中文摘要：

Based on the dynamic framework of Lorenz 96 model，the ensemble prediction system ( EPS) containing stochastic forcing has been developed. In this system, effects of stochastic forcing on the model climate state and ensemble mean prediction have been studied. The results show that the climate mean and standard deviation provided by a new computing paradigm by means of introduction of the proper stochastic forcing into numerical model integration process are closer to that of the true value than that made by the non-stochastic forcing. In other words, numerical model integration process with stochastic forcing has positive effect on the model climate state, and the effect is found to be positive mainly in the long lead time. Meanwhile, with respect to ensemble forecast effect yielded by white noise stochastic forcing, most results are better than those provided by no-stochastic forcing, and improvements pertaining to white noise stochastic forcing vary non-monotonically with the increase of the size of white noise. Moreover, the effects made by the identical white noise stochastic forcing also are different in various non-linear systems. With respect to EPS effect yielded by red noise stochastic forcing, most results are better than those provided by no-stochastic forcing, but only a part of ensemble forecast effect influenced by red noise is superior to that influenced by white noise. Finally，improvements pertaining to red noise stochastic forcing vary non-symmetrically and non-monotonically with the distribution of coefficient d). Besides, the selection of correlation coefficient cp is also dependent on non-linear models.