论文标题
组成替代观测操作员以进行顺序数据同化
Composing a surrogate observation operator for sequential data assimilation
论文作者
论文摘要
在数据同化中，当观察算子未知时，状态估计并不是一件直接的。这项研究提出了一种在未知的真实操作员未知时组成替代操作员的方法。神经网络用于改善替代模型，以减少观测值和替代模型结果之间的差异。双胞胎实验表明，所提出的方法优于在整个数据同化过程中暂时使用特定操作员的方法。
In data assimilation, state estimation is not straightforward when the observation operator is unknown. This study proposes a method for composing a surrogate operator when the true operator is unknown. A neural network is used to improve the surrogate model iteratively to decrease the difference between the observations and the results of the surrogate model. A twin experiment suggests that the proposed method outperforms approaches that tentatively use a specific operator throughout the data assimilation process.
