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Volume 27 Issue 4

Jul.  2010

Article Contents
Article >  ADVANCES IN ATMOSPHERIC SCIENCES  > 2010 >  27(4): 715-727

An Economical Approach to Four-dimensional Variational Data Assimilation

  • 1.

    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, Graduate School 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, Graduate School 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, Graduate School 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, Graduate School 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, Graduate School of the Chinese Academy of Sciences, Beijing 100049,College of Marine Science, University of South Florida, St. Petersburg, FL 33701, USA,Mesoscale and Microscale Meteorology Division, National Center for Atmospheric Research, Boulder, CO $80307$--3000, USA


doi: 10.1007/s00376-009-9122-3

  • Four-dimensional variational data assimilation (4DVar) is one of the most promising methods to provide optimal analysis for numerical weather prediction (NWP). Five national NWP centers in the world have successfully applied 4DVar methods in their global NWPs, thanks to the increment method and adjoint technique. However, the application of 4DVar is still limited by the computer resources available at many NWP centers and research institutes. It is essential, therefore, to further reduce the computational cost of 4DVar. Here, an economical approach to implement 4DVar is proposed, using the technique of dimension-reduced projection (DRP), which is called ``DRP-4DVar." The proposed approach is based on dimension reduction using an ensemble of historical samples to define a subspace. It directly obtains an optimal solution in the reduced space by fitting observations with historical time series generated by the model to form consistent forecast states, and therefore does not require implementation of the adjoint of tangent linear approximation. To evaluate the performance of the DRP-4DVar on assimilating different types of mesoscale observations, some observing system simulation experiments are conducted using MM5 and a comparison is made between adjoint-based 4DVar and DRP-4DVar using a 6-hour assimilation window.
  • [1] 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
    [2] 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
    [3] Jincheng WANG, Xingwei JIANG, Xueshun SHEN, Youguang ZHANG, Xiaomin WAN, Wei HAN, Dan WANG, 2023: Assimilation of Ocean Surface Wind Data by the HY-2B Satellite in GRAPES: Impacts on Analyses and Forecasts, ADVANCES IN ATMOSPHERIC SCIENCES, 40, 44-61.  doi: 10.1007/s00376-022-1349-2
    [4] 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
    [5] LIU Juan, WANG Bin, LIU Hailong, YU Yongqiang, 2008: A New Global Four-Dimensional Variational Ocean Data Assimilation System and Its Application, ADVANCES IN ATMOSPHERIC SCIENCES, 25, 680-691.  doi: 10.1007/s00376-008-0680-6
    [6] WANG Yunfeng, WANG Bin, HAN Yueqi, ZHU Min, HOU Zhiming, ZHOU Yi, LIU Yudi, KOU Zheng, 2004: Variational Data Assimilation Experiments of Mei-Yu Front Rainstorms in China, ADVANCES IN ATMOSPHERIC SCIENCES, 21, 587-596.  doi: 10.1007/BF02915726
    [7] 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
    [8] Yuyang GUO, Yongqiang YU, Pengfei LIN, Hailong LIU, Bian HE, Qing BAO, Shuwen ZHAO, Xiaowei WANG, 2020: Overview of the CMIP6 Historical Experiment Datasets with the Climate System Model CAS FGOALS-f3-L, ADVANCES IN ATMOSPHERIC SCIENCES, 37, 1057-1066.  doi: 10.1007/s00376-020-2004-4
    [9] Zhang Fuqing, Lin Zhenshan, Jiang Quanrong, 1994: The Fractal Dimension Distribution of the Short-Term Climate System in China and It’s Connection with the Monsoon Climate, ADVANCES IN ATMOSPHERIC SCIENCES, 11, 459-462.  doi: 10.1007/BF02658166
    [10] Jihang LI, Zhiyan ZHANG, Lu LIU, Xubin ZHANG, Jingxuan QU, Qilin WAN, 2021: The Simulation of Five Tropical Cyclones by Sample Optimization of Ensemble Forecasting Based on the Observed Track and Intensity, ADVANCES IN ATMOSPHERIC SCIENCES, 38, 1763-1777.  doi: 10.1007/s00376-021-0353-2
    [11] Pak-wai CHAN, Wei HAN, Betty MAK, Xiaohao QIN, Yongzhu LIU, Ruoying YIN, Jincheng WANG, 2023: Ground–Space–Sky Observing System Experiment during Tropical Cyclone Mulan in August 2022, ADVANCES IN ATMOSPHERIC SCIENCES, 40, 194-200.  doi: 10.1007/s00376-022-2267-z
    [12] Xuanming ZHAO, Jiang ZHU, Lijing CHENG, Yubao LIU, Yuewei LIU, 2020: An Observing System Simulation Experiment to Assess the Potential Impact of a Virtual Mobile Communication Tower–based Observation Network on Weather Forecasting Accuracy in China. Part 1: Weather Stations with a Typical Mobile Tower Height of 40 m, ADVANCES IN ATMOSPHERIC SCIENCES, 37, 617-633.  doi: 10.1007/s00376-020-9058-1
    [13] Xuanming ZHAO, Jiang ZHU, Lijing CHENG, Yubao LIU, Yuewei LIU, 2020: An Observing System Simulation Experiment to Assess the Potential Impact of a Virtual Mobile Communication Tower–based Observation Network on Weather Forecasting Accuracy in China. Part 1: Weather Stations with a Typical Mobile Tower Height of 40 m, ADVANCES IN ATMOSPHERIC SCIENCES.  doi: 10.1007/s00376-020-9058-1-bug
    [14] Xinyao RONG, Jian LI, Haoming CHEN, Jingzhi SU, Lijuan HUA, Zhengqiu ZHANG, Yufei XIN, 2021: The CMIP6 Historical Simulation Datasets Produced by the Climate System Model CAMS-CSM, ADVANCES IN ATMOSPHERIC SCIENCES, 38, 285-295.  doi: 10.1007/s00376-020-0171-y
    [15] Yang LU, Xiaochun WANG, Jihai DONG, 2021: Melt Pond Scheme Parameter Estimation Using an Adjoint Model, ADVANCES IN ATMOSPHERIC SCIENCES, 38, 1525-1536.  doi: 10.1007/s00376-021-0305-x
    [16] Yang Yan, Li Zhijin, Ji Liren, 1997: Adjoint Sensitivity Analyses on the Anomalous Circulation Features in East Asian Summer Monsoon, ADVANCES IN ATMOSPHERIC SCIENCES, 14, 111-123.  doi: 10.1007/s00376-997-0050-9
    [17] 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
    [18] Xiao-Tong ZHENG, Lihui GAO, Gen LI, Yan DU, 2016: The Southwest Indian Ocean Thermocline Dome in CMIP5 Models: Historical Simulation and Future Projection, ADVANCES IN ATMOSPHERIC SCIENCES, 33, 489-503.  doi: 10.1007/s00376-015-5076-9
    [19] Jiawen ZHU, Juanxiong HE, Duoying JI, Yangchun LI, He ZHANG, Minghua ZHANG, Xiaodong ZENG, Kece FEI, Jiangbo JIN, 2024: CAS-ESM2.0 Successfully Reproduces Historical Atmospheric CO2 in a Coupled Carbon−Climate Simulation, ADVANCES IN ATMOSPHERIC SCIENCES, 41, 572-580.  doi: 10.1007/s00376-023-3172-9
    [20] Wang Yuan, Wu Rongsheng, 2001: Theoretical Aspect of Suitable Spatial Boundary Condition Specified for Adjoint Model on Limited Area, ADVANCES IN ATMOSPHERIC SCIENCES, 18, 1081-1089.  doi: 10.1007/s00376-001-0024-2
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    • Manuscript History

