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FourCastNet high-resolution weather forecasting and distributed training

Contribution grade

AI weather forecasting · Human-designed research method or resource

Reference score155,000Provisional editorial estimate of this exact contribution; its grade is assessed separately.

What changed

Applies AFNO to autoregressive high-resolution global weather fields and develops feature/data-parallel GPU training with optimized spectral operations, I/O and launch overhead.

Scientific scope

Applies AFNO to autoregressive high-resolution global weather fields and develops feature/data-parallel GPU training with optimized spectral operations, I/O and launch overhead. Two 2022 sources bundled with separate 13/9-person bylines; SC account shares contribution among Subramanian/Harrington/Pathak, not automatically first author. Forecast speed is inference-only on specific hardware/variables; ERA5 reanalysis/training cost and fewer vertical variables matter. Distributed timings include exposed I/O/validation but exclude setup/warm-up/capture and extrapolate some 80-epoch costs from five epochs. No general climate or exact physical-simulation certificate.

Evidence

The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Two 2022 sources bundled with separate 13/9-person bylines; SC account shares contribution among Subramanian/Harrington/Pathak, not automatically first author. Forecast speed is inference-only on specific hardware/variables; ERA5 reanalysis/training cost and fewer vertical variables matter. Distributed timings include exposed I/O/validation but exclude setup/warm-up/capture and extrapolate some 80-epoch costs from five epochs. No general climate or exact physical-simulation certificate.

Contributors

Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.

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