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.
@article{arxiv.2201.12514,
title = {Composing a surrogate observation operator for sequential data assimilation},
author = {Kosuke Akita and Yuto Miyatake and Daisuke Furihata},
journal= {arXiv preprint arXiv:2201.12514},
year = {2022}
}