mlsanityData to 14 Oct 2026

FourCastNet: Accelerating Global High-Resolution Weather Forecasting using Adaptive Fourier Neural Operators

arXiv 2208.05419 ↗First posted 8 Aug 2022physics.ao-ph cs.AI cs.CVcited by 188

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Abstract (arXiv)

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.

Key facts. Cited by 188 papers in the map (data through 14 Oct 2026). Builds on 13 of its 31 references that are in the map. First posted on arXiv 8 Aug 2022.

Builds on

13 of 31 references.

References · 13

  1. 2017Attention Is All You Needcited by 60,361
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  3. 2020Rethinking Attention with Performerscited by 1,601
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  5. 2020Linformer: Self-Attention with Linear Complexitycited by 1,474
  6. 2021Transformers in Vision: A Surveycited by 1,013
  7. 2022FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operatorscited by 776
  8. 2021Scaling Vision with Sparse Mixture of Expertscited by 747
  9. 2021Physics-Informed Neural Operator for Learning Partial Differential Equationscited by 527
  10. 2021Neural Operator: Learning Maps Between Function Spacescited by 414
  11. 2021Adaptive Fourier Neural Operators: Efficient Token Mixers for Transformerscited by 248
  12. 2022Forecasting Global Weather with Graph Neural Networkscited by 168
1 more
  1. 2021Sub-seasonal forecasting with a large ensemble of deep-learning weather prediction modelscited by 79

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 3; 2023: 28; 2024: 44; 2025: 59; 2026: 54.

188 papers.

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  1. 2022GraphCast: Learning skillful medium-range global weather forecastingcited by 267
  2. 2023Neural Operators for Accelerating Scientific Simulations and Designcited by 221
  3. 2023GenCast: Diffusion-based ensemble forecasting for medium-range weathercited by 177
  4. 2023FengWu: Pushing the Skillful Global Medium-range Weather Forecast beyond 10 Days Leadcited by 175
  5. 2023A Mathematical Guide to Operator Learningcited by 76
  6. 2023Machine learning with data assimilation and uncertainty quantification for dynamical systems: a reviewcited by 75
  7. 2023Deep Learning for Day Forecasts from Sparse Observationscited by 72
  8. 2025FourCastNet 3: A geometric approach to probabilistic machine-learning weather forecasting at scalecited by 62
  9. 2023Out-of-distributional risk bounds for neural operators with applications to the Helmholtz equationcited by 32
  10. 2023On the training and generalization of deep operator networkscited by 31
  11. 2024FengWu-GHR: Learning the Kilometer-scale Medium-range Global Weather Forecastingcited by 30
  12. 2024LUCIE: A Lightweight Uncoupled ClImate Emulator with long-term stability and physical consistency for O(1000)-member ensemblescited by 30
38 more
  1. 2024Continuous Ensemble Weather Forecasting with Diffusion modelscited by 30
  2. 2023Advancing Parsimonious Deep Learning Weather Prediction using the HEALPix Meshcited by 28
  3. 2025Fixing the Double Penalty in Data-Driven Weather Forecasting Through a Modified Spherical Harmonic Loss Functioncited by 28
  4. 2023HyperDeepONet: learning operator with complex target function space using the limited resources via hypernetworkcited by 26
  5. 2024Calibrated Uncertainty Quantification for Operator Learning via Conformal Predictioncited by 26
  6. 2024Can AI weather models predict out-of-distribution gray swan tropical cyclones?cited by 26
  7. 2023Rethinking materials simulations: Blending direct numerical simulations with neural operatorscited by 25
  8. 2023Dynamical Tests of a Deep-Learning Weather Prediction Modelcited by 24
  9. 2024Application-Driven Innovation in Machine Learningcited by 24
  10. 2024Generalizing Weather Forecast to Fine-grained Temporal Scales via Physics-AI Hybrid Modelingcited by 23
  11. 2024An operator learning perspective on parameter-to-observable mapscited by 22
  12. 2024CRA5: Extreme Compression of ERA5 for Portable Global Climate and Weather Research via an Efficient Variational Transformercited by 22
  13. 2023AI Foundation Models for Weather and Climate: Applications, Design, and Implementationcited by 21
  14. 2025Thermalizer: Stable autoregressive neural emulation of spatiotemporal chaoscited by 21
  15. 2023HEAL-SWIN: A Vision Transformer On The Spherecited by 20
  16. 2024ExtremeCast: Boosting Extreme Value Prediction for Global Weather Forecastcited by 19
  17. 2023Neural Operators of Backstepping Controller and Observer Gain Functions for Reaction-Diffusion PDEscited by 18
  18. 2024Huge Ensembles Part I: Design of Ensemble Weather Forecasts using Spherical Fourier Neural Operatorscited by 18
  19. 2025Probabilistic neural operators for functional uncertainty quantificationcited by 18
  20. 2025The Price of Freedom: Exploring Expressivity and Runtime Tradeoffs in Equivariant Tensor Productscited by 15
  21. 2023Neural Spectral Methods: Self-supervised learning in the spectral domaincited by 14
  22. 2023When Geoscience Meets Foundation Models: Towards General Geoscience Artificial Intelligence Systemcited by 13
  23. 2025Elucidated Rolling Diffusion Models for Probabilistic Forecasting of Complex Dynamicscited by 13
  24. 2024MambaDS: Near-Surface Meteorological Field Downscaling with Topography Constrained Selective State Space Modelingcited by 12
  25. 2025Principled Approaches for Extending Neural Architectures to Function Spaces for Operator Learningcited by 12
  26. 2026U-Cast: A Surprisingly Simple and Efficient Frontier Probabilistic AI Weather Forecastercited by 12
  27. 2023Monte Carlo Neural PDE Solver for Learning PDEs via Probabilistic Representationcited by 11
  28. 2024Uncertainty Quantification of Surrogate Models using Conformal Predictioncited by 11
  29. 2024GFN: A graph feedforward network for resolution-invariant reduced operator learning in multifidelity applicationscited by 10
  30. 2024Accelerating Phase Field Simulations Through a Hybrid Adaptive Fourier Neural Operator with U-Net Backbonecited by 10
  31. 2025Latent Mamba Operator for Partial Differential Equationscited by 10
  32. 2025Zephyrus: An Agentic Framework for Weather Sciencecited by 10
  33. 2026Decoupled Diffusion Sampling for Inverse Problems on Function Spacescited by 10
  34. 2023KoopmanLab: machine learning for solving complex physics equationscited by 9
  35. 2025Physics-Informed Latent Neural Operator for Real-time Predictions of time-dependent parametric PDEscited by 9
  36. 2024EWMoE: An effective model for global weather forecasting with mixture-of-expertscited by 8
  37. 2024State-observation augmented diffusion model for nonlinear assimilation with unknown dynamicscited by 8
  38. 2025TANTE: Time-Adaptive Operator Learning via Neural Taylor Expansioncited by 8

50 most cited of 188 All 188 (JSON)