Aardvark Weather is a groundbreaking AI-powered forecasting system that is faster, cheaper, and more efficient than traditional methods.
Unlike conventional weather models that require supercomputers and hours of processing, Aardvark can generate accurate forecasts in minutes using minimal computing power.
Aardvark Weather: A Revolutionary AI Forecasting System
A new AI-powered weather prediction system, Aardvark Weather, can generate highly accurate forecasts tens of times faster while using thousands of times less computing power than current AI and physics-based models, according to research published today (March 20) in Nature.
Developed by researchers at the University of Cambridge, with support from the Alan Turing Institute, Microsoft Research, and the European Centre for Medium-Range Weather Forecasts (ECMWF), Aardvark introduces a groundbreaking approach to weather forecasting that could dramatically improve current methods.
The Complexity of Current Weather Prediction
Today’s weather forecasts rely on complex, multi-stage processes that take hours to compute using specialized supercomputers. Beyond daily forecasting, maintaining and running these systems demands significant resources, including large teams of experts.
Recent research from Huawei, Google, and Microsoft has shown that AI can replace one key part of this pipeline—the numerical solver, which models how weather evolves over time. This AI-enhanced method has already improved forecast speed and accuracy and is now being implemented by ECMWF as part of a hybrid approach that combines AI with traditional forecasting techniques.
Aardvark’s Radical AI-Driven Approach
But with Aardvark, researchers have replaced the entire weather prediction pipeline with a single, simple machine learning model. The new model takes in observations from satellites, weather stations and other sensors and outputs both global and local forecasts. This fully AI driven approach means that predictions are now achievable in minutes on a desktop computer.
When using just 10% of the input data of existing systems, Aardvark already outperforms the United States national GFS forecasting system on many variables and it is also competitive with United States Weather Service forecasts that use input from dozens of weather models and analysis by expert human forecasters.
Flexibility and Customization for Industries
One of the most exciting aspects of Aardvark is its flexibility and simple design. Because it learns directly from data it can be quickly adapted to produce bespoke forecasts for specific industries or locations, be that predicting temperatures for African agriculture or wind speeds for a renewable energy company in Europe.
This contrasts to traditional weather prediction systems where creating a customized system takes years of work by large teams of researchers.
This capability has the potential to transform weather prediction in developing countries where access to the expertise and computational resources required to develop conventional systems is not typically available.
Expert Voices on Aardvark’s Potential
Professor Richard Turner, 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, said: “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.”
“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.”
Anna Allen, lead author from the University of Cambridge, said “These results are just the beginning of what Aardvark can achieve. 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.”
Collaboration is Key to AI’s Success
Matthew Chantry, Strategic Lead for Machine Learning at ECMWF said: “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. 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 optimization, ensuring effective use of the available datasets.”
Dr. Chris Bishop, Technical Fellow and Director, Microsoft Research AI for Science, said: “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.”
A Leap Toward Democratizing Forecasting
Dr. Scott Hosking, Director of Science and Innovation for Environment and Sustainability at The Alan Turing Institute, said: “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. 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.”
Next steps for Aardvark include developing a new team within the Alan Turing Institute led by Professor Richard Turner, exploring the potential to deploy Aardvark in the global south and integrating the technology into the Institute’s wider work to develop high-precision environmental forecasting for weather, oceans and sea ice.
Reference: “End-to-end data-driven weather prediction” by Anna Allen, Stratis Markou, Will Tebbutt, James Requeima, Wessel P. Bruinsma, Tom R. Andersson, Michael Herzog, Nicholas D. Lane, Matthew Chantry, J. Scott Hosking and Richard E. Turner, 20 March 2025, Nature.
DOI: 10.1038/s41586-025-08897-0
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3 Comments
I’ve got 3 weather apps on my Android phone and most of the time, they are all inaccurate with my local weather.
So, it makes stuff up faster than a human meteorologist. Great.
“Aardvark already outperforms the United States national GFS forecasting system on many variables and it is also competitive with United States Weather Service forecasts that use input from dozens of weather models and analysis by expert human forecasters.”
The important part is the claim, which remains to be verified, that it is already more accurate than what we have come to rely on, and pay dearly for. Supercomputers are not cheap to run!