Google WeatherNext 3
NexusAi Summary: WeatherNext 3 is Google DeepMind’s global AI weather model delivering hourly, high-resolution forecasts from raw satellite data. It powers Search, Maps, and Gemini, and is available to enterprises through BigQuery, Earth Engine, Maps Platform, and Cloud Storage for decision-ready insights.
NexusAi Overview
Teams query forecast grids in BigQuery or Earth Engine, overlay layers in Maps Platform, and export data to Cloud Storage for pipelines. Hourly, satellite-native updates support monitoring fast-changing precipitation, wind, temperature, humidity, and radiation to drive scheduling, routing, energy dispatch, and risk alerts.
What it does
Designed for data-driven operators and researchers: grid and renewable planners forecasting solar and wind output; logistics and mobility teams routing fleets; insurers and risk teams monitoring severe weather; agriculture platforms planning irrigation and harvest; public-sector analysts building situational awareness; and product teams embedding live weather into applications via Maps or Cloud. Teams needing global coverage, low latency, and production-ready access will benefit most.
- Generates hourly global forecasts from satellite-native inputs for responsive, local decisions.
- Delivers 5 km temperature and humidity, 10 km wind and surface variables.
- Ships through BigQuery, Earth Engine, Maps Platform, and Cloud Storage integrations.
- Supports renewable energy operations with radiation, cloud cover, and wind fields.
- Reduces modeling overhead by removing numerical weather prediction setup and maintenance.
Highlights
Who it’s for
Access WeatherNext 3 via Google Cloud products: query forecast tables in BigQuery, process gridded assets in Earth Engine, render layers with Google Maps Platform, or pull files from Cloud Storage. Many teams start by validating historical forecasts against their KPIs, then wire hourly updates into orchestration jobs for scheduling, dispatch, pricing, or alerts. The experimental Weather Lab helps explore live global layers and track tropical cyclones for evaluation; use your national meteorological service for official warnings. With no model setup required, you can bring your own geographies, assets, and demand signals and join to WeatherNext grids to power production workflows.
NexusAi: Hourly, satellite-native forecasts deliver operational weather intelligence at global scale.
Getting started
WeatherNext 3 brings production-grade weather intelligence to where teams already work. By combining satellite-native learning, hourly cadence, and 5–10 km resolution with turnkey access through BigQuery, Earth Engine, Maps Platform, and Cloud Storage, it compresses time-to-value and lowers operational burden. For organizations needing global coverage, fast updates, and actionable variables, WeatherNext 3 provides a modern path to weather-aware planning, dispatch, and customer experiences.
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Google WeatherNext 3
Google DeepMind is an AI research and engineering organization advancing state-of-the-art models for science and real-world decision-making. Its work spans multimodal systems, robotics, and geospatial modeling, including AlphaFold and WeatherNext. DeepMind partners across Google to deliver reliable, responsible AI into products and enterprise platforms.
WeatherNext 3 is Google DeepMind’s most advanced global AI weather model. It learns directly from raw satellite imagery using an ensemble architecture to produce hourly forecasts at 5–10 km resolution. The system powers consumer experiences in Search, Maps, and Gemini, and delivers enterprise-grade access through BigQuery, Earth Engine, Google Maps Platform, and Cloud Storage. By directly leveraging satellite data, WeatherNext 3 improves accuracy, latency, and coverage, including locations sparse in ground stations. Operators can track precipitation, wind, temperature, humidity, radiation, and cloud cover to optimize renewable output, logistics, and safety decisions—without operating numerical weather prediction infrastructure. Historical forecasts and real-time operational feeds support backtesting, model fusion, and production workflows.
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