Advanced Search
Impact Factor: 5.8

Volume 23 Issue 1

Jan.  2006

Article Contents
Article >  ADVANCES IN ATMOSPHERIC SCIENCES  > 2006 >  23(1): 106-117

Retrieval of Atmospheric and Oceanic Parameters and the Relevant Numerical Calculation

  • 1.

    Institute of Meteorology, PLA University of Science and Engineering, Nanjing 211101, Key Laboratory of Meso-Scale Severe Weather/MOE, Department of Atmospheric Sciences, Nanjing University, Nanjing 210093,Institute of Meteorology, PLA University of Science and Engineering, Nanjing 211101,Institute of Meteorology, PLA University of Science and Engineering, Nanjing 211101,Institute of Meteorology, PLA University of Science and Engineering, Nanjing 211101,Institute of Meteorology, PLA University of Science and Engineering, Nanjing 211101, Key Laboratory of Meso-Scale Severe Weather/MOE, Department of Atmospheric Sciences, Nanjing University, Nanjing 210093


doi: 10.1007/s00376-006-0011-8

  • It is well known that retrieval of parameters is usually ill-posed and highly nonlinear, so parameter retrieval problems are very difficult. There are still many important theoretical issues under research, although great success has been achieved in data assimilation in meteorology and oceanography. This paper reviews the recent research on parameter retrieval, especially that of the authors. First, some concepts and issues of parameter retrieval are introduced and the state-of-the-art parameter retrieval technology in meteorology and oceanography is reviewed briefly, and then atmospheric and oceanic parameters are retrieved using the variational data assimilation method combined with the regularization techniques in four examples: retrieval of the vertical eddy diffusion coefficient; of the turbulivity of the atmospheric boundary layer; of wind from Doppler radar data, and of the physical process parameters. Model parameter retrieval with global and local observations is also introduced.
  • [1] Shen YAN, Jie XIANG, Huadong DU, 2019: Determining Atmospheric Boundary Layer Height with the Numerical Differentiation Method Using Bending Angle Data from COSMIC, ADVANCES IN ATMOSPHERIC SCIENCES, 36, 303-312.  doi: 10.1007/s00376-018-7308-2
    [2] Zhu Keyun, 2001: On the 4D Variational Data Assimilation with Constraint Conditions, ADVANCES IN ATMOSPHERIC SCIENCES, 18, 1131-1145.  doi: 10.1007/s00376-001-0028-y
    [3] ZHANG Xiaoyan, WANG Bin, JI Zhongzhen, Qingnong XIAO, ZHANG Xin, 2003: Initialization and Simulation of a Typhoon Using 4-Dimensional Variational Data Assimilation-Research on Typhoon Herb(1996), ADVANCES IN ATMOSPHERIC SCIENCES, 20, 612-622.  doi: 10.1007/BF02915504
    [4] ZHANG Xin, LIU Yuewei, WANG Bin, JI Zhongzhen, 2004: Parallel Computing of a Variational Data Assimilation Model for GPS/MET Observation Using the Ray-Tracing Method, ADVANCES IN ATMOSPHERIC SCIENCES, 21, 220-226.  doi: 10.1007/BF02915708
    [5] Yaodeng CHEN, Ruizhi ZHANG, Deming MENG, Jinzhong MIN, Lina ZHANG, 2016: Variational Assimilation of Satellite Cloud Water/Ice Path and Microphysics Scheme Sensitivity to the Assimilation of a Rainfall Case, ADVANCES IN ATMOSPHERIC SCIENCES, 33, 1158-1170.  doi: 10.1007/s00376-016-6004-3
    [6] FANG Changluan, ZHENG Qin, WU Wenhua, DAI Yi, 2009: Intelligent Optimization Algorithms to VDA of Models with on/off Parameterizations, ADVANCES IN ATMOSPHERIC SCIENCES, 26, 1181-1197.  doi: 10.1007/s00376-009-8084-9
    [7] 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
    [8] Zhu Jiang, Masafumi Kamachi, Zhou Guangqing, 2002: Nonsmooth Optimization Approaches to VDA of Models with on/ off Parameterizations: Theoretical Issues, ADVANCES IN ATMOSPHERIC SCIENCES, 19, 405-424.  doi: 10.1007/s00376-002-0075-z
    [9] Huang Sixun, 1996: Inversion and Ill-Posed Problem Solutions in Atmospheric Remote Sensing, ADVANCES IN ATMOSPHERIC SCIENCES, 13, 489-504.  doi: 10.1007/BF03342039
    [10] Wei Ming, Dang Renqing, Ge Wenzhong, Takao Takeda, 1998: Retrieval Single-Doppler Radar Wind with Variational Assimilation Method-Part I: Objective Selection of Functional Weighting Factors, ADVANCES IN ATMOSPHERIC SCIENCES, 15, 553-568.  doi: 10.1007/s00376-998-0032-6
    [11] 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
    [12] WANG Bin, LIU Juanjuan, WANG Shudong, CHENG Wei, LIU Juan, LIU Chengsi, Qingnong XIAO, Ying-Hwa KUO, 2010: An Economical Approach to Four-dimensional Variational Data Assimilation, ADVANCES IN ATMOSPHERIC SCIENCES, 27, 715-727.  doi: 10.1007/s00376-009-9122-3
    [13] 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
    [14] ZENG Zhihua, DUAN Yihong, LIANG Xudong, MA Leiming, Johnny Chung-leung CHAN, 2005: The Effect of Three-Dimensional Variational Data Assimilation of QuikSCAT Data on the Numerical Simulation of Typhoon Track and Intensity, ADVANCES IN ATMOSPHERIC SCIENCES, 22, 534-544.  doi: 10.1007/BF02918486
    [15] LIU Ye, YAN Changxiang, 2010: Application of a Recursive Filter to a Three-Dimensional Variational Ocean Data Assimilation System, ADVANCES IN ATMOSPHERIC SCIENCES, 27, 293-302.  doi: 10.1007/s00376-009-8112-9
    [16] Chuan GAO, Xinrong WU, Rong-Hua ZHANG, 2016: Testing a Four-Dimensional Variational Data Assimilation Method Using an Improved Intermediate Coupled Model for ENSO Analysis and Prediction, ADVANCES IN ATMOSPHERIC SCIENCES, 33, 875-888.  doi: 10.1007/s00376-016-5249-1
    [17] Xin LI, Mingjian ZENG, Yuan WANG, Wenlan WANG, Haiying WU, Haixia MEI, 2016: Evaluation of Two Momentum Control Variable Schemes and Their Impact on the Variational Assimilation of Radar Wind Data: Case Study of a Squall Line, ADVANCES IN ATMOSPHERIC SCIENCES, 33, 1143-1157.  doi: 10.1007/s00376-016-5255-3
    [18] 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
    [19] ZHANG Lin, NI Yunqi, 2005: Four-Dimensional Variational Data Assimilation Experiments for a Heavy Rain Case During the 2002 IOP in China, ADVANCES IN ATMOSPHERIC SCIENCES, 22, 300-312.  doi: 10.1007/BF02918519
    [20] 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
PDF

