AI beats existing methods of predicting weather, air quality, ocean waves, and cyclone paths

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A Microsoft artificial intelligence (AI) called Aurora can outperform existing Earth system forecasts, according to international scientists. Earth system forecasts, which predict a range of different things, including weather, air quality, ocean currents, sea ice, and hurricanes, are used to provide early warnings of extreme events. Existing methods analyse decades worth of data and require a huge amount of computing power. That data has been fed into Aurora, and the researchers say the AI outperformed seven forecasting centres in predicting the route of cyclones five days into the future in 100% of cases, and in 92% of 10-day weather forecasts. Training Aurora took around two months, whereas existing methods can take years. However, it was only possible to train the AI so quickly because the data used to train it had already been collected by existing Earth forecasting centres, the researchers say.

Media release

From: Springer Nature

AI model outperforms existing Earth system forecasts

An AI model, developed by Microsoft, that can outperform existing Earth system forecasts is reported in Nature. The model, named Aurora, could enable more accurate and efficient forecasting of air quality, tropical cyclone paths, and ocean wave dynamics, as well as high-resolution weather forecasting.

Earth system forecasts provide information on a range of processes — such as weather, air quality, ocean currents, sea ice, and hurricanes — and serve as integral tools for providing early warnings for extreme events. The forecasts rely on complex models built on decades of data, which are computationally demanding — often requiring supercomputers and entire teams to maintain them. Recent advances in AI technology have shown promise in predictive performance and efficiency; however, their use in Earth system forecasting has not been fully explored.

Paris Perdikaris and colleagues present Aurora, an AI model trained on over 1 million hours of geophysical data. Aurora outperforms existing models on air quality, ocean waves, tropical cyclone tracks, and high-resolution weather at a lower computational cost than current forecasting methods. The authors report that Aurora performed better than seven forecasting centres on 5-day cyclone track predictions on 100% of the targets measured and on 92% of the targets for 10-day weather forecasts. The experiments required to train Aurora took around 4–8 weeks from start to finish, compared to the years currently needed to develop baseline models. The authors note that this timeline was achievable only because of the previously accumulated data from traditional approaches.

The authors note that Aurora is a foundation model for the Earth system and could be adapted for uses beyond weather forecasting. They conclude by saying that Aurora represents a development in efficient Earth system forecasting and highlights the potential of AI technology to provide wider access to weather and climate information.

Multimedia

Interview with Paris Perdikaris
Paris Perdikaris in Amy Gutmann Hall at Penn Engineering
Paris Perdikaris in Amy Gutmann Hall at Penn Engineering

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Organisation/s: Microsoft Research, The Netherlands, University of Pennsylvania, USA
Funder: Richard E. Turner was financed by EPSRC Prosperity Partnership EP/T005386/1 between Microsoft Research and the University of Cambridge during the final stages of the project.
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