Computing the Ensemble Spread From Deterministic Weather Predictions Using Conditional Generative Adversarial Networks

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 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.

Automated summary

Ensemble prediction systems are valuable tools for weather forecasting, but they have high computational costs and require statistical post-processing. This study proposes using deep-learning-based algorithms to learn the statistical properties of an ensemble prediction system, specifically the ensemble spread, from a single deterministic forecast. The results show that the trained models can accurately predict the ensemble spread based on the control forecast.