WeatherNext 3

WeatherNext 3 is Google's most advanced global weather forecasting model, developed by Google DeepMind and Google Research. By ingesting live geostationary satellite observations as a direct model input, WeatherNext 3 is initialized every hour, producing forecasts at up to 0.05° (5 km) spatial resolution with hourly timesteps. It also trains directly on real-world weather station data, producing output that better matches what is measured on the ground. For a detailed technical overview, see the research paper, the research and benchmarks page, and the announcement blog post.

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At a glance

Attribute Details
Release Date August 2026
Coverage Global
Spatial Resolution 0.05° (~5 km) stations · 0.1° (~10 km) gridded surface · 0.25° (~25 km) pressure levels
Temporal Resolution 1 hour (hourly timesteps)
Forecast Horizon 15 days (360 hours) for 6-hourly cycles (00, 06, 12, 18 UTC)
48 hours for interim hourly runs (01–05, 07–11, 13–17, 19–23 UTC)
Initialization Frequency Every hour (24 inits per day)
Ensemble Members 64
Architecture Functional Generative Network (FGN) mesh transformer (paper)
Inputs Live geostationary satellite mosaics + ECMWF HRES analysis
Training Data ERA5 / HRES-fc0, IMERG, station observations, geostationary satellite mosaics
Historical Forecasts 2026 (for backtesting and evaluation)
Historical data is being backfilled for 2024, 2025

Key features

  • Real-time satellite input: WeatherNext 3 ingests a live global geostationary satellite mosaic as a direct model input, allowing it to be initialized every hour. This hourly refresh provides additional lead time when sudden weather fronts, storms, or precipitation systems develop rapidly.

  • