By Marc Bocquet
Artificial intelligence, and particularly deep learning, revolutionised numerical weather prediction (NWP) in 2023. Several teams from giant tech companies have proposed surrogate models for high-resolution global atmospheric dynamics. These models achieve the performance levels of the deterministic IFS of the European Centre for Medium-Range Weather Forecasts, as well as its ensemble prediction variant. In this presentation, I will discuss the techniques used to construct these models, their scope and limitations, and illustrate the concepts with our own models and results, in NWP and sea-ice models for climate. I will also discuss the integration of such surrogate models with data assimilation for the improvement of NWP, as well as some more fundamental issues related to the end-to-end approaches to data assimilation.
By Laurent Dubus
By Riwal Plougonven
By Riwal Plougonven
By Bastien Cozian
By Amaury Lancelin
By Frédéric Wurtz
By Sylvain Cros
By Georges Kariniotakis
By Marc Bocquet
By Philippe Quirion
By Alicia Bassière
By Benteng Zou
By Anna Creti
By Freddy Bouchet
By Greta Cazzaniga
By Olivier Vidal
