Introducing WeatherNext 3, our most advanced and accurate global weather AI model

WeatherNext 3: Our most advanced global weather AI model

Introducing WeatherNext 3, our most advanced and accurate global weather AI model

Sep 03, 2026

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Our flagship AI weather forecasting model now includes real-time satellite data, hourly refreshes, higher resolution, precise precipitation forecasting, and clean energy variables. It’s now integrated across Search, Gemini, Maps, Google Maps Platform, and Cloud.

The WeatherNext team

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Google has launched WeatherNext 3, an advanced AI model that provides more accurate, high-resolution weather forecasts by using real-time satellite data instead of traditional physics simulations. This update delivers hourly, localized predictions that are five times sharper than previous versions, offering significant improvements for agriculture, renewable energy, and daily planning. You can now access these enhanced forecasts directly through Google Search, Maps, and Gemini, or integrate the data into your own projects via Google Cloud.

Summaries were generated by Google AI. Generative AI is experimental.

Check out "Introducing WeatherNext 3," our most advanced and accurate global weather AI model yet.

We now provide hourly, high-resolution forecasts that are five times sharper than before.

Our model uses real-time satellite data to track fast-changing weather with incredible precision.

You’ll get much better rain and snow predictions, helping you plan your day confidently.

These updates are rolling out across Google apps to help everyone make better decisions.

Summaries were generated by Google AI. Generative AI is experimental.

Google just released WeatherNext 3, an AI model that makes weather forecasts much faster and more accurate. It uses live satellite data to update every hour, giving you a much sharper picture of what’s happening outside. This helps people plan better for things like storms or sunny days. It’s now powering the weather info you see in Google Search and Maps.

Summaries were generated by Google AI. Generative AI is experimental.

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Every day, the weather influences billions of decisions. Some are as simple as grabbing an umbrella before heading out the door, but others are far more consequential. Wind, rain, and extreme weather events, like heatwaves and droughts, have cascading impacts across agriculture, global supply chains, clean energy production, and national economies. In recent years, AI has revolutionized weather forecasting, using historical records to make faster and more accurate predictions than traditional methods. Yet predicting highly local and rapidly changing weather has remained a challenge. Previous models often lacked sufficient spatial resolution, and struggled to incorporate real-time weather data from sources like satellites. Today, Google DeepMind and Google Research are introducing WeatherNext 3 , the most advanced and accurate global weather model to date, according to independent live evaluations by Brightband. Our model learns directly from real-time observations, enabling it to provide timely and more localized predictions for the weather events that impact people the most. By using raw satellite data to produce a forecast every hour in high resolution, our model makes reliable forecasts accessible across Google products worldwide.

Rapid weather prediction at unprecedented resolution A forecast's utility often comes down to detail and how finely it resolves both time and space. WeatherNext 3 generates hourly forecasts at multiple spatial resolutions, maintaining physical consistency from broad global wind patterns all the way down to local topography. With WeatherNext 3, we can visualize key surface variables — like temperature and moisture — at a 5-kilometer resolution, other surface variables at 10 kilometers, and atmospheric variables, like wind speed, at 25 kilometers. Overall, this provides a global weather picture roughly five times sharper than our previous model, WeatherNext 2, which produced forecasts on a 25-kilometer grid in 6-hour increments.

Figure 1: The end-to-end WeatherNext 3 system architecture. The model ingests live 1-hour geostationary satellite mosaics alongside traditional historical analysis to feed a single, flexible Functional Generative Network (FGN) mesh transformer. It outputs dense gridded fields, discrete cyclone tracks, and predicts station-level sparse coordinates natively.

Figure 2: Comparison of 2-meter temperature forecasts over the UK. WeatherNext 2 (left) at 25-kilometer (0.25°) resolution vs. WeatherNext 3 (right) at a native 5-kilometer (0.05°) resolution. WeatherNext 3 resolves the intricate local topography, preventing the pixelated, over-smoothed thermal representations seen in older models.

Real-world data at continuous global scale WeatherNext 3's biggest leap forward is what it learns from. Most AI weather models, including WeatherNext 2, are trained on data from numerical weather prediction (NWP) models. Although useful, NWP models are complex, supercomputer-driven physics simulations that carry a six-hour data lag. This lag can lead to biases for fast-changing variables like rain or surface temperature. By ingesting a mosaic of live, global geostationary satellite data, our new model gains a rich, continuously updating view of the atmosphere. This allows the model to generate a new forecast every hour, each one grounded in the most recent satellite observations available, at up to 5-kilometer resolution. This is important because critical weather develops fast. When storms, fronts, or precipitation systems materialize suddenly, our rapid update cycle and higher resolution provides earlier, more detailed insights needed to help drive an effective response. Some variables, like temperature and humidity, can fluctuate dramatically over just a few kilometers, which is particularly relevant for communities near coastlines, valleys, or mountain ranges. Traditional models struggle here because they train on representations of the atmosphere that lack detail and miss...