Artificial Intelligence, Data Assimilation, and Data-Driven Surrogate Models for Climate Prediction

Explore the revolutionary impact of artificial intelligence and deep learning on numerical weather prediction in this 48-minute lecture by Marc Bocquet from CEREA, École des Ponts and EDF R&D. Delve into the techniques used to construct surrogate models for high-resolution global atmospheric dynamics, examining their performance levels compared to deterministic IFS and ensemble prediction variants. Gain insights into the scope and limitations of these models through illustrations of NWP and sea-ice models for climate. Discover the integration of surrogate models with data assimilation for improving weather predictions, and consider fundamental issues related to end-to-end approaches in data assimilation. This Institut Henri Poincaré presentation offers a comprehensive overview of cutting-edge developments in climate modeling and artificial intelligence applications in meteorology.

Artificial intelligence, data assimilation, and data-driven surrogate models for the climate