“Weather prediction is one of the vital difficult problems that humanity has been working on for an extended, very long time. And for those who have a look at what has happened in the previous couple of years with climate change, that is an incredibly essential problem,” says Pushmeet Kohli, the vice chairman of research at Google DeepMind.  

Traditionally, meteorologists use massive computer simulations to make weather predictions. They’re very energy intensive and  time consuming to run, since the simulations take note of many physics-based equations and different weather variables akin to temperature, precipitation, pressure, wind, humidity, and cloudiness, one after the other. 

GraphCast uses machine learning to do these calculations in under a minute. As an alternative of using the physics-based equations, it bases its predictions on 4 a long time of historical weather data. GraphCast uses graph neural networks, which map Earth’s surface into greater than 1,000,000 grid points. At each grid point, the model predicts the temperature, wind speed and direction, and mean sea-level pressure, in addition to other conditions like humidity. The neural network is then capable of find patterns and draw conclusions about what’s going to occur next for every of those data points. 

For the past 12 months, weather forecasting has been going through a revolution as models akin to GraphCast, Huawei’s Pangu-Weather and Nvidia’s FourcastNet have made meteorologists rethink the role AI can play in weather forecasting. GraphCast improves on the performance of other competing models, akin to Pangu-Weather, and is capable of predict more weather variables, says Lam. The ECMWF is already using it.

When Google DeepMind first debuted GraphCast last December, it felt like Christmas, says Peter Dueben, head of Earth system modeling at ECMWF, who was not involved within the research. 

“It showed that these models are so good that we cannot avoid them anymore,” he says. 

GraphCast is a “reckoning moment” for weather prediction since it shows that predictions will be made using historical data, says Aditya Grover, an assistant professor of computer science at UCLA, who developed ClimaX, a foundation model that permits researchers to do different tasks regarding modeling the Earth’s weather and climate.