Computing the ensemble spread from deterministic weather predictions using conditional generative adversarial networks. (arXiv:2205.09182v1 [cs.LG])

Ensemble prediction systems are an invaluable tool for weather forecasting.
Practically, ensemble predictions are obtained by running several perturbations
of the deterministic control forecast. However, ensemble prediction is
associated with a high computational cost and often involves statistical
post-processing steps to improve its quality. Here we propose to use
deep-learning-based algorithms to learn the statistical properties of an
ensemble prediction system, the ensemble spread, given only the deterministic
control forecast. Thus, once trained, the costly ensemble prediction system
will not …

arxiv computing ensemble generative adversarial networks networks predictions weather