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Volume 20 Issue 1

Jan.  2003

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Article >  ADVANCES IN ATMOSPHERIC SCIENCES  > 2003 >  20(1): 17-27

Systematic Errors of Zonal-Mean Flow in Dynamical Monthly Prediction and Its Improvement

  • 1.

    LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029;Shanghai Typhoon Institute, Shanghai 200030,LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029,LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029,LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029


doi: 10.1007/BF03342046

  • An analysis of a large number of cases of 500 hPa height monthly prediction shows that systematicerrors exist in the zonal mean components which account for a large portion of the total forecast errors, andsuch errors are commonly seen in other prediction models. To overcome the difficulties of the numericalmodel, the authors attempt a hybrid approach to improving the dynamical extended-range (monthly)prediction. The monthly pentad-mean nonlinear dynamical regional prediction model of the zonal-meangeopotential height (wave number 0) based on a large amount of data is constituted by employing thereconstruction of phase-space theory and the spatio-temporal series predictive method. The dynamicalprediction of the numerical model is then combined with that of the nonlinear model, i.e., the pentad-mean zonal-mean height produced by the nonlinear model is transformed to its counterpart in the numericalmodel by nudging during the time integration. The forecast experiment results show that the above hybridapproach not only reduces the systematic error in zonal mean height by the numerical model, but alsomakes an improvement in the non-axisymmetric components due to the wave-flow interaction.
  • [1] Shingo Yamada, Shuhei Maeda, K. Gambo, 1997: Notes on Extended-Range Atmospheric Prediction in the Northern Hemisphere Winter, ADVANCES IN ATMOSPHERIC SCIENCES, 14, 23-40.  doi: 10.1007/s00376-997-0040-y
    [2] XIA Zhiye, CHEN Hongbin, XU Lisheng, WANG Yongqian, 2015: Extended Range (10-30 Days) Heavy Rain Forecasting Study Based on a Nonlinear Cross-Prediction Error Model, ADVANCES IN ATMOSPHERIC SCIENCES, 32, 1583-1591.  doi: 10.1007/s00376-015-4252-2
    [3] LI Xiaofeng, LI Jianping, Xiangdong ZHANG, 2013: A Two-way Stratosphere-Troposphere Coupling of Submonthly Zonal-Mean Circulations in the Arctic, ADVANCES IN ATMOSPHERIC SCIENCES, 30, 1771-1785.  doi: 10.1007/s00376-013-2210-4
    [4] Chunxiang LI, Guokun DAI, Mu MU, Zhe HAN, Xueying MA, Zhina JIANG, Jiayu ZHENG, Mengbin ZHU, 2023: Influence of Arctic Sea-ice Concentration on Extended-range Forecasting of Cold Events in East Asia, ADVANCES IN ATMOSPHERIC SCIENCES, 40, 2224-2241.  doi: 10.1007/s00376-023-3010-0
    [5] CHEN Hong, LIN Zhaohui, 2006: A Correction Method Suitable for Dynamical Seasonal Prediction, ADVANCES IN ATMOSPHERIC SCIENCES, 23, 425-430.  doi: 10.1007/s00376-006-0425-3
    [6] Zhizhen XU, Jing CHEN, Mu MU, Guokun DAI, Yanan MA, 2022: A Nonlinear Representation of Model Uncertainty in a Convective-Scale Ensemble Prediction System, ADVANCES IN ATMOSPHERIC SCIENCES, 39, 1432-1450.  doi: 10.1007/s00376-022-1341-x
    [7] Jeong-Hyeong LEE, Keon-Tae SOHN, 2007: Prediction of Monthly Mean Surface Air Temperature in a Region of China, ADVANCES IN ATMOSPHERIC SCIENCES, 24, 503-508.  doi: 10.1007/s00376-007-0503-1
    [8] Lili LEI, Yangjinxi GE, Zhe-Min TAN, Yi ZHANG, Kekuan CHU, Xin QIU, Qifeng QIAN, 2022: Evaluation of a Regional Ensemble Data Assimilation System for Typhoon Prediction, ADVANCES IN ATMOSPHERIC SCIENCES, 39, 1816-1832.  doi: 10.1007/s00376-022-1444-4
    [9] Suk-Jin CHOI, Dong-Kyou LEE, 2016: Impact of Spectral Nudging on the Downscaling of Tropical Cyclones in Regional Climate Simulations, ADVANCES IN ATMOSPHERIC SCIENCES, 33, 730-742.  doi: 10.1007/s00376-016-5061-y
    [10] Liu Jianwen, Dong Peiming, 2001: Short-range Climate Prediction Experiment of the Southern Oscillation Index Based on the Singular Spectrum Analysis, ADVANCES IN ATMOSPHERIC SCIENCES, 18, 873-881.
    [11] Swati Basu, K. J. Ramesh, Z. N. Begum, 1999: Medium Range Prediction of Summer Monsoon Activities over India vis-a-vis Their Correspondence with the Observational Features, ADVANCES IN ATMOSPHERIC SCIENCES, 16, 133-146.  doi: 10.1007/s00376-999-0009-0
    [12] Jiaqi Zheng, Qing Ling, Jia Li, Yerong Feng, 2023: Improving Short-Range Precipitation Forecast of Numerical Weather Prediction Through a Deep Learning-Based Mask Approach, ADVANCES IN ATMOSPHERIC SCIENCES.  doi: 10.1007/s00376-023-3085-7
    [13] Yang Hui, Zhang Daomin, Ji Liren, 2001: An Approach to Extract Effective Information of Monthly Dynamical Prediction-The Use of Ensemble Method, ADVANCES IN ATMOSPHERIC SCIENCES, 18, 283-293.  doi: 10.1007/s00376-001-0020-6
    [14] LIN Zhaohui, WANG Huijun, ZHOU Guangqing, CHEN Hong, LANG Xianmei, ZHAO Yan, ZENG Qingcun, 2004: Recent Advances in Dynamical Extra-Seasonal to Annual Climate Prediction at IAP/CAS, ADVANCES IN ATMOSPHERIC SCIENCES, 21, 456-466.  doi: 10.1007/BF02915572
    [15] LANG Xianmei, ZHENG Fei, 2011: A Statistical-Dynamical Scheme for the Extraseasonal Prediction of Summer Rainfall for 160 Observation Stations across China, ADVANCES IN ATMOSPHERIC SCIENCES, 28, 1291-1300.  doi: 10.1007/s00376-011-0177-6
    [16] JIANG Zhina, MU Mu, 2009: A Comparison Study of the Methods of Conditional Nonlinear Optimal Perturbations and Singular Vectors in Ensemble Prediction, ADVANCES IN ATMOSPHERIC SCIENCES, 26, 465-470.  doi: 10.1007/s00376-009-0465-6
    [17] Sijia LI, Yuan WANG, Huiling YUAN, Jinjie SONG, Xin XU, 2016: Ensemble Mean Forecast Skill and Applications with the T213 Ensemble Prediction System, ADVANCES IN ATMOSPHERIC SCIENCES, 33, 1297-1305.  doi: 10.1007/s00376-016-6155-2
    [18] Yan Shaojin, Peng Yongqing, Quo Guang, 1995: Monthly Mean Temperature Prediction Based on a Multi-level Mapping Model of Neural Network BP Type, ADVANCES IN ATMOSPHERIC SCIENCES, 12, 225-232.  doi: 10.1007/BF02656835
    [19] ZHANG Hanbin, CHEN Jing, ZHI Xiefei, WANG Yi, WANG Yanan, 2015: Study on Multi-Scale Blending Initial Condition Perturbations for a Regional Ensemble Prediction System, ADVANCES IN ATMOSPHERIC SCIENCES, 32, 1143-1155.  doi: 10.1007/s00376-015-4232-6
    [20] Xia LIU, Qiang WANG, Mu MU, 2018: Optimal Initial Error Growth in the Prediction of the Kuroshio Large Meander Based on a High-resolution Regional Ocean Model, ADVANCES IN ATMOSPHERIC SCIENCES, 35, 1362-1371.  doi: 10.1007/s00376-018-8003-z
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    • Manuscript History

