AIFS -- ECMWF's data-driven forecasting system
Simon Lang, Mihai Alexe, Matthew Chantry, Jesper Dramsch, Florian, Pinault, Baudouin Raoult, Mariana C. A. Clare, Christian Lessig, Michael, Maier-Gerber, Linus Magnusson, Zied Ben Bouall\`egue, Ana Prieto Nemesio,, Peter D. Dueben, Andrew Brown, Florian Pappenberger

TL;DR
AIFS is a novel data-driven weather forecasting system using graph neural networks and transformers, developed by ECMWF, that provides accurate medium-range forecasts and complements traditional NWP models.
Contribution
Introduces AIFS, a modular, high-resolution, data-driven forecasting system based on GNNs and transformers, enhancing ECMWF's weather prediction capabilities.
Findings
AIFS achieves high skill in forecasting upper-air and surface variables.
AIFS accurately predicts tropical cyclone tracks.
AIFS forecasts are comparable to ECMWF's NWP analyses.
Abstract
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…
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Taxonomy
TopicsForecasting Techniques and Applications
