Data Assimilation using Time-Delay Nudging in the Presence of Gaussian Noise

We study a discrete-in-time data-assimilation algorithm based on nudging through a time-delayed feedback control in which the observational measurements have been contaminated by a Gaussian noise process. In the context of the two-dimensional incompressible Navier-Stokes equations we prove the expected value of the square-error between the approximating solution and the reference solution over time is proportional to the variance of the noise up to a logarithmic correction. The qualitative behavior and physical relevance of our analysis is further illustrated by numerical simulation.