New AI systems could speed up our ability to create weather forecasts

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New AI systems could speed up our ability to create weather forecasts
MIT Technology Review, July 5, 2023
Artificial Intelligence
by Melissa Heikkilä

“They could also help to make them more accurate.”

 

As climate change makes weather more unpredictable and extreme, we need more reliable forecasts to help us prepare and prevent disasters. Today, meteorologists use massive computer simulations to make their forecasts. They take hours to complete, because scientists have to analyze weather variables such as temperature, precipitation, pressure, wind, humidity, and cloudiness one by one.

 

However, new artificial-intelligence systems could significantly speed up that process and make forecasts—and extreme-weather warnings—more accurate, two papers published in Nature today suggest.

The first, developed by Huawei, details how its new AI model, Pangu-Weather, can predict weekly weather patterns around the world much more quickly than traditional forecasting methods, but with comparable accuracy.

 

The second demonstrates how a deep-learning algorithm was able to predict extreme rainfall more accurately and with more notice than other leading methods, ranking first around 70% of the time in tests against similar existing systems.

 

If adopted, these models could be used alongside conventional weather predicting methods to improve authorities’ ability to prepare for bad weather, says Lingxi Xie, a senior researcher at Huawei.

 

To build Pangu-Weather, researchers at Huawei built a deep neural network trained on 39 years of reanalysis data, which combines historical weather observations with modern models. Unlike conventional methods that analyze weather variables one at a time, which could take hours, Pangu-Weather is able to analyze all of them at the same time in mere seconds.

 

The researchers tested Pangu-Weather against one of the leading conventional weather prediction systems in the world, the operational integrated forecasting system of the European Centre for Medium-Range Weather Forecasts (ECMWF), and found that it produced similar accuracy.

 

Pangu-Weather was also able to accurately track the path of a tropical cyclone, despite not having been trained with data on tropical cyclones. This finding shows that machine-learning models are able to pick up on the physical processes of weather and generalize them to situations they haven’t seen before, says Oliver Fuhrer, the head of the numerical prediction department at MeteoSwiss, the Swiss Federal Office of Meteorology and Climatology. He was not involved in the research.

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About the Author:

Melissa Heikkilä is a senior reporter at MIT Technology Review, where she covers artificial intelligence and how it is changing our society. Previously she wrote about AI policy and politics at POLITICO. She has also worked at The Economist and used to be a news anchor. Forbes named her as one of its 30 under 30 in European media in 2020.

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