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The FourCastNet team at NVLabs published a journal paper in 2022 with details of their work and results (https://arxiv.org/pdf/2202.11214.pdf) of using a Fourier-based neural network forecasting model. Their results show that 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 small-scale variables, 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. FourCastNet has shown impressive results on predicting various variables on ERA5. While FourCastNet enjoys quasi-linear time and memory complexity in sequence length compared to quadratic complexity in vanilla transformers, training FourCastNet on ERA5 from scratch still requires large amount of compute resources which is expensive or even inaccessible to average researchers.

Wind and Precipitation prediction in FourCastNet notebook

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