Recent advancements in AI weather models have shown promising capabilities in weather prediction from short to medium-term (0 to 10 days lead time), comparable to physic-based state-of-the-art models. Notable examples include FourCastNet, SFNO, GraphCast, GenCast, Pangu-Weather, which have adopted a trend observed in other fields (, language models, vision models, multi-modal models): they leverage large architectures with an extensive pre-training on very large datasets. This type of large models (also called foundation models) facilitates the development of downstream applications.

In the context of weather forecasting, these AI models are typically trained on the gold standard global reanalysis dataset ERA-5, developed by the European Centre for Medium-Range Weather Forecasts. ERA-5 is widely used for weather and climate studies due to its high resolution, comprehensive coverage, and accurate representation of atmospheric conditions from 1979 to the present day. AI model...