Researchers eye AI revolution in natural disaster forecasts

LUGANO

Swiss National Supercomputing Centre (CSCS) director Thomas Schulthess (R) discuss with professor of climate physics at the Swiss Federal Institute of Technology Zurich (ETH Zurich) Reto Knutti next to the Alps supercomputer, at the CSCS, in Lugano, southern Switzerland, on August 27, 2026. (AFP Photo)

A revolution in forecasting natural disasters is underway, say researchers in Switzerland who are training AI models on vast troves of NASA climate data to produce potentially-lifesaving data at lightning speed.

They are feeding the NASA file stash into one of the world's most powerful supercomputers so artificial intelligence can speed up and expand vital weather and climate forecasting, and spot patterns scientists could not have seen.

AI models are increasingly used in weather and climate forecasting and for early detection of natural hazards.

As well as speed, they carry the promise of spotting previously imperceptible patterns in satellite and other data, potentially making it possible to flag in advance disasters like Nepal's devastating flood, which last month left thousands dead or missing.

But training such AI models requires vast amounts of high-quality climate and Earth observation data, and significant computing power.

Researchers at Switzerland's Federal Institute of Technology Zurich (ETH) say they now have both, after copying around 100 petabytes of publicly available NASA data onto servers adjacent to one of the world's most powerful supercomputers, known as Alps.

"This is a huge scientific opportunity," said Thomas Schulthess, an ETH computational physics professor and head of the Swiss National Supercomputing Centre (CSCS) in the southern city of Lugano.

"It's really enabling scientists to do things we would not even have thought of before," he said.

It took approximately a year to copy the roughly six billion NASA files onto servers connected to the supercomputer, said Reto Knutti, a climate physics professor who heads ETH's Center for Climate Systems Modeling (C2SM).

That is equivalent to around 20 million feature-length films in terms of data volume, or around a million times the storage on a typical computer.

Now the researchers are using the mass of information to develop AI models that can speed up and expand vital weather and climate forecasting.

"Data is essentially everything," Knutti said.

"The next step will be making sense of the data".

That is where the proximity to the massive computing power of Alps comes in, Schulthess said.

"It matters whether you can move the data within a few seconds or whether you have to wait days for the data to come," he said.

New AI-generated statistical models are far faster than the traditional process of using mathematically equations to simulate the complex processes taking place in the oceans and atmosphere.

There is "a revolution in weather forecasting," Knutti said, describing the statistical models as "really, really powerful" and "incredibly fast."

Instead of the hours traditionally needed to produce a forecast, a statistical model based on pattern recognition "can run a global weather forecast for multiple days — the whole globe — in a minute or so", he pointed out.

And while expensive to train, the models are "cheap to run," he said, meaning "you can do more iterations, maybe every few minutes, to see if some specific weather pattern exists."

"That allows us to do early warning systems to save lives."