  Machine learning-based weather forecasting models have quickly emerged as a
promising methodology for accurate medium-range global weather forecasting.
Here, we introduce the Artificial Intelligence Forecasting System (AIFS), a
data driven forecast model developed by the European Centre for Medium-Range
Weather Forecasts (ECMWF). AIFS is based on a graph neural network (GNN)
encoder and decoder, and a sliding window transformer processor, and is trained
on ECMWF's ERA5 re-analysis and ECMWF's operational numerical weather
prediction (NWP) analyses. It has a flexible and modular design and supports
several levels of parallelism to enable training on high-resolution input data.
AIFS forecast skill is assessed by comparing its forecasts to NWP analyses and
direct observational data. We show that AIFS produces highly skilled forecasts
for upper-air variables, surface weather parameters and tropical cyclone
tracks. AIFS is run four times daily alongside ECMWF's physics-based NWP model
and forecasts are available to the public under ECMWF's open data policy.
