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FourCastNeXt: Optimizing FourCastNet Training for Limited Compute
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FourCastNeXt is an optimization of FourCastNet - a global machine learning weather forecasting model - that performs with a comparable level of accuracy and can be trained using around 5% of the original FourCastNet computational requirements. This technical report presents strategies for model optimization that maintain similar performance as measured by the root-mean-square error (RMSE) of the modelled variables. By providing a model with very low comparative training costs, FourCastNeXt makes Neural Earth System Modelling much more accessible to researchers looking to conduct training experiments and ablation studies. FourCastNeXt training and inference code are available at https://github.com/nci/FourCastNeXt
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Cited by 2 Pith papers
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ArchesWeather & ArchesWeatherGen: a deterministic and generative model for efficient ML weather forecasting
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Democracy of AI Numerical Weather Models: An Example of Global Forecasting with FourCastNetv2 Made by a University Research Lab Using GPU
A university group demonstrated that the freely available FourCastNetv2 model can make global forecasts and support classroom teaching with modest GPU resources, and shared the tutorial materials.
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