Postprocessing Galerkin method applied to a data assimilation algorithm: a uniform in time error estimate
Provides theoretical error guarantees for a data assimilation method in fluid dynamics, relevant for numerical weather prediction and climate modeling.
The authors apply the Postprocessing Galerkin method to a continuous data assimilation algorithm for the 2D Navier-Stokes equations, obtaining uniform-in-time error estimates between the numerical approximation and the reference solution under conditions on nudging parameter, coarse mesh resolution, and numerical scheme resolution.
We apply the Postprocessing Galerkin method to a recently introduced continuous data assimilation (downscaling) algorithm for obtaining a numerical approximation of the solution of the two-dimensional Navier-Stokes equations corresponding to given measurements from a coarse spatial mesh. Under suitable conditions on the relaxation (nudging) parameter, the resolution of the coarse spatial mesh and the resolution of the numerical scheme, we obtain uniform in time estimates for the error between the numerical approximation given by the Postprocessing Galerkin method and the reference solution corresponding to the measurements. Our results are valid for a large class of interpolant operators, including low Fourier modes and local averages over finite volume elements. Notably, we use here the 2D Navier-Stokes equations as a paradigm, but our results apply equally to other evolution equations, such as the Boussinesq system of Benard convection and other oceanic and atmospheric circulation models.