- Google has launched a new AI-powered forecasting model called WeatherNext 2.
- WeatherNext 2 delivers faster, more accurate, higher-resolution forecasts by simulating hundreds of possible weather outcomes in less than a minute.
- WeatherNext 2 now powers forecasts in Google Search, Gemini, Pixel and Maps
Google reviews your weather forecast with AI that thinks in probabilities. Rather than new radar towers or satellite launches, the new WeatherNext 2 AI-based forecasting model developed by Google DeepMind and Google Research delivers results up to eight times faster than traditional systems and can predict hundreds of possible weather outcomes from a single starting point, at higher resolution and with better accuracy than its predecessors.
WeatherNext 2 is integrated into many of Google’s most popular platforms, including Google Search, Gemini, Pixel Weather, and Maps, with wider deployment coming soon through the Google Maps Platform Weather API.
What makes this upgrade more than just a backend refresh is the scale of its ambition. WeatherNext 2 is designed to account for uncertainty in unusual ways. Where older models could spit out a single most likely outcome, WeatherNext 2 can generate hundreds of potential futures, allowing forecasters and you to see a full range of possibilities.
This also means your forecast might not just say “Rain, 40% chance” but instead show several consistent outcomes for your afternoon walk, with a better idea of what might actually happen and when.
Look on it
WeatherNext 2 uses what Google calls a Functional Generative Network (FGN). The model does not rely solely on completed forecasts or complete weather systems; instead, it is trained on individual, standalone variables such as temperature, wind speed, and humidity. The model then determines how these variables interact to create “seals,” complex, real-world patterns such as storm fronts, heat waves, or regional wind changes.
Google claims that this architecture allows WeatherNext 2 to outperform even its previous best-in-class model by providing more accurate forecasts for 99.9% of variables for up to 15 days.
It is also much faster at making its predictions, completing a full forecast in less than a minute. By comparison, traditional physics-based predictions can take hours on a supercomputer. This efficiency allows for more frequent and more detailed forecast updates.
Face the future
After considerable testing, Google Gemini will begin showing forecasts based on WeatherNext 2 results, just like Google Maps. The average person could theoretically reap many benefits from the upgrade. Weather forecasts are one of those invisible systems that underpin an extraordinary range of decisions. Make it more specific and you’ll remove a thousand little sources of stress from people’s days.
There are also larger implications. For example, with smarter weather forecasts, renewable energy providers can better estimate wind and solar production, and emergency services can respond more accurately when forecasts capture uncertainty rather than mask it.
This emphasis on uncertainty is essential. Forecasting is not about being perfectly right, but about preparing wisely for what might happen. By providing a range of physically realistic and interconnected scenarios, WeatherNext 2 takes forecasting towards something more strategic.
It may not solve the chaos caused by climate change and the natural disasters that come with it, but it could be a boon for those planning to better cope with it. AI-driven forecasting is starting to look like critical infrastructure.
Better data means better decisions. And when it comes to weather, that means more than just helping us decide what kind of coat we need in the morning.
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