AI prediction model is a major breakthrough in weather forecasting

New technology is transforming weather prediction. Aardvark Weather is a system that uses artificial intelligence to deliver accurate forecasts in just minutes – right from a regular desktop computer. It’s tens of times faster than current methods and requires only a fraction of the computing power.

The technology was developed by scientists at the University of Cambridge, with support from the Alan Turing Institute, Microsoft Research, and the European Centre for Medium-Range Weather Forecasts (ECMWF).

The system lays the foundation for a new kind of forecasting that could shift the way we understand and prepare for weather around the world.

How traditional weather forecasting works

Right now, most weather forecasts come from massive systems that require supercomputers to run. These systems are highly accurate but expensive, complex, and slow.

Each step in the forecasting process – from collecting data to generating local predictions – takes hours and involves many technical stages. Even minor updates to these systems take large teams of experts and years to implement.

Recently, companies like Huawei, Google, and Microsoft have demonstrated that AI can replace one of those steps – the numerical solver, which calculates how weather systems evolve.​

This has made forecasting both faster and more accurate. ECMWF is already using a mix of these traditional and AI-based tools.

New approach to weather forecasting

Aardvark goes a step further. It replaces the entire pipeline with a single machine learning model. The system takes real-time data from satellites, sensors, and weather stations, and immediately outputs both local and global forecasts.

The result? Lightning-fast predictions that can be generated on a desktop computer. And because it’s trained directly on data, the system avoids many of the hurdles that come with designing new forecasting models from scratch.

Professor Richard Turner is the lead researcher for Weather Prediction at the Alan Turing Institute and Professor of Machine Learning in the Department of Engineering at the University of Cambridge.

“Aardvark reimagines current weather prediction methods offering the potential to make weather forecasts faster, cheaper, more flexible and more accurate than ever before, helping to transform weather prediction in both developed and developing countries,” said Professor Turner.

“Importantly, Aardvark would not have been possible without decades of physical-model development by the community, and we are particularly indebted to ECMWF for their ERA5 dataset which is essential for training Aardvark.”

Doing more with less data

Even when Aardvark uses only 10% of the data that conventional models need, it still outperforms the United States’ national GFS forecasting system in many categories.

Aardvark also holds its own against forecasts produced by expert analysts working with dozens of models. This efficiency makes it not only fast, but incredibly versatile.

The model can be quickly trained to produce location-specific predictions – like rainfall for African farms or wind speeds for European wind farms. That level of customization used to take years to build. With Aardvark, it can happen in weeks.

“These results are just the beginning of what Aardvark can achieve,“ said Anna Allen, lead author from the University of Cambridge.

“This end-to-end learning approach can be easily applied to other weather forecasting problems, for example hurricanes, wildfires, and tornadoes. Beyond weather, its applications extend to broader Earth system forecasting, including air quality, ocean dynamics, and sea ice prediction.”

Forecasting for everyone

One of Aardvark’s most promising features is accessibility. Since it doesn’t rely on expensive supercomputers, the model can be used anywhere – even in countries with limited computing infrastructure. This levels the playing field for weather prediction and planning.

“We have been thrilled to collaborate on this project which explores the next generation of weather forecasting systems – part of our mission to develop and deliver operational AI-weather forecasting while openly sharing data to benefit science and the wider community,” said Matthew Chantry, Strategic Lead for Machine Learning at ECMWF.

”It is essential that academia and industry work together to address technological challenges and leverage new opportunities that AI offers. Aardvark’s approach combines both modularity with end-to-end forecasting optimisation, ensuring effective use of the available datasets.”

Dr. Chris Bishop is the director of Microsoft Research AI for Science. He noted that Aardvark represents not only an important achievement in AI weather prediction but it also reflects the power of collaboration and bringing the research community together to improve and apply AI technology in meaningful ways.

What’s next for Aardvark

The next step is to build a team within the Alan Turing Institute focused on expanding Aardvark’s use, especially in developing countries. The experts also plan to integrate the model into wider efforts around environmental prediction, including sea ice and ocean behavior.

“Unleashing AI’s potential will transform decision-making for everyone from policymakers and emergency planners to industries that rely on accurate weather forecasts. Aardvark’s breakthrough is not just about speed, it’s about access,” said Dr. Scott Hosking, Director of Science and Innovation for Environment and Sustainability at The Alan Turing Institute.

”By shifting weather prediction from supercomputers to desktop computers, we can democratize forecasting, making these powerful technologies available to developing nations and data-sparse regions around the world.”

The full study was published in the journal Nature.

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