Google DeepMind’s weather AI can forecast extreme weather faster and more accurately

“Weather prediction is one of the most challenging problems that humanity has been working on for a long, long time. And if you look at what has happened in the last few years with climate change, this is an incredibly important problem,” says Pushmeet Kohli, the vice president of research at Google DeepMind.  

Traditionally, meteorologists use massive computer simulations to make weather predictions. They are very energy intensive and  time consuming to run, because the simulations take into account many physics-based equations and different weather variables such as temperature, precipitation, pressure, wind, humidity, and cloudiness, one by one. 

GraphCast uses machine learning to do these calculations in under a minute. Instead of using the physics-based equations, it bases its predictions on four decades of historical weather data. GraphCast uses graph neural networks, which map Earth’s surface into more than a million grid points. At each grid point, the model predicts the temperature, wind speed and direction, and mean sea-level pressure, as well as other conditions like humidity. The neural network is then able to find patterns and draw conclusions about what will happen next for each of these data points. 

For the past year, weather forecasting has been going through a revolution as models such as 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, such as Pangu-Weather, and is able to 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 in 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 because it shows that predictions can be made using historical data, says Aditya Grover, an assistant professor of computer science at UCLA, who developed ClimaX, a foundation model that allows researchers to do different tasks relating to modeling the Earth’s weather and climate. 

“Weather prediction is one of the most challenging problems that humanity has been working on for a long, long time. And if you look at what has happened in the last few years with climate change, this is an incredibly important problem,” says Pushmeet Kohli, the vice president of research at Google DeepMind.  

Traditionally, meteorologists use massive computer simulations to make weather predictions. They are very energy intensive and  time consuming to run, because the simulations take into account many physics-based equations and different weather variables such as temperature, precipitation, pressure, wind, humidity, and cloudiness, one by one. 

GraphCast uses machine learning to do these calculations in under a minute. Instead of using the physics-based equations, it bases its predictions on four decades of historical weather data. GraphCast uses graph neural networks, which map Earth’s surface into more than a million grid points. At each grid point, the model predicts the temperature, wind speed and direction, and mean sea-level pressure, as well as other conditions like humidity. The neural network is then able to find patterns and draw conclusions about what will happen next for each of these data points. 

For the past year, weather forecasting has been going through a revolution as models such as 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, such as Pangu-Weather, and is able to 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 in 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 because it shows that predictions can be made using historical data, says Aditya Grover, an assistant professor of computer science at UCLA, who developed ClimaX, a foundation model that allows researchers to do different tasks relating to modeling the Earth’s weather and climate.