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纬向平均环流预报的系统性误差及其改进

Systematic Errors of Zonal-Mean Flow in Dynamical Monthly Prediction and Its Improvement
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摘要 An analysis of a large number of cases of 500 hPa height monthly prediction shows that systematic errors exist in the zonal mean components which account for a large portion of the total forecast errors, and such errors are commonly seen in other prediction models. To overcome the difficulties of the numerical model, 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-mean geopotential height (wave number 0) based on a large amount of data is constituted by employing the reconstruction of phase-space theory and the spatio-temporal series predictive method. The dynamical prediction of the numerical model is then combined with that of the nonlinear model, i.e., the pentadmean zonal-mean height produced by the nonlinear model is transformed to its counterpart in the numerical model by nudging during the time integration. The forecast experiment results show that the above hybrid approach not only reduces the systematic error in zonal mean height by the numerical model, but also makes an improvement in the non-axisymmetric components due to the wave-flow interaction. An analysis of a large number of cases of 500 hPa height monthly prediction shows that systematic errors exist in the zonal mean components which account for a large portion of the total forecast errors, and such errors are commonly seen in other prediction models. To overcome the difficulties of the numerical model, 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-mean geopotential height (wave number 0) based on a large amount of data is constituted by employing the reconstruction of phase-space theory and the spatio-temporal series predictive method. The dynamical prediction of the numerical model is then combined with that of the nonlinear model, i.e., the pentadmean zonal-mean height produced by the nonlinear model is transformed to its counterpart in the numerical model by nudging during the time integration. The forecast experiment results show that the above hybrid approach not only reduces the systematic error in zonal mean height by the numerical model, but also makes an improvement in the non-axisymmetric components due to the wave-flow interaction.
机构地区 LASG LASG
出处 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2003年第1期17-27,共11页 大气科学进展(英文版)
基金 The study was financed by theNational Key Project for Development of Science and Tech-nology(96-908-02),by the National Natural Science Foun-dation of China under Grant No.40175013,and partly bythe Project of the Chinese Academy of Sciences (ZKC)
关键词 dynamical extended-range prediction zonal-mean component nonlinear regional prediction nudging dynamical extended-range prediction zonal-mean component nonlinear regional prediction nudging
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