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.

翻译：在数据同化方面,当观测操作员未知时,国家估计并非直截了当。本研究提出了在真正的操作员未知时组成替代操作员的方法。神经网络被用来迭接改进代用模型,以减少观测与代用模型结果之间的差别。一个双重实验表明,拟议的方法优于在数据同化过程中暂时使用特定操作员的方法。