  An online Data Assimilation strategy based on the Ensemble Kalman Filter
(EnKF) is used to improve the predictive capabilities of Large Eddy Simulation
(LES) for the analysis of the turbulent flow in a plane channel, $Re_\tau
\approx 550$. The algorithm sequentially combines the LES prediction with
high-fidelity, sparse instantaneous data obtained from a Direct Numerical
Simulation (DNS). It is shown that the procedure provides an augmented state
which exhibits higher accuracy than the LES model and it synchronizes with the
time evolution of the high-fidelity DNS data if the hyperparameters governing
the EnKF are properly chosen. In addition, the data-driven algorithm is able to
improve the accuracy of the subgrid-scale model included in the LES, the
Smagorinsky model, via the optimization of a free coefficient. However, while
the online EnKF strategy is able to reduce the global error of the LES
prediction, a discrepancy with the reference DNS data is still observed because
of structural flaws of the subgrid-scale model used.
