Advanced Search
Article Contents

Impact of Analysis-time Tuning on the Performance of the DRP-4DVar Approach


doi: 10.1007/s00376-010-9191-3

  • In this study we extend the dimension-reduced projection-four dimensional variational data assimilation (DRP-4DVar) approach to allow the analysis time to be tunable, so that the intervals between analysis time and observation times can be shortened. Due to the limits of the perfect-model assumption and the tangent-linear hypothesis, the analysis-time tuning is expected to have the potential to further improve analyses and forecasts. Various sensitivity experiments using the Lorenz-96 model are conducted to test the impact of analysis-time tuning on the performance of the new approach under perfect and imperfect model scenarios, respectively. Comparing three DRP-4DVar schemes having the analysis time at the start, middle, and end of the assimilation window, respectively, it is found that the scheme with the analysis time in the middle of the window outperforms the others, on the whole. Moreover, the advantage of this scheme is more pronounced when a longer assimilation window is adopted or more observations are assimilated.
  • [1] 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
    [2] Mu Mu, Guo Huan, Wang Jiafeng, LiYong, 2000: The Impact of Nonlinear Stability and Instability on the Validity of the Tangent Linear Model, ADVANCES IN ATMOSPHERIC SCIENCES, 17, 375-390.  doi: 10.1007/s00376-000-0030-9
    [3] 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
    [4] Ji-Hyun HA, Dong-Kyou LEE, 2012: Effect of Length Scale Tuning of Background Error in WRF-3DVAR System on Assimilation of High-Resolution Surface Data for Heavy Rainfall Simulation, ADVANCES IN ATMOSPHERIC SCIENCES, 29, 1142-1158.  doi: 10.1007/s00376-012-1183-z
    [5] Lin Wantao, Ji Zhongzhen, Wang Bin, 2002: A Comparative Analysis of Computational Stability for Linear and Non-Linear Evolution Equations, ADVANCES IN ATMOSPHERIC SCIENCES, 19, 699-704.  doi: 10.1007/s00376-002-0009-9
    [6] Wang Yunfeng, Wu Rongsheng, Wang Yuan, Pan Yinong, 2000: A Simple Method of Calculating the Optimal Step Size in 4DVAR Technique, ADVANCES IN ATMOSPHERIC SCIENCES, 17, 433-444.  doi: 10.1007/s00376-000-0034-5
    [7] KE Zongjian, DONG Wenjie, ZHANG Peiqun, WANG Jin, ZHAO Tianbao, 2009: An Analysis of the Difference between the Multiple Linear Regression Approach and the Multimodel Ensemble Mean, ADVANCES IN ATMOSPHERIC SCIENCES, 26, 1157-1168.  doi: 10.1007/s00376-009-8024-8
    [8] Xiangjun TIAN, Xiaobing FENG, 2019: An Adjoint-Free CNOP-4DVar Hybrid Method for Identifying Sensitive Areas in Targeted Observations: Method Formulation and Preliminary Evaluation, ADVANCES IN ATMOSPHERIC SCIENCES, , 721-732.  doi: 10.1007/s00376-019-9001-5
    [9] Lu ZHANG, Xiangjun TIAN, Hongqin ZHANG, Feng CHEN, 2020: Impacts of Multigrid NLS-4DVar-based Doppler Radar Observation Assimilation on Numerical Simulations of Landfalling Typhoon Haikui (2012), ADVANCES IN ATMOSPHERIC SCIENCES, 37, 873-892.  doi: 10.1007/s00376-020-9274-8
    [10] CHU Kekuan, TAN Zhemin, Ming XUE, 2007: Impact of 4DVAR Assimilation of Rainfall Data on the Simulation of Mesoscale Precipitation Systems in a Mei-yu Heavy Rainfall Event, ADVANCES IN ATMOSPHERIC SCIENCES, 24, 281-300.  doi: 10.1007/s00376-007-0281-9
    [11] 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
    [12] Chang LIU, Shaoqing ZHANG, Shan LI, Zhengyu LIU, 2017: Impact of the Time Scale of Model Sensitivity Response on Coupled Model Parameter Estimation, ADVANCES IN ATMOSPHERIC SCIENCES, 34, 1346-1357.  doi: 10.1007/s00376-017-6272-6
