Google's WeatherNext 3 uses live satellite data for sharper forecasts
Google has unveiled an upgraded AI weather model, WeatherNext 3, that incorporates real-time satellite observations to generate global forecasts with five times the resolution of its predecessor. The system aims to improve predictions for rain and snowfall by learning from fresher observational data rather than relying solely on physics-based simulations. According to the company, this approach allows hourly updates based on the latest satellite feeds.
WeatherNext 3 marks a shift from physics-based simulation toward pattern recognition trained on fresher observational feeds. The model's hourly update cycle and five-kilometer grid represent a substantial jump from the previous six-hour, 25-kilometer output, enabling sharper tracking of fast-moving precipitation systems. The system also addresses a known weakness in global forecasting: sparse ground instrumentation in regions outside the US and Europe, where satellite-derived data can compensate for missing rain gauges. Google additionally tailored the model for renewable energy planning, including wind-speed predictions at turbine height, reflecting the company's growing data-center power demands alongside broader energy infrastructure needs.
This advancement could meaningfully improve early warning for severe rain and snow events, particularly in underserved regions where ground monitoring is limited. More accurate precipitation forecasts may help farmers, disaster-response agencies, and energy operators plan around weather risks, while renewable-energy forecasting could support grid stability as wind and solar adoption grows. However, reliance on proprietary AI models raises questions about equitable access to forecasting tools, since public agencies and developing nations may depend on Google's platform for critical weather data.