Deep Learning for Weather Prediction - Advances and Future Directions

Watch a recorded lecture from SPCL_Bcast where Martin Schultz explores the revolutionary advancements in machine learning models for weather prediction. Discover how large-scale deep learning models, trained on years of reanalysis data, are now surpassing traditional numerical models in both accuracy and prediction timeframes. Learn about the emerging capabilities of these AI models in ensemble forecasting and data assimilation, while also examining their current limitations in weather and climate applications. Gain insights into the state-of-the-art developments in this field and understand the future trajectory of AI-powered weather prediction systems through this comprehensive 70-minute presentation delivered at ETH Zurich's Scalable Parallel Computing Lab.