Aardvark Weather AI-Powered Weather Forecasting System Unveiled, Does Not Require Supercomputers

Aardvark Weather, an artificial intelligence (AI) system for weather forecasting that offers several advantages over traditional methods, was unveiled on Thursday. The fully AI-driven system is said to be capable enough to replace the existing systems that require custom supercomputers and a team of experts to deliver predictions. It is also said to improve prediction accuracy and speed, and provide hyperlocal forecasts. A large group of researchers from academic institutions and tech companies worked together to develop the AI system.

Researchers Unveil Aardvark Weather AI Forecasting System

The research paper was published in the Nature journal (can be accessed here), and details how a single, unified AI model replaces supercomputers and expert supervision. Notably, last year Google unveiled its GenCast model which uses AI to replace the numerical solver to improve the accuracy and speed of weather predictions. However, the scale of Aardvark Weather is much larger as it aims to replace the entire forecasting system.

The AI system was developed by researchers from the University of Cambridge, Microsoft Research AI for Science, Google DeepMind, the University of Toronto, The Alan Turing Institute, and the European Centre for Medium-Range Weather Forecasts (ECMWF).

Aardvark Weather collects data from weather satellites, weather balloons, stations, and other sensors, and it can process and analyse the data to produce both global and local forecasts. What typically takes custom supercomputers and weather science experts a few hours can be generated in a few minutes with the AI system.

Researchers claimed that the AI system was able to outperform the US national Global Forecast System (GFS) on multiple variables with just 10 percent of the input data. It is also said to perform on par with the US Weather Service forecasts. Aardvark can also be used to provide bespoke weather predictions for specific industries or locations.

“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,” said first author Anna Allen, from Cambridge’s Department of Computer Science and Technology, in a statement.