Get Citation+

shu
Export:  

Share Article

Article Metrics

Article Views: 1244 Times

PDF downloads: 1070 Times

Cited by: Times
  • 加载中

    • Manuscript History

    Manuscript received: 10 January 2006
    Manuscript revised: 10 January 2006
    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

    Retrieval of Atmospheric and Oceanic Parameters and the Relevant Numerical Calculation

    • 1. Institute of Meteorology, PLA University of Science and Engineering, Nanjing 211101, Key Laboratory of Meso-Scale Severe Weather/MOE, Department of Atmospheric Sciences, Nanjing University, Nanjing 210093,Institute of Meteorology, PLA University of Science and Engineering, Nanjing 211101,Institute of Meteorology, PLA University of Science and Engineering, Nanjing 211101,Institute of Meteorology, PLA University of Science and Engineering, Nanjing 211101,Institute of Meteorology, PLA University of Science and Engineering, Nanjing 211101, Key Laboratory of Meso-Scale Severe Weather/MOE, Department of Atmospheric Sciences, Nanjing University, Nanjing 210093
    • Manuscript received: 2006-01-10
    • Manuscript revised: 2006-01-10

    Abstract: It is well known that retrieval of parameters is usually ill-posed and highly nonlinear, so parameter retrieval problems are very difficult. There are still many important theoretical issues under research, although great success has been achieved in data assimilation in meteorology and oceanography. This paper reviews the recent research on parameter retrieval, especially that of the authors. First, some concepts and issues of parameter retrieval are introduced and the state-of-the-art parameter retrieval technology in meteorology and oceanography is reviewed briefly, and then atmospheric and oceanic parameters are retrieved using the variational data assimilation method combined with the regularization techniques in four examples: retrieval of the vertical eddy diffusion coefficient; of the turbulivity of the atmospheric boundary layer; of wind from Doppler radar data, and of the physical process parameters. Model parameter retrieval with global and local observations is also introduced.

      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