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Researchers eye AI revolution in natural disaster forecasts

Disasters that relate to geology, like landslides and glacier collapses, can often be detected early. Read more at straitstimes.com.

The Straits TimesThe Straits Times2026-09-11 11:20查看原文 ↗

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

Published Sep 11, 2026, 11:20 AM

Updated Sep 11, 2026, 03:37 PM

Researchers in Switzerland are using AI and NASA's vast climate data on a supercomputer called Alps to revolutionise natural disaster forecasting.

AI models can rapidly produce global weather forecasts in minutes and detect early warning signs of disasters like glacier collapses.

This approach could improve early warning systems, potentially saving many lives by enabling faster and more accurate disaster prevention.

LUGANO – A revolution in forecasting natural disasters is under way, 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.

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 in August 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.

Standing in front of rows of what look like giant filing cabinets that contain Alps, he told AFP he was excited to have all of NASA’s climate data plugged directly into the machine.

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

Data is ‘everything’

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 Centre 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, he told AFP.

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, nodding to the clusters of servers humming loudly just metres from the supercomputer.

“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.”

Not all disasters are foreseeable but those that relate to geology, like landslides and glacier collapses, can often be detected early in the data, if there is capacity to spot the pattern.

Kutti pointed to the case of the Swiss village of Blatten, which was wiped out by a dramatic glacier collapse in May 2025, “that was visible in satellite data more than a year before it actually happened”.

Thanks to close monitoring, Swiss authorities evacuated the village a week before the collapse, avoiding mass casualties.

Closer monitoring could also possibly have sounded the alarm before the devastating Aug 26 glacial collapse on the Nepal-China border.

Nature reported last week satellite image analysis showed some warning signs before the disaster, which, had they been spotted, could have flagged the area “as a hotspot warranting closer attention and monitoring”.

Knutti agreed that this was a good example of how satellite data could potentially be used for “systematic observing systems for disaster prevention that could save hundreds or thousands of lives”. AFP

AI/artificial intelligence

人工智能模型越来越多地应用于天气和气候预测以及自然灾害的早期检测。

发布于 2026 年 9 月 11 日上午 11:20

更新于2026年9月11日下午3:37

瑞士的研究人员正在利用人工智能和美国宇航局庞大的气候数据，在一台名为“阿尔卑斯山”的超级计算机上，彻底改变自然灾害预测的方式。

人工智能模型可以在几分钟内快速生成全球天气预报，并能检测出冰川崩塌等灾害的早期预警信号。

这种方法可以改进预警系统，通过更快、更准确地预防灾害，有可能挽救许多生命。

卢加诺——瑞士的研究人员表示，一场自然灾害预测革命正在进行中，他们正在利用美国宇航局的大量气候数据训练人工智能模型，以闪电般的速度生成可能拯救生命的数据。

他们正在将 NASA 的文件库输入到世界上最强大的超级计算机之一中，以便人工智能能够加快和扩展重要的天气和气候预测，并发现科学家无法看到的模式。

除了速度之外，它们还有望发现卫星和其他数据中以前无法察觉的模式，从而有可能提前预警像尼泊尔毁灭性洪水这样的灾难，这场洪水在 8 月份造成数千人死亡或失踪。

但训练此类人工智能模型需要大量的优质气候和地球观测数据，以及强大的计算能力。

瑞士苏黎世联邦理工学院 (ETH) 的研究人员表示，他们现在已经同时拥有了这两项数据，他们将大约 100 PB 的美国宇航局公开数据复制到了与世界上最强大的超级计算机之一——阿尔卑斯超级计算机相邻的服务器上。