Krylov Methods for Adjoint-Free Singular Vector Based Perturbations in Dynamical Systems

TL;DR

This paper introduces a matrix-free Krylov method using Arnoldi iterations to efficiently generate fast-growing perturbations for uncertainty estimation in high-dimensional dynamical systems, avoiding the need for linear or adjoint models.

Contribution

It presents a novel, efficient approach for approximating singular vectors in large systems using the Evolved Increment Matrix and Arnoldi method, bypassing explicit eigenvalue computations.

Findings

Effective approximation of fastest growing perturbations in Lorenz96 model.

Good performance with few Arnoldi iterations in shallow water model.

Avoids linear and adjoint models, reducing computational complexity.

Abstract

The estimation of weather forecast uncertainty with ensemble systems requires a careful selection of perturbations to establish a reliable sampling of the error growth potential in the phase space of the model. Usually, the singular vectors of the tangent linear model propagator are used to identify the fastest growing modes (classical singular vector perturbation (SV) method). In this paper we present an efficient matrix-free block Krylov method for generating fast growing perturbations in high dimensional dynamical systems. A specific matrix containing the non-linear evolution of perturbations is introduced, which we call Evolved Increment Matrix (EIM). Instead of solving an equivalent eigenvalue problem, we use the Arnoldi method for a direct approximation of the leading singular vectors of this matrix, which however is never computed explicitly. This avoids linear and adjoint models but requires forecasts with the full non-linear system. The performance of the approximated perturbations is compared with singular vectors of a full EIM (not with the classical SV method). We show promising results for the Lorenz96 differential equations and a shallow water model, where we obtain good approximations of the fastest growing perturbations by using only a small number of Arnoldi iterations.