  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.
