Energy and carbon footprint considerations for data‐driven weather forecasting models

Abstract Data‐driven models for weather forecasting display impressive computational speed‐up. However, these gains do not include the training phase of the models. This study gathers information from the literature about training and inference to estimate their energy and carbon footprints. Despite being considerably more costly than a physics‐based forecast, the training is rapidly compensated by the savings made during inferences. For a use‐case corresponding to a one‐year usage, data‐driven models are estimated to consume 21 to 1273 times less energy than the physics‐based model. Consequently, for low‐resolution forecasts, data‐driven models bring opportunities to significantly reduce the carbon footprint of weather forecasting.