    [13] 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
    [14] Yujie PAN, Mingjun WANG, 2019: Impact of the Assimilation Frequency of Radar Data with the ARPS 3DVar and Cloud Analysis System on Forecasts of a Squall Line in Southern China, ADVANCES IN ATMOSPHERIC SCIENCES, 36, 160-172.  doi: 10.1007/s00376-018-8087-5
    [15] FU Weiwei, 2012: Altimetric Data Assimilation by EnOI and 3DVAR in a Tropical Pacific Model: Impact on the Simulation of Variability, ADVANCES IN ATMOSPHERIC SCIENCES, 29, 823-837.  doi: 10.1007/s00376-011-1022-7
    [16] Peng Yongqing, Yan Shaojin, Wang Tongmei, 1995: A Nonlinear Time-lag Differential Equation Model for Predicting Monthly Precipitation, ADVANCES IN ATMOSPHERIC SCIENCES, 12, 319-324.  doi: 10.1007/BF02656980
    [17] Chao Jiping, Ji Zhengang, 1985: ON THE INFLUENCES OF LARGE-SCALE INHOMOGENEITY OF SEA TEMPERATURE UPON THE OCEANIC WAVES IN THE TROPICAL REGIONS——PART I : LINEAR THEORETICAL ANALYSIS, ADVANCES IN ATMOSPHERIC SCIENCES, 2, 295-306.  doi: 10.1007/BF02677245
    [18] Rong KONG, Ming XUE, Edward R. MANSELL, Chengsi LIU, Alexandre O. FIERRO, 2024: Assimilation of GOES-R Geostationary Lightning Mapper Flash Extent Density Data in GSI 3DVar, EnKF, and Hybrid En3DVar for the Analysis and Short-Term Forecast of a Supercell Storm Case, ADVANCES IN ATMOSPHERIC SCIENCES, 41, 263-277.  doi: 10.1007/s00376-023-2340-2
    [19] Silvia Alessio, Arnaldo Longhetto, Luo Meixia, 1999: The Space and Time Features of Global SST Anomalies Studied by Complex Principal Component Analysis, ADVANCES IN ATMOSPHERIC SCIENCES, 16, 1-23.  doi: 10.1007/s00376-999-0001-8
    [20] QIAN Weihong, LIN Xiang, 2009: An Integrated Analysis of Dry-Wet Variability in Western China for the Last 4--5 Centuries, ADVANCES IN ATMOSPHERIC SCIENCES, 26, 951-961.  doi: 10.1007/s00376-009-8070-2

Get Citation+

Export:  

Share Article

Manuscript History

Manuscript received: 10 January 2011
Manuscript revised: 10 January 2011
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

Impact of Analysis-time Tuning on the Performance of the DRP-4DVar Approach

  • 1. State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics,Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, Graduate University of the Chinese Academy of Sciences, Beijing 100049,State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029,State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029

Abstract: In this study we extend the dimension-reduced projection-four dimensional variational data assimilation (DRP-4DVar) approach to allow the analysis time to be tunable, so that the intervals between analysis time and observation times can be shortened. Due to the limits of the perfect-model assumption and the tangent-linear hypothesis, the analysis-time tuning is expected to have the potential to further improve analyses and forecasts. Various sensitivity experiments using the Lorenz-96 model are conducted to test the impact of analysis-time tuning on the performance of the new approach under perfect and imperfect model scenarios, respectively. Comparing three DRP-4DVar schemes having the analysis time at the start, middle, and end of the assimilation window, respectively, it is found that the scheme with the analysis time in the middle of the window outperforms the others, on the whole. Moreover, the advantage of this scheme is more pronounced when a longer assimilation window is adopted or more observations are assimilated.

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return