Degenerate Kalman filter error covariances and their convergence onto the unstable subspace

TL;DR

This paper proves that the Kalman filter's error covariance converges onto the unstable-neutral subspace of the model dynamics under linear, error-free conditions, with implications for data assimilation efficiency.

Contribution

It provides the first formal proof of covariance convergence onto the unstable-neutral subspace for linear, error-free models, including convergence rate and conditions.

Findings

Covariance matrix converges onto the unstable-neutral subspace.

Convergence rate is explicitly derived.

Numerical results support theoretical proof.

Abstract

The characteristics of the model dynamics are critical in the performance of (ensemble) Kalman filters. In particular, as emphasized in the seminal work of Anna Trevisan and co-authors, the error covariance matrix is asymptotically supported by the unstable-neutral subspace only, i.e., it is spanned by the backward Lyapunov vectors with non-negative exponents. This behavior is at the core of algorithms known as Assimilation in the Unstable Subspace, although a formal proof was still missing. This paper provides the analytical proof of the convergence of the Kalman filter covariance matrix onto the unstable-neutral subspace when the dynamics and the observation operator are linear and when the dynamical model is error-free, for any, possibly rank-deficient, initial error covariance matrix. The rate of convergence is provided as well. The derivation is based on an expression that explicitly relates the error covariances at an arbitrary time to the initial ones. It is also shown that if the unstable and neutral directions of the model are sufficiently observed and if the column space of the initial covariance matrix has a non-zero projection onto all of the forward Lyapunov vectors associated with the unstable and neutral directions of the dynamics, the covariance matrix of the Kalman filter collapses onto an asymptotic sequence which is independent of the initial covariances. Numerical results are also shown to illustrate and support the theoretical findings.