Paper ID: 2201.12514

Composing a surrogate observation operator for sequential data assimilation

Kosuke Akita, Yuto Miyatake, Daisuke Furihata

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.

Submitted: Jan 29, 2022