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

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.