Advanced Search
Impact Factor: 5.8

Volume 21 Issue 2

Mar.  2004

Article Contents
Article >  ADVANCES IN ATMOSPHERIC SCIENCES  > 2004 >  21(2): 181-192

Ocean Data Assimilation with Background Error Covariance Derived from OGCM Outputs

  • 1.

    Nansen-Zhu International Research Centre (NZC), Institute of Atmospheric Physics,Chinese Academy of Sciences, Beijing 100029,Nansen-Zhu International Research Centre (NZC), Institute of Atmospheric Physics,Chinese Academy of Sciences, Beijing 100029,Nansen-Zhu International Research Centre (NZC), Institute of Atmospheric Physics,Chinese Academy of Sciences, Beijing 100029


doi: 10.1007/BF02915704

  • The background error covariance plays an important role in modern data assimilation and analysis systems by determining the spatial spreading of information in the data. A novel method based on model output is proposed to estimate background error covariance for use in Optimum Interpolation. At every model level, anisotropic correlation scales are obtained that give a more detailed description of the spatial correlation structure. Furthermore, the impact of the background field itself is included in the background error covariance. The methodology of the estimation is presented and the structure of the covariance is examined. The results of 20-year assimilation experiments are compared with observations from TOGATAO (The Tropical Ocean-Global Atmosphere-Tropical Atmosphere Ocean) array and other analysis data.
  • [1] ZHENG Xiaogu, WU Guocan, ZHANG Shupeng, LIANG Xiao, DAI Yongjiu, LI Yong, , 2013: Using Analysis State to Construct a Forecast Error Covariance Matrix in Ensemble Kalman Filter Assimilation, ADVANCES IN ATMOSPHERIC SCIENCES, 30, 1303-1312.  doi: 10.1007/s00376-012-2133-5
    [2] LIU Juanjuan, WANG Bin, WANG Shudong, 2010: The Structure of Background-error Covariance in a Four-dimensional Variational Data Assimilation System: Single-point Experiment, ADVANCES IN ATMOSPHERIC SCIENCES, 27, 1303-1310.  doi: 10.1007/s00376-010-9067-6
    [3] Xiaogu ZHENG, 2009: An Adaptive Estimation of Forecast Error Covariance Parameters for Kalman Filtering Data Assimilation, ADVANCES IN ATMOSPHERIC SCIENCES, 26, 154-160.  doi: 10.1007/s00376-009-0154-5
    [4] Bohua Huang, James L. Kinter III, Paul S. Schopf, 2002: Ocean Data Assimilation Using Intermittent Analyses and Continuous Model Error Correction, ADVANCES IN ATMOSPHERIC SCIENCES, 19, 965-992.  doi: 10.1007/s00376-002-0059-z
    [5] Tao SUN, Yaodeng CHEN, Deming MENG, Haiqin CHEN, 2021: Background Error Covariance Statistics of Hydrometeor Control Variables Based on Gaussian Transform, ADVANCES IN ATMOSPHERIC SCIENCES, 38, 831-844.  doi: 10.1007/s00376-021-0271-3
    [6] Chaoqun MA, Tijian WANG, Zengliang ZANG, Zhijin LI, 2018: Comparisons of Three-Dimensional Variational Data Assimilation and Model Output Statistics in Improving Atmospheric Chemistry Forecasts, ADVANCES IN ATMOSPHERIC SCIENCES, 35, 813-825.  doi: 10.1007/s00376-017-7179-y
    [7] BAI Yulong, LI Xin, and HUANG Chunlin, 2013: Handling error propagation in sequential data assimilation using an evolutionary strategy, ADVANCES IN ATMOSPHERIC SCIENCES, 30, 1096-1105.  doi: 10.1007/s00376-012-2115-7
    [8] Xinrong WU, Shaoqing ZHANG, Zhengyu LIU, 2016: Implementation of a One-Dimensional Enthalpy Sea-Ice Model in a Simple Pycnocline Prediction Model for Sea-Ice Data Assimilation Studies, ADVANCES IN ATMOSPHERIC SCIENCES, 33, 193-207.  doi: 10.1007/s00376-015-5099-2
    [9] Banglin ZHANG, Vijay TALLAPRAGADA, Fuzhong WENG, Jason SIPPEL, Zaizhong MA, 2016: Estimation and Correction of Model Bias in the NASA/GMAO GEOS5 Data Assimilation System: Sequential Implementation, ADVANCES IN ATMOSPHERIC SCIENCES, 33, 659-672.  doi: 10.1007/ s00376-015-5155-y
    [10] Qizhen SUN, Timo VIHMA, Marius O. JONASSEN, Zhanhai ZHANG, 2020: Impact of Assimilation of Radiosonde and UAV Observations from the Southern Ocean in the Polar WRF Model, ADVANCES IN ATMOSPHERIC SCIENCES, 37, 441-454.  doi: 10.1007/s00376-020-9213-8
    [11] GAO Feng*, Peter P. CHILDS, Xiang-Yu HUANG, Neil A. JACOBS, and Jinzhong MIN, 2014: A Relocation-based Initialization Scheme to Improve Track-forecasting of Tropical Cyclones, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 27-36.  doi: 10.1007/s00376-013-2254-5
    [12] LIU Liping, ZHANG Pengfei, Qin XU, KONG Fanyou, LIU Shun, 2005: A Model for Retrieval of Dual Linear Polarization Radar Fields from Model Simulation Outputs, ADVANCES IN ATMOSPHERIC SCIENCES, 22, 711-719.  doi: 10.1007/BF02918714
    [13] 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
    [14] Yong LI, Siming LI, Yao SHENG, Luheng WANG, 2018: Data Assimilation Method Based on the Constraints of Confidence Region, ADVANCES IN ATMOSPHERIC SCIENCES, 35, 334-345.  doi: 10.1007/s00376-017-7045-y
    [15] Banglin ZHANG, Vijay TALLAPRAGADA, Fuzhong WENG, Jason SIPPEL, Zaizhong MA, 2015: Use of Incremental Analysis Updates in 4D-Var Data Assimilation, ADVANCES IN ATMOSPHERIC SCIENCES, 32, 1575-1582.  doi: 10.1007/s00376-015-5041-7
    [16] Jidong GAO, Keith BREWSTER, Ming XUE, 2006: A Comparison of the Radar Ray Path Equations and Approximations for Use in Radar Data Assimilation, ADVANCES IN ATMOSPHERIC SCIENCES, 23, 190-198.  doi: 10.1007/s00376-006-0190-3
    [17] Fuqing ZHANG, Meng ZHANG, James A. HANSEN, 2009: Coupling Ensemble Kalman Filter with Four-dimensional Variational Data Assimilation, ADVANCES IN ATMOSPHERIC SCIENCES, 26, 1-8.  doi: 10.1007/s00376-009-0001-8
    [18] Ting WANG, Jianfang FEI, Xiaoping CHENG, Xiaogang HUANG, Jian ZHONG, 2018: Estimating the Correlated Observation-Error Characteristics of the Chinese FengYun Microwave Temperature Sounder and Microwave Humidity Sounder, ADVANCES IN ATMOSPHERIC SCIENCES, 35, 1428-1441.  doi: 10.1007/s00376-018-8014-9
    [19] Xiaoli XIA, Jinzhong MIN, Feifei SHEN, Yuanbing WANG, Chun YANG, 2019: Aerosol Data Assimilation Using Data from Fengyun-3A and MODIS: Application to a Dust Storm over East Asia in 2011, ADVANCES IN ATMOSPHERIC SCIENCES, 36, 1-14.  doi: 10.1007/s00376-018-8075-9
    [20] Zhu Jiang, Wang Hui, Masafumi Kamachi, 2002: The Improvement Made by a Modified TLM in 4DVAR with a Geophysical Boundary Layer Model, ADVANCES IN ATMOSPHERIC SCIENCES, 19, 563-582.  doi: 10.1007/s00376-002-0001-4
PDF

