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Volume 20 Issue 5

Sep.  2003

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Article >  ADVANCES IN ATMOSPHERIC SCIENCES  > 2003 >  20(5): 837-841

A New Way to Predict Forecast Skill

  • 1.

    Department of Earth Sciences, Science College, University of Zhejiang, Hangzhou 310028,Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029,Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029


doi: 10.1007/BF02915409

  • Forecast skill (Anomaly Correlated Coefficient, ACC) is a quantity to show the forecast quality ofthe products of numerical weather forecasting models. Predicting forecast skill, which is the foundationof ensemble forecasting, means submitting products to predict their forecast quality before they are used.Checking the reason is to understand the predictability for the real cases. This kind of forecasting servicehas been put into operational use by statistical methods previously at the National Meteorological Center(NMC), USA (now called the National Center for Environmental Prediction (NCEP)) and European Centerfor Medium-range Weather Forecast (ECMWF). However, this kind of service is far from satisfactorybecause only a single variable is used with the statistical method. In this paper, a new way based onthe Grey Control Theory with multiple predictors to predict forecast skill of forecast products of theT42L9 of the NMC, China Meteorological Administration (CMA) is introduced. The results show: (1)The correlation coefficients between "forecasted" and real forecast skill range from 0.56 to 0.7 at differentseasons during the two-year period. (2) The grey forecasting model GM(1,8) forecasts successfully thehigh peaks, the increasing or decreasing tendency, and the turning points of the change of forecast skill ofcases from 5 January 1990 to 29 February 1992.
  • [1] 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
    [2] Zheng HE, Pangchi HSU, Xiangwen LIU, Tongwen WU, Yingxia GAO, 2019: Factors Limiting the Forecast Skill of the Boreal Summer Intraseasonal Oscillation in a Subseasonal-to-Seasonal Model, ADVANCES IN ATMOSPHERIC SCIENCES, 36, 104-118.  doi: 10.1007/s00376-018-7242-3
    [3] Jing Ma, Haiming Xu, Changming Dong, Jingjia Luo, 2023: Forecast Skills and Predictability Source of the Marine Heatwaves in the NUIST-CFS1.0 Hindcasts, ADVANCES IN ATMOSPHERIC SCIENCES.  doi: 10.1007/s00376-023-3139-x
    [4] Gill M. MARTIN, Nick J. DUNSTONE, Adam A. SCAIFE, Philip E. BETT, 2020: Predicting June Mean Rainfall in the Middle/Lower Yangtze River Basin, ADVANCES IN ATMOSPHERIC SCIENCES, 37, 29-41.  doi: 10.1007/s00376-019-9051-8
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    [7] Hui-Wen LAI, Fuqing ZHANG, Eugene E. CLOTHIAUX, David R. STAUFFER, Brian J. GAUDET, Johannes VERLINDE, Deliang CHEN, 2020: Modeling Arctic Boundary Layer Cloud Streets at Grey-zone Resolutions, ADVANCES IN ATMOSPHERIC SCIENCES, 37, 42-56.  doi: 10.1007/s00376-019-9105-y
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    [10] Daquan ZHANG, Lijuan CHEN, Gill M. MARTIN, Zongjian KE, 2023: Seasonal Prediction Skill and Biases in GloSea5 Relating to the East Asia Winter Monsoon, ADVANCES IN ATMOSPHERIC SCIENCES, 40, 2013-2028.  doi: 10.1007/s00376-023-2258-8
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    • Manuscript History

    Manuscript received: 10 September 2003
    Manuscript revised: 10 September 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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    A New Way to Predict Forecast Skill

    • 1. Department of Earth Sciences, Science College, University of Zhejiang, Hangzhou 310028,Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029,Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029
    • Manuscript received: 2003-09-10
    • Manuscript revised: 2003-09-10

    Abstract: Forecast skill (Anomaly Correlated Coefficient, ACC) is a quantity to show the forecast quality ofthe products of numerical weather forecasting models. Predicting forecast skill, which is the foundationof ensemble forecasting, means submitting products to predict their forecast quality before they are used.Checking the reason is to understand the predictability for the real cases. This kind of forecasting servicehas been put into operational use by statistical methods previously at the National Meteorological Center(NMC), USA (now called the National Center for Environmental Prediction (NCEP)) and European Centerfor Medium-range Weather Forecast (ECMWF). However, this kind of service is far from satisfactorybecause only a single variable is used with the statistical method. In this paper, a new way based onthe Grey Control Theory with multiple predictors to predict forecast skill of forecast products of theT42L9 of the NMC, China Meteorological Administration (CMA) is introduced. The results show: (1)The correlation coefficients between "forecasted" and real forecast skill range from 0.56 to 0.7 at differentseasons during the two-year period. (2) The grey forecasting model GM(1,8) forecasts successfully thehigh peaks, the increasing or decreasing tendency, and the turning points of the change of forecast skill ofcases from 5 January 1990 to 29 February 1992.

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