Abstract: A new paradigm for weather and climate prediction has emerged recently: data-driven prediction models have become competitive with standard physics-based models on many aspects, thanks to an accurate encoding of the data distribution. Most models have been developed for task-specific purposes and are trained on a single type of data (such as the ERA5 reanalysis). The next challenge to expand the capabilities of data-driven modeling is to fully exploit the vast range of atmospheric observations, characterized by spatio-temporal variations and heterogeneous outputs (point or spatial time series, vertical profiles, vertically integrated data, … ). This naturally leads to the development of foundation models that learn a task-agnostic representation of the atmosphere. An overview of the most advanced models will be presented, as well as early results for integrating heterogeneous data sources.
Bio: A graduate of the National School of Meteorology and holder of a PhD from the University of Toulouse 3, Laure Raynaud is a researcher in numerical weather prediction at the National Center for Meteorological Research (CNRM/MétéoFrance). Her research focuses mainly on data assimilation and probabilistic forecasting. She regularly collaborates with several applied fields (agriculture, energy, transportation). Her recent work explores artificial intelligence methods and their use in the computation and operational application of weather forecasts. She holds a chair at the Toulouse Artificial Intelligence Institute (ANITI).