Computing the ensemble spread from deterministic weather predictions using conditional generative adversarial networks
This work addresses computational efficiency for weather forecasting agencies, though it is incremental as it adapts existing methods to a specific domain.
The paper tackles the high computational cost of ensemble weather prediction systems by using deep learning to predict ensemble spread from a single deterministic forecast, achieving highly accurate results for 500 hPa geopotential height.
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.