Ensemble methods for neural network-based weather forecasts

Understand

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.