Focusing on common and significant forecast errorsthe zonal mean errors in the numerical prediction model, this report proposes an approach to improving the dynamical extended-range (monthly) prediction. Firstly, the ...Focusing on common and significant forecast errorsthe zonal mean errors in the numerical prediction model, this report proposes an approach to improving the dynamical extended-range (monthly) prediction. Firstly, the monthly pentad-mean nonlinear dynamical regional predic-tion model of the zonal-mean height based on a large num-ber of historical data is constituted by employing the recon-struction phase space theory and the spatio-temporal series predictive method. The zonal height thus produced is trans-formed to its counterpart in the numerical model and fur-ther used to revise the numerical model prediction during the integration process. In this way, the two different kinds of prediction are combined. The forecasting experimenal results show that the above hybrid approach not only re-duces the systematical error of the numerical model, but also improves the forecast of the non-axisymmetric components due to the wave-flow interaction.展开更多
基金supported by the National Key Project for Development of Science and Technology(Grant No.96-908-02)by the National Natural Science Foundation of China(Grant No.40175013)partly by the Project of Chinese Academy of Sciences(Grant No.ZKCX2-SW-210).
文摘Focusing on common and significant forecast errorsthe zonal mean errors in the numerical prediction model, this report proposes an approach to improving the dynamical extended-range (monthly) prediction. Firstly, the monthly pentad-mean nonlinear dynamical regional predic-tion model of the zonal-mean height based on a large num-ber of historical data is constituted by employing the recon-struction phase space theory and the spatio-temporal series predictive method. The zonal height thus produced is trans-formed to its counterpart in the numerical model and fur-ther used to revise the numerical model prediction during the integration process. In this way, the two different kinds of prediction are combined. The forecasting experimenal results show that the above hybrid approach not only re-duces the systematical error of the numerical model, but also improves the forecast of the non-axisymmetric components due to the wave-flow interaction.
基金Project supported by the National Natural Science Foundation of China （Nos. 51707082 and 51607080）, the Natural Science Foundation of Jiangsu Province, China （Nos. BK20170546 and BK20150510）, the China Postdoctoral Science Foundation （No. 2017M620192）, and the Priority Academic Program Development of Jiangsu Higher Education Institutions
