🎙️ Episode 23: FourCastNet – Accelerating Global High-Resolution Weather Forecasting Using Adaptive Fourier Neural Operators
🔗 DOI: https://doi.org/10.1145/3592979.3593412
🌍 Abstract
As climate change intensifies extreme weather events, traditional numerical weather prediction (NWP) struggles to keep pace due to computational limits. This episode explores FourCastNet, a deep learning Earth system emulator that delivers high-resolution, medium-range global forecasts at unprecedented speed—up to five orders of magnitude faster than NWP—while maintaining near state-of-the-art accuracy.
📌 Bullet points summary
FourCastNet outpaces traditional NWP with forecasts that are not only faster by several magnitudes but also comparably accurate, thanks to its data-driven deep learning approach.
Powered by Adaptive Fourier Neural Operators (AFNO), the model efficiently handles high-resolution data, leveraging spectral convolutions, model/data parallelism, and performance optimizations like CUDA graphs and JIT compilation.
Scales excellently across supercomputers such as Selene, Perlmutter, and JUWELS Booster, reaching 140.8 petaFLOPS and enabling rapid training and large-scale ensemble forecasts.
Addresses long-standing challenges in weather and climate modeling, including limits in resolution, complexity, and throughput, paving the way for emulating fine-scale Earth system processes.
Enables "Interactivity at Scale"—supporting digital Earth twins and empowering users to explore future climate scenarios interactively, aiding science, policy, and public understanding.
💡 The Big Idea
FourCastNet revolutionizes weather forecasting by merging the power of deep learning and spectral methods, unlocking interactive, ultra-fast, and high-fidelity Earth system simulations for a changing world.
📖 Citation
Kurth, Thorsten, et al. "Fourcastnet: Accelerating global high-resolution weather forecasting using adaptive fourier neural operators." Proceedings of the Platform for Advanced Scientific Computing Conference. 2023.
