AI is making weather forecasts better - by Aaron Foyer

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Deep Dives<br>AI is Making Weather Forecasts Better<br>How being able to better predict rain will reshape the energy sector...

Aaron Foyer<br>Mar 10, 2026

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Here is every dad for the past 80 years: “A weatherman is someone who tells you tomorrow will be nice then apologizes the next day.”<br>*slaps knee*<br>Okay, a couple of things wrong with this one: First off, it’s 2026 and the professionals are called “weather forecasters” or just “meteorologists”. Secondly, those meteorologists are now remarkably accurate. Decades of investment in satellites, international data gathering systems and supercomputers have greatly improved weather forecasts.<br>But the science is in the midst of a major step change in performance due to AI. A completely new approach to meteorology that incorporates the latest technology coming out of Silicon Valley is already outperforming today’s best models. And beyond just informing you whether it’s a good idea to wear white sneakers outside, better forecasts will also transform the energy industry.

Google DeepMind’s WeatherNext 2 AI-driven weather forecast<br>So, how will AI-driven forecasts make the grid cheaper? Let’s read the radar and predict what’s ahead.<br>Background

A lot has changed in the science of predicting the weather since Aristotle wrote Meteorologica back in 340 BCE.<br>For millennia, the practice was a blend of observation, folklore and cultural memory, more akin to the Freman of Dune than science. During the 17th century, polymaths including Evangelista Torricelli, Blaise Pascal and Galileo developed tools like barometers and thermometers that would help transform weather from mysticism to mechanical.<br>But the real Lisan al-Gaib of meteorology is John von Newmann, sometimes considered the smartest man who ever lived, who realized in the 1940s that early computers could finally solve the fluid flow and thermodynamic equations needed to truly predict the weather.

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By incorporating pressure, temperature and other atmospheric data points collected from across the globe, physics models spun through computers could predict how the atmosphere was likely to evolve and what that meant for upcoming weather.<br>Since then, forecasts have only got better, mostly by incorporating more data and better underlying models. Satellites now collect high-fidelity data from around the globe in real time which vast gathering systems can quickly assimilate and feed into supercomputers that then run some of the most sophisticated algorithms humans have ever built.<br>In plain English, today’s models divide the world up into millions of tiny grids, apply the laws of physics and then move the movie forward one frame at a time.<br>Current conditions: There are now more than 200 billion weather observations made each day around the world, from satellites, weather balloons, airplane sensors and on-the-ground instruments.<br>In a paper published in Nature, researchers found the accuracy of forecasts have been improving by about a day per decade, meaning the six-day forecast today is as good as the five-day forecast was a decade ago.<br>Improvements to weather forecasts have started to level off

Difference between the forecast and subsequent weather

European Center for Medium-Range Weather Forecasts<br>And forecasts can be a matter of life or death. On how tropical cyclones evolve, the US National Oceanic and Atmospheric Administration found errors on the 5-day path outlook have declined by roughly half since the mid-1990s, while the error on the intensity of the storm dropped by nearly 30%. That could be the difference between living through a Cat 5 hurricane and boarding up then getting the heck out of Dodge.<br>AI makes landfall

Like most industries, meteorology has been upended by artificial intelligence. It would be easy to fall into the trap of thinking that AI is simply making the existing models of weather forecasting better, but that’s not what’s happening. The latest AI models coming out of shops like IBM and Google DeepMind are a complete departure from the olden ways of predicting the future.<br>Instead of a frame-by-frame giant physics calculator, teams are training models on decades of global data, learning statistical relationships across space and time and using those to predict the next atmospheric state directly.<br>In other words, as opposed to asking, “Given physics, what happens next?” teams armed with AI are asking, “Given everything we’ve ever observed, what usually happens next?”<br>The results are astonishing: Despite being a relatively new approach, the AI-based models are already outperforming even the best conventional models. They make Al Roker look like “I love lamp” Brick Tamland.

Images from IBM, adapted by Orennia<br>The independent European Center of Medium-Range Weather Forecasts found its Artificial Intelligence Forecasting System, which integrates machine learning and AI, outperforms state-of-the-art physics-based models by...