    Manuscript received: 10 January 2003
    Manuscript revised: 10 January 2003
    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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    Systematic Errors of Zonal-Mean Flow in Dynamical Monthly Prediction and Its Improvement

    • 1. LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029;Shanghai Typhoon Institute, Shanghai 200030,LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029,LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029,LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029
    • Manuscript received: 2003-01-10
    • Manuscript revised: 2003-01-10

    Abstract: An analysis of a large number of cases of 500 hPa height monthly prediction shows that systematicerrors exist in the zonal mean components which account for a large portion of the total forecast errors, andsuch errors are commonly seen in other prediction models. To overcome the difficulties of the numericalmodel, the authors attempt a hybrid approach to improving the dynamical extended-range (monthly)prediction. The monthly pentad-mean nonlinear dynamical regional prediction model of the zonal-meangeopotential height (wave number 0) based on a large amount of data is constituted by employing thereconstruction of phase-space theory and the spatio-temporal series predictive method. The dynamicalprediction of the numerical model is then combined with that of the nonlinear model, i.e., the pentad-mean zonal-mean height produced by the nonlinear model is transformed to its counterpart in the numericalmodel by nudging during the time integration. The forecast experiment results show that the above hybridapproach not only reduces the systematic error in zonal mean height by the numerical model, but alsomakes an improvement in the non-axisymmetric components due to the wave-flow interaction.

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