  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 $0.25^{\circ}$ 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.
