Speeding up Weather Forecasts with Artificial Intelligence

Thursday - July 6 2023, 04:16 UTC - 1 year ago

Climate change is making weather more unpredictable, so reliable forecasts need to be made to protect us from disasters. A new AI model called Pangu-Weather was created by Huawei that can predict weekly weather patterns faster than traditional forecasting systems with comparable accuracy. Models like Pangu-Weather can improve on current methods and make extreme-weather warnings more accurate. Climate change is making weather forecasting increasingly important, and the AI models have opened up conversations between meteorologists on how to best use the technology with existing methods.

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.

Pangu-Weather is exciting because it can forecast weather much faster than scientists were able to before and forecast things that weren’t in its original training data, says Fuhrer.

In the past year, multiple tech companies have unveiled AI models that aim to improve weather forecasting. Pangu-Weather and similar models, such as Nvidia’s FourcastNet and Google-DeepMind’s GraphCast, are making meteorologists "reconsider how we use machine learning and weather forecasts," says Peter Dueben, head of Earth system modeling at ECMWF. He was not involved in the research but has tested Pangu-Weather.

Before, machine learning was seen as more of a "toy" project, Dueben says. But now it looks likely that meteorologists will be able to use it alongside conventional methods to make their forecasts.

Time will tell if models like Pangu-Weather can help protect us from extreme-weather events as climate change creates more frequent and intense storms, floods, and wind.