, Lichao YANG, Mu MU, Bin WANG, Xueshun SHEN, Zhiyong MENG, Ruiqiang DING, 2023: Recent Advances in China on the Predictability of Weather and Climate, ADVANCES IN ATMOSPHERIC SCIENCES, 40, 1521-1547.  doi: 10.1007/s00376-023-2334-0
    [15] DUAN Wansuo, JIANG Zhina, XU Hui, 2007: Progress in Predictability Studies in China (2003--2006), ADVANCES IN ATMOSPHERIC SCIENCES, 24, 1086-1098.  doi: 10.1007/s00376-007-1086-6
    [16] WANG Weiwen, WANG Dongxiao, ZHOU Wen, LIU Qinyan, YU Yongqiang, LI Chao, 2011: Impact of the South China Sea Throughflow on the Pacific Low-Latitude Western Boundary Current: A Numerical Study for Seasonal and Interannual Time Scales, ADVANCES IN ATMOSPHERIC SCIENCES, 28, 1367-1376.  doi: 10.1007/s00376-011-0142-4
    [17] ZHENG Qin, DAI Yi, ZHANG Lu, SHA Jianxin, LU Xiaoqing, 2012: On the Application of a Genetic Algorithm to the Predictability Problems Involving ``On--Off'' Switches, ADVANCES IN ATMOSPHERIC SCIENCES, 29, 422-434.  doi: 10.1007/s00376-011-1054-z
    [18] WU Duochang, MENG Zhiyong, YAN Dachun, 2013: The Predictability of a Squall Line in South China on 23 April 2007, ADVANCES IN ATMOSPHERIC SCIENCES, 30, 485-502.  doi: 10.1007/s00376-012-2076-x
    [19] ZHU Benlu, LIN Wantao, ZHANG Yun, 2010: Analysis Study on Perturbation Energy and Predictability of Heavy Precipitation in South China, ADVANCES IN ATMOSPHERIC SCIENCES, 27, 382-392.  doi: 10.1007/s00376-009-8164-x
    [20] Se-Hwan YANG, LU Riyu, 2014: Predictability of the East Asian Winter Monsoon Indices by the Coupled Models of ENSEMBLES, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 1279-1292.  doi: 10.1007/s00376-014-4020-8

Get Citation+

Export:  

Share Article

Manuscript History

Manuscript received: 10 September 2001
Manuscript revised: 10 September 2001
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

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Assimilation of Satellite Altimetry into a Western North Pacific Operational Model

  • 1. Meteorological Research Institute, 1-1 Nagamine, Tsukuba 305-0052 Japan,Meteorological Research Institute, 1-1 Nagamine, Tsukuba 305-0052 Japan,Japan Meteorological Agency, 1-3-4 Otemachi, Tokyo 100-8122 Japan,Institute of Atmospheric Physics, Chinese Academy of Science, Beijing 100029,Magritte SNC, Milano, Italy

Abstract: An ocean data assimilation system, COMPASS-K (the Comprehensive Ocean Modeling, Prediction, Analysis and Synthesis System in the Kuroshio-region), has been developed at the Meteorological Research Institute (MRI). The purposes of the development are understanding ocean variability in the Kuroshio re gion as a local response to a global climate change with assimilated four-dimensional data sets, develop ment of an operational system in the Japan Meteorological Agency, and for the GODAE (Global Ocean Data Assimilation Experiment) project. The model is an eddy permitting version of an MRI-OGCM. Space-time decorrelation scales of ocean variability are estimated with TOPEX/POSEIDON (T/P) altimeter data. Subsurface temperature and salinity fields are projected from the T / P altimeter data with a statistical correlation method and are assim ilated into the model with a time-retrospective nudging scheme. Seasonal variation in the western North Pacific is investigated. Realistic space-time distribution of the physical quantities, the path of Kuroshio and its separation from Honshu are captured well. The Kuroshio volume transport is well reproduced in a reanalysis experiment of 1993. Preliminary predictability experi ments are done in February and March, 1994. Predictability diagram shows the time scale of the predictability for temperature field is about 17 days in the Kuroshio south of Japan. This time scale is smal ler than that in the North Atlantic.

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return