Get Citation+

shu
Export:  

Share Article

Article Metrics

Article Views: 1111 Times

PDF downloads: 943 Times

Cited by: Times
  • 加载中

    • Manuscript History

    Manuscript received: 10 March 2004
    Manuscript revised: 10 March 2004
    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

    Ocean Data Assimilation with Background Error Covariance Derived from OGCM Outputs

    • 1. Nansen-Zhu International Research Centre (NZC), Institute of Atmospheric Physics,Chinese Academy of Sciences, Beijing 100029,Nansen-Zhu International Research Centre (NZC), Institute of Atmospheric Physics,Chinese Academy of Sciences, Beijing 100029,Nansen-Zhu International Research Centre (NZC), Institute of Atmospheric Physics,Chinese Academy of Sciences, Beijing 100029
    • Manuscript received: 2004-03-10
    • Manuscript revised: 2004-03-10

    Abstract: The background error covariance plays an important role in modern data assimilation and analysis systems by determining the spatial spreading of information in the data. A novel method based on model output is proposed to estimate background error covariance for use in Optimum Interpolation. At every model level, anisotropic correlation scales are obtained that give a more detailed description of the spatial correlation structure. Furthermore, the impact of the background field itself is included in the background error covariance. The methodology of the estimation is presented and the structure of the covariance is examined. The results of 20-year assimilation experiments are compared with observations from TOGATAO (The Tropical Ocean-Global Atmosphere-Tropical Atmosphere Ocean) array and other analysis data.

      HTML

    Catalog

      Publish with AAS

      Contact us

      Links

      Copyright © 2018-2028 ADVANCES IN ATMOSPHERIC SCIENCES 京ICP备14024088号-7

      Supported by: Beijing Renhe Information Technology Co. Ltd  support: info@rhhz.net  

      /

      DownLoad:  Full-Size Img  PowerPoint
      Return
      Return