A twin-experiment is carried out introducing elements of an Ensemble Kalman Filter (EnKF), to assess and correct ocean uncertainties in a high-resolution Bay of Biscay configuration. Initially, an ensemble of 102 members is performed by applying stochastic modeling of the wind forcing. The target of this step is to simulate the envelope of possible realizations and to explore the robustness of the method at building ensemble covariances. Our second step includes the integration of the ensemble-based error estimates into a data assimilative system adopting a 4D Ensemble Optimal Interpolation (4DEnOI) approach. In the twin-experiment context, synthetic observations are simulated from a perturbed member not used in the subsequent analyses, satisfying the condition of an unbiased probability distribution function against the ensemble by performing a rank histogram. We evaluate the assimilation performance on short-term predictability focusing on the ensemble size, the observational network, and the enrichment of the ensemble by inexpensive time-lagged techniques. The results show that variations in performance are linked to intrinsic oceanic processes, such as the spring shoaling of the thermocline, in combination with external forcing modulated by river runoffs and time-variable wind patterns, constantly reshaping the error regimes. Ensemble covariances are able to capture high-frequency processes associated with coastal density fronts, slope currents and upwelling events near the Armorican and Galician shelf break. Further improvement is gained when enriching model covariances by including pattern phase errors, with the help of time-neighbor states augmenting the ensemble spread.