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.