Data assimilation as a nonlinear dynamical systems problem: Stability and convergence of the prediction-assimilation system

TL;DR

This paper investigates the stability and convergence of prediction-assimilation systems in geosciences, emphasizing the importance of observational networks and assimilation methods for system stabilization and accurate long-term predictions.

Contribution

It provides a theoretical analysis of the stability and convergence of nonlinear prediction-assimilation systems, including spectral analysis of Lyapunov exponents for complex models.

Findings

Stability ensures uniqueness and convergence of solutions.

Data-induced stabilization depends on observational network and assimilation method.

Spectral analysis reveals the role of Lyapunov exponents in system stability.

Abstract

We study prediction-assimilation systems, which have become routine in meteorology and oceanography and are rapidly spreading to other areas of the geosciences and of continuum physics. The long-term, nonlinear stability of such a system leads to the uniqueness of its sequentially estimated solutions and is required for the convergence of these solutions to the system's true, chaotic evolution. The key ideas of our approach are illustrated for a linearized Lorenz system. Stability of two nonlinear prediction-assimilation systems from dynamic meteorology is studied next via the complete spectrum of their Lyapunov exponents; these two systems are governed by a large set of ordinary and of partial differential equations, respectively. The degree of data-induced stabilization is crucial for the performance of such a system. This degree, in turn, depends on two key ingredients: (i) the observational network, either fixed or data-adaptive; and (ii) the assimilation method.