标题： 基于联合状态的集合区域数据同化 显示英文标题

标题： Ensemble regional data assimilation using joint states

摘要： 我们提出一种数据同化方案，同时生成全球模型和嵌入式有限区域模型的分析，考虑来自两个模型的预报信息。 所提出方法的目的有两个。 首先，我们期望全球分析将从包含高分辨率有限区域模型的信息中受益。 其次，我们的方法预期生成的有限区域分析将比传统的有限区域分析更受大尺度流的约束。 所提出的方案在成本函数中进行最小化，其中控制变量是全球模型和有限区域模型的联合状态。 此外，成本函数包括一个约束项，该约束项对全球模型和有限区域模型状态估计之间的较大差异进行惩罚。 所提出的方法通过理想化实验进行了测试，使用了Lorenz在2005年引入的“玩具”模型。 这些实验的结果表明，所提出的方法改善了有限区域域内和附近的全球分析以及侧边界附近的区域分析。 这些分析改进导致了全球模型和有限区域模型的预报改进。 显示英文摘要

摘要： We propose a data assimilation scheme that produces the analyses for a global and an embedded limited area model simultaneously, considering forecast information from both models. The purpose of the proposed approach is twofold. First, we expect that the global analysis will benefit from incorporation of information from the higher resolution limited area model. Second, our method is expected to produce a limited area analysis that is more strongly constrained by the large scale flow than a conventional limited area analysis. The proposed scheme minimizes a cost function in which the control variable is the joint state of the global and the limited area models. In addition, the cost function includes a constraint term that penalizes large differences between the global and the limited area state estimates. The proposed approach is tested by idealized experiments, using `toy' models introduced by Lorenz in 2005. The results of these experiments suggest that the proposed approach improves the global analysis within and near the limited area domain and the regional analysis near the lateral boundaries. These analysis improvements lead to forecast improvements in both the global and the limited area models.