From Folklore to AI: The Evolution of Weather Forecasting

Weather forecasting has evolved from reading cloud patterns and animal behavior to running trillion-calculation-per-second computer simulations. The first numerical weather prediction was attempted by Lewis Fry Richardson in 1922, who estimated it would take 64,000 people doing calculations by hand to keep up with global weather. The first computer forecast came in 1950 on the ENIAC machine, predicting 24-hour pressure patterns. Modern forecasting relies on ensemble modeling, running 50+ simulations with slightly different initial conditions to estimate uncertainty.

The European ECMWF model and the American GFS model are the two leading global systems, with the ECMWF generally more accurate at 3-5 day ranges. Forecast skill has improved roughly one day per decade: a 7-day forecast today is as accurate as a 5-day forecast was 20 years ago. AI is transforming the field. GraphCast, developed by DeepMind, produces 10-day forecasts in under a minute that match or exceed traditional physics-based models requiring hours of supercomputer time.

FourCastNet and Pangu-Weather achieve similar results. These AI models learn weather patterns from 40 years of historical data rather than solving physical equations. The revolution extends to nowcasting: Google DeepMinds DGMR predicts precipitation in the next 2 hours more accurately than any previous method, critical for severe storm warnings. Satellite data from 160+ satellites, radar networks, and 10,000+ weather stations feed these systems with continuous observations..