At Belvedere Labs we spend a lot of time trying out new AI capabilities, wherever they show up. Weather caught our eye because it makes an unusually clean test. The forecasts are public, the answers arrive a few days later from official thermometers and rain gauges, and two of the world's biggest forecasting agencies now run an AI model alongside their traditional one.
So we gathered the forecasts four models made for eight US cities from July to October 2026 and compared each one with what actually happened. This article is a short summary of what we noticed. The full data set lives in our interactive forecast explorer, where you can dig into any city, day or model.
Two Ways to Predict the Weather
Traditional forecasts come from physics. The model splits the atmosphere into millions of 3D boxes and applies the equations for air movement, heat and moisture, stepping forward a few minutes at a time until it reaches next week. That takes a supercomputer. We looked at the two best-known examples: IFS from the European centre ECMWF, and GFS from the US agency NOAA.
AI models take a different route. They are neural networks trained on decades of past weather, and they learn how the atmosphere tends to change over six hours. Repeating that step gets them a week or more ahead. Training one takes a lot of computing, but running it is far cheaper than running a physics model. ECMWF estimates its AI model uses about 1,000 times less energy per forecast, and Google DeepMind reported that its GraphCast model produces a 10-day forecast in under a minute on a single machine. The AI models still start from today's weather as measured and prepared by the traditional systems, so the two approaches work together. ECMWF made its AI model, AIFS, operational in early 2025, and NOAA followed later that year with AIGFS, which builds on GraphCast.
