The Back and Forth Nudging algorithm for data assimilation problems: theoretical results on transport equations

TL;DR

This paper provides theoretical analysis of the back and forth nudging algorithm for data assimilation in 1D transport equations, establishing convergence conditions and rates for various cases.

Contribution

It offers the first theoretical convergence results for the back and forth nudging algorithm applied to 1D transport equations, including viscous and inviscid cases.

Findings

Convergence holds under observability conditions for non viscous equations.

Exponential convergence rate in time is established.

Convergence for viscous linear transport equations under strong hypotheses.

Abstract

In this paper, we consider the back and forth nudging algorithm that has been introduced for data assimilation purposes. It consists of iteratively and alternately solving forward and backward in time the model equation, with a feedback term to the observations. We consider the case of 1-dimensional transport equations, either viscous or inviscid, linear or not (B\"urgers' equation). Our aim is to prove some theoretical results on the convergence, and convergence properties, of this algorithm. We show that for non viscous equations (both linear transport and Burgers), the convergence of the algorithm holds under observability conditions. Convergence can also be proven for viscous linear transport equations under some strong hypothesis, but not for viscous Burgers' equation. Moreover, the convergence rate is always exponential in time. We also notice that the forward and backward system of equations is well posed when no nudging term is considered.