The company’s AI-enhanced forecasts have proven themselves to be quite accurate so far. AI weather models have also been able to make predictions faster and more efficiently than conventional physics-based models. Until now, Google’s work in this area has been mostly experimental. Now, it’s making those predictions a selling point for Google products and services.

“We’re taking it out of the lab and really putting it into the hands of users in more ways than we have before and sort of shedding off the experimental kind of designation because we have confidence that our forecasts are really quite effective and quite useful,” Peter Battaglia, senior director of research and sustainability at Google DeepMind, said in a briefing with reporters.

The new AI model, WeatherNext 2, can generate forecasts eight times faster than Google’s previous model, and is also more accurate in predicting 99.9 percent of variables like temperature or wind. WeatherNext 2 can pump out hundreds of potential outcomes from a particular starting point. It takes less than a minute using one of Google’s TPU chips to make a prediction, which the company says would typically take several hours to accomplish using physics-based models on a supercomputer.

Those conventional models are computationally intensive because they’re essentially attempting to recreate the complicated physics of the atmosphere to produce forecasts. AI models, in contrast, try to discern patterns out of historical weather data in order to predict future outcomes.

Google was able to streamline its process by using a strategy it calls a Functional Generative Network (FGN) in WeatherNext 2. Older AI weather models still required repeated processing to generate one forecast. FGN is more efficient because it incorporates noise — or targeted randomness — into the model each time it’s provided an input so that WeatherNext 2 can generate many different possible outcomes in a single step.

The advancements allow WeatherNext 2 to make predictions up to 15 days in advance and generate hourly forecasts. Google’s banking on that appealing to enterprise customers as well as individual consumers.

“We found that energy, agriculture, transportation, logistics, and customers in many other industries are quite interested in these one-hour steps. It helps them make more precise decisions relating to things that affect their business,” Akib Uddin, a product manager at Google Research, said on the call.

Aside from adding WeatherNext 2 to Maps, Search, Gemini, and Pixel Weather, Google is also offering an early access program for customers interested in custom modeling. The forecast data is also available in Google Earth Engine for geospatial analysis and BigQuery for large-scale data analysis.