标题： 带有模型误差的数据同化 显示英文标题

标题： Data assimilation with model errors

摘要： 校正是一种易于分析和实现的数据同化方法。 它还具有（据报道）与其他同化方法相比对模型误差不敏感的优势。 然而，在存在模型误差的情况下，校正行为很少被分析。 本报告对校正以纠正模型误差进行了分析。 分析表明，由于模型误差引起的误差随着校正参数$\chi \to \infty$按照$\mathcal{O}(\chi^{-\frac{1}{2}})$的方式衰减，定理 3.2。 数值测试验证了预测的收敛速率，并验证了校正对模型误差的有效性。 显示英文摘要

摘要： Nudging is a data assimilation method amenable to both analysis and implementation. It also has the (reported) advantage of being insensitive to model errors compared to other assimilation methods. However, nudging behavior in the presence of model errors is little analyzed. This report gives an analysis of nudging to correct model errors. The analysis indicates that the error contribution due to the model error decays as the nudging parameter $\chi \to \infty$ like $\mathcal{O}(\chi^{-\frac{1}{2}})$, Theorem 3.2. Numerical tests verify the predicted convergence rates and validate the nudging correction to model errors.