Stochastically perturbed bred vectors in multi-scale systems

TL;DR

This paper introduces a stochastic perturbation method for bred vectors in multi-scale systems, enhancing ensemble diversity and forecast accuracy while maintaining computational efficiency.

Contribution

It presents a novel stochastic perturbation approach for bred vectors tailored to multi-scale systems, improving ensemble diversity and forecast skill.

Findings

Enhanced forecast accuracy with stochastic perturbations.

More reliable ensembles demonstrated by error-spread and reliability metrics.

Numerical simulations show superiority over standard bred vectors.

Abstract

The breeding method is a computationally cheap way to generate flow-adapted ensembles to be used in probabilistic forecasts. Its main disadvantage is that the ensemble may lack diversity and collapse to a low-dimensional subspace. To still benefit from the breeding method's simplicity and its low computational cost, approaches are needed to increase the diversity of these bred vector (BV) ensembles. We present here such a method tailored for multi-scale systems. We describe how to judiciously introduce stochastic perturbations to the standard bred vectors leading to stochastically perturbed bred vectors. The increased diversity leads to a better forecast skill as measured by the RMS error, as well as to more reliable ensembles quantified by the error-spread relationship, the continuous ranked probability score and reliability diagrams. Our approach is dynamically informed and in effect generates random draws from the fast equilibrium measure conditioned on the slow variables. We illustrate the advantage of stochastically perturbed bred vectors over standard BVs in numerical simulations of a multi-scale Lorenz 96 model.