    Manuscript received: 10 July 2010
    Manuscript revised: 10 July 2010
    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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    An Economical Approach to Four-dimensional Variational Data Assimilation

    • 1. 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, Graduate School 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, Graduate School 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, Graduate School 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, Graduate School 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, Graduate School of the Chinese Academy of Sciences, Beijing 100049,College of Marine Science, University of South Florida, St. Petersburg, FL 33701, USA,Mesoscale and Microscale Meteorology Division, National Center for Atmospheric Research, Boulder, CO $80307$--3000, USA
    • Manuscript received: 2010-07-10
    • Manuscript revised: 2010-07-10

    Abstract: Four-dimensional variational data assimilation (4DVar) is one of the most promising methods to provide optimal analysis for numerical weather prediction (NWP). Five national NWP centers in the world have successfully applied 4DVar methods in their global NWPs, thanks to the increment method and adjoint technique. However, the application of 4DVar is still limited by the computer resources available at many NWP centers and research institutes. It is essential, therefore, to further reduce the computational cost of 4DVar. Here, an economical approach to implement 4DVar is proposed, using the technique of dimension-reduced projection (DRP), which is called ``DRP-4DVar." The proposed approach is based on dimension reduction using an ensemble of historical samples to define a subspace. It directly obtains an optimal solution in the reduced space by fitting observations with historical time series generated by the model to form consistent forecast states, and therefore does not require implementation of the adjoint of tangent linear approximation. To evaluate the performance of the DRP-4DVar on assimilating different types of mesoscale observations, some observing system simulation experiments are conducted using MM5 and a comparison is made between adjoint-based 4DVar and DRP-4DVar using a 6-hour assimilation window.

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