利用联合状态进行封闭区域资料同化
Ensemble regional data assimilation using joint states
摘要：
我们提出了一个数据同化方案，同时产生全球和嵌入式有限区域模式的分析，考虑两个模型的预报信息。所提议的办法有两个目的。首先，我们希望全球分析将受益于从更高分辨率的有限区域模型的信息纳入。其次，我们的方法预计将产生一个有限的区域分析，更强烈地限制了大规模的流动比传统的有限区域分析。该方案最小化的成本函数，其中的控制变量是联合状态的全球和有限区域模型。此外，成本函数包括惩罚全局和有限区域状态估计之间的大差异的约束项。所提出的方法进行了测试理想化的实验，使用“玩具”模型介绍了洛伦兹在2005年。这些实验的结果表明，所提出的方法提高了有限区域内和附近的全球分析和区域分析的横向边界附近。这些分析改进导致全球和有限区域模型的预测改进。
英文摘要：
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.
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