Multiscale data assimilation in turbulent models

We explore the potential of data assimilation (DA) within the multiscale framework of a shell model of turbulence, with a focus on the ensemble Kalman filter (EnKF). The central objective is to understand how measuring mesoscales (i.e., inertial-range scales) enhances the prediction of both large-scale and small-scale intermittent variables, by systematically varying observation frequency and the set of measured scales. We demonstrate that measurements conducted at frequencies that exceed those of the observed scales enable full synchronization of larger scales, provided that at least two adjacent mesoscales are measured. In addition, we benchmark the EnKF against two other DA methods, namely, nudging and ensemble four-dimensional variational method. EnKF is clearly superior to the former and comparable with the latter but achieving the result with a lower computational complexity. Moreover, our results underscore the need for a tailored, scale-aware inflation technique to stabilize the assimilation process, preventing filter divergence and ensuring robust convergence.