Public Evening Lecture “AI and HPC for Weather and Climate Prediction”

Recent advances in machine learning are fundamentally reshaping weather and climate prediction. Data-driven models such as FourCastNet, GenCast, AIFS, and other emerging generative approaches now rival or complement traditional numerical weather prediction (NWP) by delivering faster forecasts while capturing complex atmospheric dynamics. At the same time, initiatives such as WeatherGenerator and NVIDIA Earth-2 highlight a broader vision: building AI-powered digital twins of the Earth system that integrate weather, climate, and environmental processes at unprecedented scale.

This talk explores the intersection of these developments with high-performance computing (HPC). AI workloads are reshaping both software and hardware stacks on modern supercomputers: accelerator-centric architectures, mixed-precision training, distributed deep learning frameworks, and scalable I/O pipelines for massive geophysical datasets are becoming central design considerations. Conversely, HPC expertise remains critical for scaling training and inference, integrating hybrid AI–physics workflows, and enabling robust deployment.

We will discuss implementation aspects and practical challenges using FourCastNet3 and transformer-based models as concrete examples. In particular, we examine architectural trends, scaling behavior, and the impact of modern GPU features on scientific AI workloads.

Finally, we outline open challenges and future directions, including coupling AI models with observational data, extending methods toward climate timescales, and improving physical consistency and reliability. The convergence of AI and HPC is not only accelerating weather prediction but also redefining how Earth system science is conducted.