AI works in ways that are very different from traditional forecasting models. The latter are webs of complex equations meant to capture the atmosphere’s chaotic physics. They are fed data from weather balloons and stations around the world, and they use them to project how the weather will unfurl as various air masses and other atmospheric features interact. Forecasters generally run several such models and then integrate the resulting information—filtered through their own expert knowledge of local geography and each model’s strengths and weaknesses—into a coherent prediction.

In contrast, GraphCast and most of the other new AI tools abandon efforts to understand and mathematically replicate real-world physics (though NVIDIA’s FourCastNet is an exception). Instead the AI tools are statistical models: they recognize patterns in training data sets composed of decades of observational weather records and information gleaned from physical forecasting. Thus these models may notice that the weather setup of a certain day resembles similar events in the past and make a forecast based on that pattern.

Because of their reliance on past data, most AI models might be poorly equipped to forecast rare and never-before-seen events, says Kim Wood, an associate professor of atmospheric science and hydrology at the University of Arizona. Such events include Hurricane Harvey, which dropped an unprecedented 60 inches of rain on parts of Texas in 2017, and the exceptionally rapid intensification of Hurricane Otis from a tropical storm to a Category 5 monster just before it hit Mexico’s Pacific Coast last year. “The events it sees most often [in training data], it’s going to be best at capturing. So on average, it’s probably pretty good,” Wood says. “But the kind of events that can change peoples’ lives forever—maybe it would struggle more with that.” Those “rare” events are becoming more commonplace as the climate changes, Wood notes, so accurately capturing and predicting them is increasingly important….

And though it’s true that an AI model can spit out a forecast in a matter of minutes versus the two to three hours it takes physics-based models to complete a supercomputer-powered run, there is no way to determine exactly how the AI arrives at its forecast. Unlike physics-based models, GraphCast and other similar forecasting tools are not “interpretable.” That means outcomes can’t be readily traced back to the tens of millions of parameters that comprise these models.

Read more:

Leffer, L. (2024, January 9). AI Weather Forecasting Can’t Replace Humans—Yet. Scientific American. https://www.scientificamerican.com/article/ai-weather-forecasting-cant-replace-humans-yet/