Reading the press release for Google DeepMind’s WeatherNext 2, I wondered: have weather forecasts actually improved over the past years?
Turns out they have, dramatically. A four-day forecast today matches the accuracy of a one-day forecast from 30 years ago. Hurricane track errors that once exceeded 400 nautical miles for 72-hour forecasts now sit below 80 miles. The European Centre for Medium-Range Weather Forecasts reports three-day forecasts now reach 97% accuracy, with seven-day forecasts approaching that threshold.
Google’s new model accelerates this trend. The hurricane model performed remarkably well this season when tested against actual paths. WeatherNext 2 generates forecasts 8 times faster than its predecessor with resolution down to one hour. Each prediction takes under a minute on a single TPU compared to hours on a supercomputer using physics-based models. The speed comes from a smarter training approach. WeatherNext 2 (along with neuralgcm) uses a continuous ranked probability score (CRPS) objective rather than the L2 losses common in earlier neural weather models. The method adds random noise to parameters and trains the model to minimize L1 loss while maximizing differences between ensemble members with different noise initializations.
This matters because L2 losses blur predictions when models roll out autoregressively over multiple time steps. Spatial features degrade and the model truncates extremes. Models trained with L2 losses struggle to forecast high-impact extreme weather at moderate lead times. The CRPS objective preserves the sharp spatial features and extreme values needed for cyclone tracking and heat wave prediction. These improvements stem from better satellite and ground station data, faster computers running higher-resolution models, and improved communication through apps and online services. AI systems like WeatherNext 2 and Pangu-Weather (which performs forecasts up to 10,000 times faster than traditional methods) are accelerating progress that has been building for decades.