Recently, Google DeepMind announced GenCast, a new artificial intelligence weather prediction tool capable of generating high-resolution forecasts up to 15 days in advance. According to Nature, GenCast significantly improves weather forecasting, marking a major advancement in the field.
Unlike traditional weather models that rely on solving physics equations, GenCast is a machine learning-based weather model that learns directly from historical weather data. "GenCast is not limited to learning dynamics/patterns that are known exactly and can be written down in an equation," said Ilan Price, a research scientist at Google DeepMind and an author of the paper published in Nature, according to The Register.
GenCast merges computational approaches used by atmospheric scientists with a diffusion model commonly used in generative AI, maintaining high resolution while significantly cutting computational costs, according to the Nature paper. It produces a 15-day weather forecast in just eight minutes, which is a significant improvement over traditional methods that can take hours to generate forecasts.
In tests comparing the 15-day forecasts generated for weather in 2019, GenCast was, on average, more accurate than the European Centre for Medium-Range Weather Forecasts (ECMWF) Ensemble Prediction System (ENS) 97.2 percent of the time. With lead times greater than 36 hours, GenCast was 99.8 percent more accurate than ECMWF's ENS system.
GenCast consistently outperformed ENS in forecasting extreme weather, including extreme heat, cold, and high wind speeds, as noted by Business Insider. It also demonstrated superior forecasting capability in predicting the path of tropical cyclones, according to The Register.
The AI tool helps in better understanding and monitoring dangerous phenomena such as severe storms and hurricanes, allowing necessary protective measures to be taken, which can safeguard lives and reduce damage.
"Better predictions of extreme weather enable better decisions," Google's DeepMind said in an announcement, according to the Financial Times.
Rémi Lam of DeepMind noted that GenCast's generative skills were rooted in factual data gathered from nature rather than the internet. "We have a ground truth. We have a reality check," Lam said, as reported by The New York Times.
Experts in the field have recognized the significance of GenCast's advancements."It's a big deal; it's an important step forward," said Kerry Emanuel, a professor emeritus of atmospheric science at MIT who did not participate in DeepMind's research, according to The New York Times. "The status quo isn't going to disappear. Perhaps the two of them working together will prove to be the best way forward," he added.
GenCast's probabilistic forecasting capabilities provide a range of percentages for the likelihood of various weather scenarios, enhancing users' understanding and decision-making in high-risk situations, according to The New York Times.
The ECMWF acknowledged the advancements made by GenCast. Matthew Chantry, an AI specialist at the ECMWF, confirmed that his agency was already adopting some features of DeepMind's GenCast. "We've actually implemented some of the key breakthroughs in our own machine-learning model," he said.
Price said that GenCast's weather predictions would soon be posted publicly on Google's Earth Engine and BigQuery, giving scientists access to the new forecasts, according to Engadget. "We're excited for the community to use and build on our research," Price said, reported The New York Times.
Despite its high resolution, DeepMind estimates that a single instance of GenCast can be run out to 15 days on Google's tensor processing systems in just eight minutes, according to Ars Technica. This speed makes GenCast's projections much timelier, providing a significant advantage in monitoring rapidly moving storms.
This article was written in collaboration with generative AI company Alchemiq