Mechanisms and predictability of the dominant atmospheric variability : An approach using a hierarchical modeling

我们开发了一组具有不同复杂程度的大气模型。它们被用来合作，以检查主导环流变率的机制，并澄清，例如，强迫过程的夏季遥相关模式。我们还开发了一个实验预报系统的集合卡尔曼滤波的基础上。通过扩展系统，使模型参数被同化和扰动，我们显示了1个月预测技能的提高，这是由于模型误差的减少。

We developed a group of atmospheric models having different levels of complexity. They are used cooperatively to examine the mechanisms of dominant circulation variability and clarified, for example, forcing processes for the summertime teleconnection patterns. We also developed an experimental forecast system based on the ensemble Kalman filter. By extending the system such that the model's parameters are assimilated and perturbed, we showed an improvement in the 1-mo forecast skill, which is due to the reduction of the model error.