AI Redefines Weather Forecasting New AI System Outperforms Traditional Weather Models

A new AI weather prediction system, Aardvark Weather, can deliver accurate forecasts tens of times faster and using thousands of times less computing power than current AI and physics-based forecasting systems, according to research published in Nature.

Researchers from the University of Cambridge supported by the Alan Turing Institute, Microsoft Research and the European Centre for Medium Range Weather Forecasting, have developed Aardvark. This weather AI provides a blueprint for a completely new approach to weather forecasting with the potential to transform current practices.

The weather forecasts that people rely upon are currently generated through a complex set of stages, each taking several hours to run on bespoke supercomputers. Aside from daily usage, the development, maintenance and deployment of these complex systems requires significant time and large teams of experts. 

More recently, research by Huawei, Google, and Microsoft has demonstrated that one component of this pipeline, the numerical solver (which calculates how weather evolves over time), can be replaced with AI resulting in faster and more accurate predictions. This combination of AI and traditional approaches is now being deployed by the European Centre for Medium Range Weather Forecasts.

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. 

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 customised 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. 

Faster, Cheaper and More Accurate Weather Predictions

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.”

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 optimisation, 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.”

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 democratise 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.

Original Article: Allen, A., et al. 2025. ‘End-to-end data-driven weather prediction’, Nature, DOI: 10.1038/s41586-025-08897-0

(ID:50363380)