  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.
