ECMWF’s AIFS Now Available in the Weather API

Starting today, our users can access AIFS, ECMWF’s AI-based model, via the Weather API.

ECMWF Enters the AI Era

ECMWF, short for European Centre for Medium-Range Weather Forecasts, has been operational since 1975 and possesses the world's largest archive of numerical weather prediction data. It is considered the gold standard for global weather forecasting, and its data serves as the baseline for many other weather models.

Meteomatics’s high-resolution numerical weather model, EURO1k, for example, uses ECMWF's Integrated Forecasting System (IFS) for its boundary conditions. Other AI-based models, such as FourCastNet and GraphCast — which have been available on the API since April — are usually trained on a subset of historical weather data from ECMWF’s ERA5 reanalysis dataset, and rely on ECMWF-IFS analysis for initial conditions when producing a forecast.

Earlier this year, they announced their decision to develop their own AI weather forecasting model to "better understand and explore the machine learning (ML) technologies underpinning these models," referring to NVIDIA's FourCastNet and Google DeepMind's GraphCast.

AIFS: Artificial Intelligence/Integrated Forecasting System

AIFS stands for "Artificial Intelligence/Integrated Forecasting System." Here are some of its characteristics:

• Forecast system based on graph neural networks (GNNs)
• Current version trained on a subset of the ERA5 reanalysis data for 1979-2018 and fine-tuned on operational IFS data from 2019-2020
• Utilizes the PyTorch machine learning framework

Similar to FourCastNet and GraphCast, AIFS has a resolution of 25 km and an update frequency of 6 hours. Its forecast horizon is 15 days.

Enhanced Forecasts With Meteomatics 90-meter Downscaling

The output of AI-based forecasting models will also benefit from Meteomatics' signature 90-meter downscaling feature. This advanced downscaling capability enhances the spatial resolution of weather data, providing users with highly localized forecasts tailored to their specific geographical areas of interest.

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