Monthly Extended Predicting Experiments with Nonlinear Regional Prediction. Part I: Prediction of Zonal Mean Flow

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  • Funds:

    Supported by the National Natural Science Foundation of China under Grant No. 40175013, the National Key Project for Development of Science and Technology (96-908-02-01), and the Project of Chinese Academy of Sciences (ZKCX2-SW-210).

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  • Systematic errors have recently been founded to be distinct in the zonal mean component forecasts,which account for a large portion of the total monthly-mean forecast errors. To overcome the difficulty of numerical model, the monthly pentad-mean nonlinear dynamic regional prediction models of the zonal mean geopotential height at 200, 300, 500, and 700 hPa based on a large number of historical data (NCEP/NCAR reanalysis data) were constituted by employing the local approximation of the phase space reconstruction theory and nonlinear spatio-temporal series prediction method. The 12-month forecast experiments of 1996 indicated that the results of the nonlinear model are better than those of the persistent, climatic prediction,and T42L9 model either over the high- and mid-latitude areas of the Northern and Southern Hemispheres or the tropical area. The root-mean-square of the monthly-mean height of T42L9 model was considerably decreased with a change of 30.4%, 26.6%, 82.6%, and 39.4%, respectively, over the high- and mid-latitudes of the Northern Hemisphere, over the high- and mid-latitudes of the Southern Hemisphere, over the tropics and over the globe, and also the corresponding anomaly correlation coe cients over the four areas were respectively increased by 0.306-0.312, 0.304-0.429, 0.739-0.746, and 0.360-0.400 (averagely a relative change of 11.0% over the globe) by nonlinear correction after integration, implying that the forecasts given by nonlinear model include more useful information than those of T42L9 model.
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    沈阳化工大学材料科学与工程学院 沈阳 110142

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Monthly Extended Predicting Experiments with Nonlinear Regional Prediction. Part I: Prediction of Zonal Mean Flow

  • 1. Shanghai Climate Center,Shanghai Meteorological Bureau,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;
    LASG,Institute of Atmospheric Physics,Chinese Academy of Sciences,Beijing 100029
Funds: Supported by the National Natural Science Foundation of China under Grant No. 40175013, the National Key Project for Development of Science and Technology (96-908-02-01), and the Project of Chinese Academy of Sciences (ZKCX2-SW-210).

Abstract: Systematic errors have recently been founded to be distinct in the zonal mean component forecasts,which account for a large portion of the total monthly-mean forecast errors. To overcome the difficulty of numerical model, the monthly pentad-mean nonlinear dynamic regional prediction models of the zonal mean geopotential height at 200, 300, 500, and 700 hPa based on a large number of historical data (NCEP/NCAR reanalysis data) were constituted by employing the local approximation of the phase space reconstruction theory and nonlinear spatio-temporal series prediction method. The 12-month forecast experiments of 1996 indicated that the results of the nonlinear model are better than those of the persistent, climatic prediction,and T42L9 model either over the high- and mid-latitude areas of the Northern and Southern Hemispheres or the tropical area. The root-mean-square of the monthly-mean height of T42L9 model was considerably decreased with a change of 30.4%, 26.6%, 82.6%, and 39.4%, respectively, over the high- and mid-latitudes of the Northern Hemisphere, over the high- and mid-latitudes of the Southern Hemisphere, over the tropics and over the globe, and also the corresponding anomaly correlation coe cients over the four areas were respectively increased by 0.306-0.312, 0.304-0.429, 0.739-0.746, and 0.360-0.400 (averagely a relative change of 11.0% over the globe) by nonlinear correction after integration, implying that the forecasts given by nonlinear model include more useful information than those of T42L9 model.

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