mlsanityData to 7 Oct 2026

FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators

arXiv 2202.11214 ↗First posted 22 Feb 2022physics.ao-ph cs.LGcited by 748

Not on the map. Why?

Cited by 55 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

FourCastNet, short for Fourier Forecasting Neural Network, is a global data-driven weather forecasting model that provides accurate short to medium-range global predictions at 0.25circ resolution. FourCastNet accurately forecasts high-resolution, fast-timescale variables such as the surface wind speed, precipitation, and atmospheric water vapor. It has important implications for planning wind energy resources, predicting extreme weather events such as tropical cyclones, extra-tropical cyclones, and atmospheric rivers. FourCastNet matches the forecasting accuracy of the ECMWF Integrated Forecasting System (IFS), a state-of-the-art Numerical Weather Prediction (NWP) model, at short lead times for large-scale variables, while outperforming IFS for variables with complex fine-scale structure, including precipitation. FourCastNet generates a week-long forecast in less than 2 seconds, orders of magnitude faster than IFS. The speed of FourCastNet enables the creation of rapid and inexpensive large-ensemble forecasts with thousands of ensemble-members for improving probabilistic forecasting. We discuss how data-driven deep learning models such as FourCastNet are a valuable addition to the meteorology toolkit to aid and augment NWP models.

Key facts. Cited by 748 papers in the map (data through 7 Oct 2026). Builds on 11 of its 39 references that are in the map. First posted on arXiv 22 Feb 2022.

Builds on

11 of 39 references.

References · 11

  1. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  2. 2020Fourier Neural Operator for Parametric Partial Differential Equationscited by 2,806
  3. 2021Physics-Informed Neural Operator for Learning Partial Differential Equationscited by 516
  4. 2020WeatherBench: A benchmark dataset for data-driven weather forecastingcited by 272
  5. 2021Adaptive Fourier Neural Operators: Efficient Token Mixers for Transformerscited by 237
  6. 2020Improving data-driven global weather prediction using deep convolutional neural networks on a cubed spherecited by 95
  7. 2021Sub-seasonal forecasting with a large ensemble of deep-learning weather prediction modelscited by 76
  8. 2020Deep Learning for Post-Processing Ensemble Weather Forecastscited by 52
  9. 2021Will Artificial Intelligence supersede Earth System and Climate Models?cited by 37
  10. 2019Analog forecasting of extreme-causing weather patterns using deep learningcited by 33
  11. 2020Climbing down Charney's ladder: Machine Learning and the post-Dennard era of computational climate sciencecited by 18

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 44; 2023: 122; 2024: 205; 2025: 203; 2026: 174.

748 papers.

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  1. 2022Fourier Neural Operator with Learned Deformations for PDEs on General Geometriescited by 416
  2. 2023ClimaX: A foundation model for weather and climatecited by 325
  3. 2023Geometry-Informed Neural Operator for Large-Scale 3D PDEscited by 293
  4. 2022Transformer for Partial Differential Equations' Operator Learningcited by 270
  5. 2022GraphCast: Learning skillful medium-range global weather forecastingcited by 261
  6. 2023FuXi: A cascade machine learning forecasting system for 15-day global weather forecastcited by 237
  7. 2023WeatherBench 2: A benchmark for the next generation of data-driven global weather modelscited by 227
  8. 2023Spherical Fourier Neural Operators: Learning Stable Dynamics on the Spherecited by 225
  9. 2022U-NO: U-shaped Neural Operatorscited by 219
  10. 2023Neural Operators for Accelerating Scientific Simulations and Designcited by 218
  11. 2023Convolutional Neural Operators for robust and accurate learning of PDEscited by 207
  12. 2023PDE-Refiner: Achieving Accurate Long Rollouts with Neural PDE Solverscited by 205
38 more
  1. 2022Towards Multi-spatiotemporal-scale Generalized PDE Modelingcited by 190
  2. 2022FourCastNet: Accelerating Global High-Resolution Weather Forecasting using Adaptive Fourier Neural Operatorscited by 188
  3. 2023FengWu: Pushing the Skillful Global Medium-range Weather Forecast beyond 10 Days Leadcited by 170
  4. 2023GenCast: Diffusion-based ensemble forecasting for medium-range weathercited by 169
  5. 2022Pangu-Weather: A 3D High-Resolution Model for Fast and Accurate Global Weather Forecastcited by 164
  6. 2022Earthformer: Exploring Space-Time Transformers for Earth System Forecastingcited by 146
  7. 2024DPOT: Auto-Regressive Denoising Operator Transformer for Large-Scale PDE Pre-Trainingcited by 137
  8. 2023Towards Foundation Models for Scientific Machine Learning: Characterizing Scaling and Transfer Behaviorcited by 128
  9. 2023Scalable Transformer for PDE Surrogate Modelingcited by 123
  10. 2023An Expert's Guide to Training Physics-informed Neural Networkscited by 119
  11. 2023Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlookcited by 109
  12. 2023Scaling transformer neural networks for skillful and reliable medium-range weather forecastingcited by 106
  13. 2023DYffusion: A Dynamics-informed Diffusion Model for Spatiotemporal Forecastingcited by 105
  14. 2023Residual Corrective Diffusion Modeling for Km-scale Atmospheric Downscalingcited by 103
  15. 2023ForecastPFN: Synthetically-Trained Zero-Shot Forecastingcited by 102
  16. 2023PreDiff: Precipitation Nowcasting with Latent Diffusion Modelscited by 96
  17. 2024A Comprehensive Survey of Scientific Large Language Models and Their Applications in Scientific Discoverycited by 96
  18. 2022Clifford Neural Layers for PDE Modelingcited by 91
  19. 2023Multiple Physics Pretraining for Physical Surrogate Modelscited by 90
  20. 2022Physics-Informed Machine Learning: A Survey on Problems, Methods and Applicationscited by 88
  21. 2023Score-based Diffusion Models in Function Spacecited by 80
  22. 2024ClimODE: Climate and Weather Forecasting with Physics-informed Neural ODEscited by 78
  23. 2023Deep Learning for Day Forecasts from Sparse Observationscited by 70
  24. 2023Multi-Grid Tensorized Fourier Neural Operator for High-Resolution PDEscited by 66
  25. 2022Generic bounds on the approximation error for physics-informed (and) operator learningcited by 65
  26. 20253D and 4D World Modeling: A Surveycited by 64
  27. 2023Neural Inverse Operators for Solving PDE Inverse Problemscited by 61
  28. 2024Probabilistic Forecasting with Stochastic Interpolants and F"ollmer Processescited by 61
  29. 2024DiffDA: a Diffusion Model for Weather-scale Data Assimilationcited by 59
  30. 2024Neural Operators with Localized Integral and Differential Kernelscited by 59
  31. 2024On conditional diffusion models for PDE simulationscited by 56
  32. 2025FourCastNet 3: A geometric approach to probabilistic machine-learning weather forecasting at scalecited by 56
  33. 2023Towards Stability of Autoregressive Neural Operatorscited by 54
  34. 2023Latent diffusion models for generative precipitation nowcasting with accurate uncertainty quantificationcited by 53
  35. 2023ACE: A fast, skillful learned global atmospheric model for climate predictioncited by 53
  36. 2024Estimating Epistemic and Aleatoric Uncertainty with a Single Modelcited by 52
  37. 2025AB-UPT: Scaling Neural CFD Surrogates for High-Fidelity Automotive Aerodynamics Simulations via Anchored-Branched Universal Physics Transformerscited by 49
  38. 2023Super-Resolution Neural Operatorcited by 48

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