New AI-based weather forecasting is superior to traditional methods

Dan Stillman, Why your weather forecasts may soon become more accurate, Washington Post, Nov. 14, 2023.

Google DeepMind’s AI model, named “GraphCast,” was trained on nearly 40 years of historical data and can make a 10-day forecast at six-hour intervals for locations spread around the globe in less than a minute on a computer the size of a small box. It takes a traditional model an hour or more on a supercomputer the size of a school bus to accomplish the same feat. GraphCast was about 10 percent more accurate than the European model on more than 90 percent of the weather variables evaluated.

The study’s results are similar to those in an academic article published in August to the online database arXiv.

“To be competitive with arguably the best global prediction system, if not outperforming it, is astonishing,” Aaron Hill, lead developer of Colorado State University’s machine learning prediction system, said in an email. “You can safely add GraphCast to a growing list of AI-based weather prediction models that should see continued evaluation for their application in industry, research and operational forecasting.”

AI weather models have drawn increasing attention from government weather agencies because of their speed, efficiency and potential cost savings.

Traditional weather models, such as “the European,” operated by the European Center for Medium-Range Weather Forecasts (ECMWF) in Reading, Britain, and “the American,” by the National Oceanic and Atmospheric Administration, make forecasts based on complex mathematical equations. Such models underpin forecasts and lifesaving warnings worldwide but are expensive to run because they require tremendous amounts of computing power.

AI models use a different approach. They are first trained to recognize patterns in vast amounts of historical weather data, then generate forecasts by ingesting current conditions and applying what they learned from the historical patterns. The process is much less computationally intensive and can be completed in minutes or even seconds on much smaller computers.

However:

Researchers have expressed concerns about the ability of AI to accurately forecast extreme weather, in part because there are relatively few such events to learn from in the past. Yet GraphCast reduced cyclone forecast track errors by around 10 to 15 miles at a lead time of two to four days, improved forecasts of water vapor associated with atmospheric rivers by 10 to 25 percent, and provided more precise forecasts of extreme heat and cold five to 10 days ahead of time.

And:

Most experts, including the study’s authors, agree that traditional models aren’t about to be replaced by AI models, which still depend on the older models to supply training data and to generate the current conditions they use as a starting point to make a forecast.

Here's a link to the article in Science reporting the research underlying this article.