  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 be needed anymore to obtain future ensemble forecasts, and the
statistical properties of the ensemble can be derived from a single
deterministic forecast. We adapt the classical pix2pix architecture to a
three-dimensional model and also experiment with a shared latent space
encoder-decoder model, and train them against several years of operational
(ensemble) weather forecasts for the 500 hPa geopotential height. The results
demonstrate that the trained models indeed allow obtaining a highly accurate
ensemble spread from the control forecast only.
