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Explore how artificial intelligence and accelerated computing are revolutionizing weather prediction systems in this 49-minute conference talk. Learn about cutting-edge approaches that significantly reduce computational time while maintaining or improving forecast accuracy compared to traditional physics-based models. Discover the implementation of machine learning algorithms, including deep learning architectures like Transformers and Graph Neural Networks, that are being adopted by national meteorological agencies for operational weather forecasting. Understand how AI-driven models such as FourCastNet, DLWP, Pangu, and GraphCast are transforming the field by processing vast amounts of atmospheric data more efficiently than conventional numerical weather prediction systems. Gain insights into the technical challenges of scaling weather prediction models, the role of GPU acceleration in processing complex meteorological datasets, and the practical benefits of hybrid approaches that combine physics-based understanding with data-driven methodologies. Examine real-world applications where these advanced computational techniques are already improving forecast reliability and extending prediction horizons while reducing the computational resources required for global weather modeling.
