  We study the numerical performance of a continuous data assimilation
(downscaling) algorithm, based on ideas from feedback control theory, in the
context of the two-dimensional incompressible Navier--Stokes equations. Our
model problem is to recover an unknown reference solution, asymptotically in
time, by using continuous-in-time coarse-mesh nodal-point observational
measurements of the velocity field of this reference solution (subsampling), as
might be measured by an array of weather vane anemometers. Our calculations
show that the required nodal observation density is remarkably less that what
is suggested by the analytical study; and is in fact comparable to the {\it
number of numerically determining Fourier modes}, which was reported in an
earlier computational study by the authors. Thus, this method is
computationally efficient and performs far better than the analytical estimates
suggest.
