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Recently, the FourCastNet Neural Earth System Model (NESM) has shownimpressive results on predicting various atmospheric variables, trained on theERA5 reanalysis dataset. While FourCastNet enjoys quasi-linear time and memorycomplexity in sequence length compared to quadratic complexity in vanillatransformers, training FourCastNet on ERA5 from scratch still requires largeamount of compute resources, which is expensive or even inaccessible to mostresearchers. In this work, we will show improved methods that can trainFourCastNet using only 1% of the compute required by the baseline, whilemaintaining model performance or par or even better than the baseline.

FourCastNeXt: Improving FourCastNet Training with Limited Compute