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FourCastNet, short for Fourier Forecasting Neural Network, is a globaldata-driven weather forecasting model that provides accurate short tomedium-range global predictions at $0.25^{\circ}$ resolution. FourCastNetaccurately forecasts high-resolution, fast-timescale variables such as thesurface wind speed, precipitation, and atmospheric water vapor. It hasimportant implications for planning wind energy resources, predicting extremeweather events such as tropical cyclones, extra-tropical cyclones, andatmospheric rivers. FourCastNet matches the forecasting accuracy of the ECMWFIntegrated Forecasting System (IFS), a state-of-the-art Numerical WeatherPrediction (NWP) model, at short lead times for large-scale variables, whileoutperforming IFS for variables with complex fine-scale structure, includingprecipitation. FourCastNet generates a week-long forecast in less than 2seconds, orders of magnitude faster than IFS. The speed of FourCastNet enablesthe creation of rapid and inexpensive large-ensemble forecasts with thousandsof ensemble-members for improving probabilistic forecasting. We discuss howdata-driven deep learning models such as FourCastNet are a valuable addition tothe meteorology toolkit to aid and augment NWP models.

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