Groundbreaking AI Aurora Predicts Natural Disasters Faster, Cheaper & More Accurately

Researchers have introduced Aurora, a powerful AI model promising faster, more accurate climate and weather forecasts.

As climate-related disasters become more intense and frequent, a team of international researchers has unveiled Aurora, a powerful new AI model built to transform how we forecast the environment. Aurora delivers faster, more accurate, and more affordable predictions for critical areas like air quality, ocean waves, and extreme weather events. Trained on over a million hours of Earth system data, Aurora represents a major leap forward. Scientists believe it could reshape the way we prepare for natural disasters and tackle the challenges of climate change.

From devastating floods in Europe to the growing strength of tropical cyclones around the globe, the climate crisis has made reliable forecasting more important than ever. Traditional methods depend on highly complex numerical models developed over decades, which require massive supercomputers and large expert teams. Aurora offers a smarter and more efficient solution by harnessing the power of artificial intelligence.

Machine learning at the core

“Aurora uses state-of-the-art machine learning techniques to deliver superior forecasts for key environmental systems—air quality, weather, ocean waves, and tropical cyclones,” explains Max Welling, machine learning expert at the University of Amsterdam and one of the researchers behind the model.

Unlike conventional methods, Aurora requires far less computational power, making high-quality forecasting more accessible and scalable—especially in regions that lack expensive infrastructure.

Trained on a million hours of earth data

Aurora is built on a 1.3 billion parameter foundation model, trained on more than one million hours of Earth system data. It has been fine-tuned to excel in a range of forecasting tasks:

• Air quality: Outperforms traditional models in 74% of cases
• Ocean waves: Exceeds numerical simulations on 86% of targets
• Tropical cyclones: Beats seven operational forecasting centers in 100% of tests
• High-resolution weather: Surpasses leading models in 92% of scenarios, especially during extreme events

Forecasting that’s fast, accurate, and inclusive

As climate volatility increases, rapid and reliable forecasts are crucial for disaster preparedness, emergency response, and climate adaptation. The researchers believe Aurora can help by making advanced forecasting more accessible.

‘Development cycles that once took years can now be completed in just weeks by small engineering teams,’ notes AI researcher Ana Lucic, also of the University of Amsterdam. “This could be especially valuable for countries in the Global South, smaller weather services, and research groups focused on localized climate risks.”

“Importantly, this acceleration builds on decades of foundational research and the vast datasets made available through traditional forecasting methods,” Welling adds.

Aurora is available freely online for anyone to use. If someone wants to fine-tune it for a specific task, they will need to provide data for that task. “But the ‘initial’ training is done, we don’t need these vast datasets anymore, all the information from them is baked into Aurora already,” Lucic explains.

A future-proof forecasting tool

Although current research focuses on the four applications mentioned above, the researchers say Aurora is flexible and can be used for a wide range of future scenarios. These could include forecasting flood risks, wildfire spread, seasonal weather trends, agricultural yields, and renewable energy output.

“Its ability to process diverse data types makes it a powerful and future-ready tool,” states Welling.

As the world faces more extreme weather—from heatwaves to hurricanes—innovative models like Aurora could shift the global approach from reactive crisis response to proactive climate resilience, concludes the study.

Reference: “A foundation model for the Earth system” by Cristian Bodnar, Wessel P. Bruinsma, Ana Lucic, Megan Stanley, Anna Allen, Johannes Brandstetter, Patrick Garvan, Maik Riechert, Jonathan A. Weyn, Haiyu Dong, Jayesh K. Gupta, Kit Thambiratnam, Alexander T. Archibald, Chun-Chieh Wu, Elizabeth Heider, Max Welling, Richard E. Turner and Paris Perdikaris, 21 May 2025, Nature.
DOI: 10.1038/s41586-025-09005-y

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