  Ensemble weather forecasts enable a measure of uncertainty to be attached to
each forecast, by computing the ensemble's spread. However, generating an
ensemble with a good spread-error relationship is far from trivial, and a wide
range of approaches to achieve this have been explored -- chiefly in the
context of numerical weather prediction models. Here, we aim to transform a
deterministic neural network weather forecasting system into an ensemble
forecasting system. We test four methods to generate the ensemble: random
initial perturbations, retraining of the neural network, use of random dropout
in the network, and the creation of initial perturbations with singular vector
decomposition. The latter method is widely used in numerical weather prediction
models, but is yet to be tested on neural networks. The ensemble mean forecasts
obtained from these four approaches all beat the unperturbed neural network
forecasts, with the retraining method yielding the highest improvement.
However, the skill of the neural network forecasts is systematically lower than
that of state-of-the-art numerical weather prediction models.
