  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.
