Reduced basis approximation and a~posteriori error bounds for 4D-Var data assimilation

TL;DR

This paper introduces a certified reduced basis method for 4D-Var data assimilation, providing efficient approximations and reliable error bounds for large-scale PDE-based inverse problems involving model and initial condition estimation.

Contribution

The paper develops a novel a posteriori error bound framework for reduced basis approximations in 4D-Var data assimilation, applicable to both strong- and weak-constraint formulations.

Findings

Validated the error bounds through numerical experiments.

Achieved significant computational efficiency improvements.

Demonstrated applicability to large-scale PDE models.

Abstract

We propose a certified reduced basis approach for the strong- and weak-constraint four-dimensional variational (4D-Var) data assimilation problem for a parametrized PDE model. While the standard strong-constraint 4D-Var approach uses the given observational data to estimate only the unknown initial condition of the model, the weak-constraint 4D-Var formulation additionally provides an estimate for the model error and thus can deal with imperfect models. Since the model error is a distributed function in both space and time, the 4D-Var formulation leads to a large-scale optimization problem for every given parameter instance of the PDE model. To solve the problem efficiently, various reduced order approaches have therefore been proposed in the recent past. Here, we employ the reduced basis method to generate reduced order approximations for the state, adjoint, initial condition, and model error. Our main contribution is the development of efficiently computable \textit{a~posteriori} upper bounds for the error of the reduced basis approximation with respect to the underlying high-dimensional 4D-Var problem. Numerical results are conducted to test the validity of our approach.