DeepMind’s AI predicts almost exactly when and where it’s going to rain

Will Douglas Heaven, DeepMind’s AI predicts almost exactly when and where it’s going to rain, MIT Technology Review, September 29, 2021.

In a blind comparison with existing tools, several dozen experts judged DGMR’s forecasts to be the best across a range of factors—including its predictions of the location, extent, movement, and intensity of the rain—89% of the time. The results were published in a Nature paper today.

Difficulties for nowcasting

Forecasting rain, especially heavy rain, is crucial for a lot of industries, from outdoor events to aviation to emergency services. But doing it well is hard. Figuring out how much water is in the sky, and when and where it’s going to fall, depends on a number of weather processes, such as changes in temperature, cloud formation, and wind. All these factors are complex enough by themselves, but they’re even more complex when taken together.

The best existing forecasting techniques use massive computer simulations of atmospheric physics. These work well for longer-term forecasting but are less good at predicting what’s going to happen in the next hour or so, known as nowcasting. Previous deep-learning techniques have been developed, but these typically do well at one thing, such as predicting location, at the expense of something else, such as predicting intensity. 

Model

The researchers fed this data to a deep generative network, similar to a GAN—a kind of AI that is trained to generate new samples of data that are very similar to the real data it was trained on. GANs have been used to generate fake faces, even fake Rembrandts. In this case, DGMR (which stands for “deep generative model of rainfall”) learned to generate fake radar snapshots that continued the sequence of actual measurements. It’s the same idea as seeing a few frames of a movie and guessing what’s going to come next, says Shakir Mohamed, who led the research at DeepMind. 

Domain knowledge is important. 

DeepMind’s collaboration with the Met Office is a good example of AI development done in collaboration with the end user, something that seems like an obviously good idea but often does not happen. The team worked on the project for several years, and input from the Met Office’s experts shaped the project. “It pushed our model development in a different way than we would have gone down on our own,” says Suman Ravuri, a research scientist at DeepMind. “Otherwise we might have made a model that was ultimately not particularly useful.”