Advanced Search
Impact Factor: 5.8

Volume 22 Issue 2

Mar.  2005

Article Contents
Article >  ADVANCES IN ATMOSPHERIC SCIENCES  > 2005 >  22(2): 212-219

A Comparison Study of Tropical Pacific Ocean State Estimation: Low-Resolution Assimilation vs. High-Resolution Simulation

  • 1.

    Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029;Graduate School of the Chinese Academy of Sciences, Beijing 100039,Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029,Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029,Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029


doi: 10.1007/BF02918510

  • A comparison study is performed to contrast the improvements in the tropical Pacific oceanic state of a low-resolution model respectively via data assimilation and by an increase in horizontal resolution.A low resolution model (LR) (1°lat by 2°lon) and a high-resolution model (HR) (0.5°lat by 0.5°lon) are employed for the comparison. The authors perform 20-yr numerical experiments and analyze the annual mean fields of temperature and salinity. The results indicate that the low-resolution model with data assimilation behaves better than the high-resolution model in the estimation of ocean large-scale features.From 1990 to 2000, the average of HR's RMSE (root-mean-square error) relative to independent Tropical Atmosphere Ocean project (TAO) mooring data at randomly selected points is 0.97℃ compared to a RMSE of 0.56℃ for LR with temperature assimilation. Moreover, the LR with data assimilation is more frugal in computation. Although there is room to improve the high-resolution model, the low-resolution model with data assimilation may be an advisable choice in achieving a more realistic large-scale state of the ocean at the limited level of information provided by the current observational system.
  • [1] 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
    [2] 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
    [3] 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
    [4] 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
    [5] 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
    [6] 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
    [7] 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
    [8] 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
    [9] Sibo ZHANG, Li GUAN, 2017: Preliminary Study on Direct Assimilation of Cloud-affected Satellite Microwave Brightness Temperatures, ADVANCES IN ATMOSPHERIC SCIENCES, 34, 199-208.  doi: 10.1007/s00376-016-6043-9
    [10] 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
    [11] 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
    [12] 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
    [13] FU Weiwei, ZHOU Guangqing, WANG Huijun, 2004: Ocean Data Assimilation with Background Error Covariance Derived from OGCM Outputs, ADVANCES IN ATMOSPHERIC SCIENCES, 21, 181-192.  doi: 10.1007/BF02915704
    [14] 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
    [15] 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
    [16] 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
    [17] Linbin He, Weiyi Peng, Yu Zhang, Shiguang Miao, Siqi Chen, Jiajing Li, Duanzhou Shao, Xutao Zhang, 2024: Comparison of Adaptive Simulation Observation Experiments of the Heavy Rainfall in South China and Sichuan Basin, ADVANCES IN ATMOSPHERIC SCIENCES.  doi: 10.1007/s00376-024-3114-1
    [18] 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
    [19] Yaodeng CHEN, Jie SHEN, Shuiyong FAN, Deming MENG, Cheng WANG, 2020: Characteristics of Fengyun-4A Satellite Atmospheric Motion Vectors and Their Impacts on Data Assimilation, ADVANCES IN ATMOSPHERIC SCIENCES, 37, 1222-1238.  doi: 10.1007/s00376-020-0080-0
    [20] Guifu ZHANG, Jidong GAO, Muyun DU, 2021: Parameterized Forward Operators for Simulation and Assimilation of Polarimetric Radar Data with Numerical Weather Predictions, ADVANCES IN ATMOSPHERIC SCIENCES, 38, 737-754.  doi: 10.1007/s00376-021-0289-6
PDF

Get Citation+

shu
Export:  

Share Article

Article Metrics

Article Views: 1386 Times

PDF downloads: 1200 Times

Cited by: Times
  • 加载中

    • Manuscript History

    Manuscript received: 10 March 2005
    Manuscript revised: 10 March 2005
    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

    A Comparison Study of Tropical Pacific Ocean State Estimation: Low-Resolution Assimilation vs. High-Resolution Simulation

    • 1. Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029;Graduate School of the Chinese Academy of Sciences, Beijing 100039,Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029,Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029,Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029
    • Manuscript received: 2005-03-10
    • Manuscript revised: 2005-03-10

    Abstract: A comparison study is performed to contrast the improvements in the tropical Pacific oceanic state of a low-resolution model respectively via data assimilation and by an increase in horizontal resolution.A low resolution model (LR) (1°lat by 2°lon) and a high-resolution model (HR) (0.5°lat by 0.5°lon) are employed for the comparison. The authors perform 20-yr numerical experiments and analyze the annual mean fields of temperature and salinity. The results indicate that the low-resolution model with data assimilation behaves better than the high-resolution model in the estimation of ocean large-scale features.From 1990 to 2000, the average of HR's RMSE (root-mean-square error) relative to independent Tropical Atmosphere Ocean project (TAO) mooring data at randomly selected points is 0.97℃ compared to a RMSE of 0.56℃ for LR with temperature assimilation. Moreover, the LR with data assimilation is more frugal in computation. Although there is room to improve the high-resolution model, the low-resolution model with data assimilation may be an advisable choice in achieving a more realistic large-scale state of the ocean at the limited level of information provided by the current observational system.

      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