Bibliographic Details
| Title: |
FourCastNet: Accelerating Global High-Resolution Weather Forecasting using Adaptive Fourier Neural Operators |
| Authors: |
Kurth, Thorsten, Subramanian, Shashank, Harrington, Peter, Pathak, Jaideep, Mardani, Morteza, Hall, David, Miele, Andrea, Kashinath, Karthik, Anandkumar, Animashree |
| Publication Year: |
2022 |
| Collection: |
Computer Science
Physics (Other) |
| Subject Terms: |
Physics - Atmospheric and Oceanic Physics, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Computer Science - Performance |
| More Details: |
Extreme weather amplified by climate change is causing increasingly devastating impacts across the globe. The current use of physics-based numerical weather prediction (NWP) limits accuracy due to high computational cost and strict time-to-solution limits. We report that a data-driven deep learning Earth system emulator, FourCastNet, can predict global weather and generate medium-range forecasts five orders-of-magnitude faster than NWP while approaching state-of-the-art accuracy. FourCast-Net is optimized and scales efficiently on three supercomputing systems: Selene, Perlmutter, and JUWELS Booster up to 3,808 NVIDIA A100 GPUs, attaining 140.8 petaFLOPS in mixed precision (11.9%of peak at that scale). The time-to-solution for training FourCastNet measured on JUWELS Booster on 3,072GPUs is 67.4minutes, resulting in an 80,000times faster time-to-solution relative to state-of-the-art NWP, in inference. FourCastNet produces accurate instantaneous weather predictions for a week in advance, enables enormous ensembles that better capture weather extremes, and supports higher global forecast resolutions. |
| Document Type: |
Working Paper |
| Access URL: |
http://arxiv.org/abs/2208.05419 |
| Accession Number: |
edsarx.2208.05419 |
| Database: |
arXiv |
