Google DeepMind’s New AI Weatherman Tops World’s Most Reliable System

This was another year of rollercoaster weather. Heat domes broiled the US southwest. California experienced a “second summer” in October, with multiple cities breaking heat records. Hurricane Helene—and just a few weeks later, Hurricane Milton—pummeled the Gulf Coast, unleashing torrential rainfall and severe flooding. What shocked even seasoned meteorologists was how fast the hurricanes intensified, with one choking up as he said “this is just horrific.”

When bracing for extreme weather, every second counts. But planning measures rely on accurate predictions. Here’s where AI comes in.

This week, Google DeepMind unveiled an AI that predicts weather 15 days in advance in minutes, rather than the hours usually needed with traditional models. In a head-to-head with the European Center for Medium-Range Weather Forecasts’ model (ENS)—the best “medium-range” weather forecaster today—the AI won over 90 percent of the time.

Dubbed GenCast, the algorithm is DeepMind’s latest foray into weather prediction. Last year, they unleashed a version with strikingly accurate prediction for a 10-day forecast. GenCast differs in its machine learning architecture. True to its name, it’s a generative AI model, roughly similar to those that power ChatGPT, Gemini, or generate images and videos with a text prompt.

The setup gives GenCast an edge over previous models, which usually provide a single weather path prediction. GenCast, in contrast, pumps out 50 or more predictions—each representing a potential weather trajectory, while assigning their likelihood.

In other words, the AI “imagines” a multiverse of future weather possibilities and picks the one with the largest chance of occurring.

GenCast didn’t just excel at day-to-day weather prediction. It also beat ENS at predicting extreme weather—heat, cold, and high wind speeds. Challenged with data from Typhoon Hagibis—the deadliest tropical cyclone to strike Japan in decades—GenCast visualized possible routes seven days before landfall.

“As climate change drives more extreme weather events, accurate and trustworthy forecasts are more essential than ever,” wrote study authors Ilan Price and Matthew Wilson in a DeepMind blog post.

Embracing Uncertainty

Predicting weather is notoriously difficult. This is largely because weather is a chaotic system. You might have heard of the “butterfly effect”—a butterfly flaps it wings, stirring a tiny change in the atmosphere and triggering tsunamis and other weather disasters a world apart. Although just a metaphor, it highlights that any small changes in initial weather conditions can rapidly spread across large regions, changing weather outcomes.

For decades, scientists have tried to emulate these processes using physical simulations of the Earth’s atmosphere. By gathering data from weather stations across the globe and satellites, they’ve written equations mapping current estimates of the weather and forecasting how they’ll change over time.

The problem? The deluge of data takes hours, if not days, to crunch on supercomputers, and consumes a huge amount of energy.

AI may be able to help. Rather than mimicking the physics of atmospheric shifts or the swirls of our oceans, these systems slurp up decades of data to find weather patterns. GraphCast, released in 2013, captured more than a million points across our planet’s surface to predict 10-day weather in less than a minute. Others in the race to improve weather forecasting are Huawei’s Pangu-Weather and NowcastNet, both based in China. The latter gauges the chance of rain with high accuracy—one of the toughest aspects of weather prediction.

But weather is finicky. GraphCast and other similar weather-prediction AI models, in contrast, are deterministic. They only forecast a single weather trajectory. The weather community is now increasingly embracing an “ensemble model,” which predicts a range of possible scenarios.

“Such ensemble forecasts are more useful than relying on a single forecast, as they provide decision makers with a fuller picture of possible weather conditions in the coming days and weeks and how likely each scenario is,” wrote the team.

Cloudy With a Chance of Rain

GenCast tackles the weather’s uncertainty head-on. The AI mainly relies on a diffusion model, a type of generative AI. Overall, it incorporates 12 metrics about the Earth’s surface and atmosphere—such as temperature, wind speed, humidity, and atmospheric pressure—traditionally used to gauge weather.

The team trained the AI on 40 years of historical weather data from a publicly available database up to 2018. Rather than asking for one prediction, they had GenCast spew out a number of forecasts, each one starting with a slightly different weather condition—a different “butterfly,” so to speak. The results were then combined into an ensemble forecast, which also predicted the chance of each weather pattern actually occurring.

When tested with weather data from 2019, which GenCast had never seen, the AI outperformed the current leader, ENS—especially for longer-term forecasting up to 15 days. Checked against recorded data, the AI outperformed ENS 97 percent of the time across 1,300 measures of weather prediction.

GenCast’s predictions are also blazingly fast. Compared to the hours on supercomputers usually needed to generate results, the AI churned out predictions in roughly eight minutes. If adopted, the system could add valuable time for emergency notices.

All for One

Although GenCast wasn’t explicitly trained to forecast severe weather patterns, it was able to predict the path of Typhoon Hagibis before landfall in central Japan. One of the deadliest storms in decades, the typhoon flooded neighborhoods up to the rooftops as water broke through levees and took out much of the region’s electrical power.

GenCast’s ensemble prediction was like a movie. It began with a relatively wide range of possible paths for Typhoon Hagibis seven days before landfall. As the storm edged closer, however, the AI got more accurate, narrowing its predictive path. Although not perfect, GenCast painted an overall trajectory of the devastating cyclone that closely matched recorded data.

Given a week of lead time, “GenCast can provide substantial value in decisions about

when and how to prepare for tropical cyclones,” wrote the authors.

Accurate and longer predictions don’t just help prepare for future climate challenges. They could also help optimize renewable energy planning. Take wind power. Predicting where, when, and how strong wind is likely to blow could increase the power source’s reliability—reducing costs and potentially upping adoption of the technology. In a proof-of-concept analysis, GenCast was more accurate than ENS at predicting total wind power generated by over 5,000 wind power plants across the globe, opening the possibility of building wind farms based on data.

GenCast isn’t the only AI weatherman. Nvidia’s FourCastNet also uses generative AI to predict weather with a lower energy cost than traditional methods. Google Research has also engineered myriad weather-predicting algorithms, including NeuralGCM and SEEDS. Some are being integrated into Google search and maps, including rain forecasts, wildfires, flooding, and heat alerts. Microsoft joined the race with ClimaX, a flexible AI that can be tailored to generate predictions from hours to months ahead (with varying accuracies).

All this is not to say AI will be taking jobs from meteorologists. The DeepMind team stresses that GenCast wouldn’t be possible without foundational work from climate scientists and physics-based models. To give back, they’re releasing aspects of GenCast to the wider weather community to gain further insights and feedback.

Image Credit: NASA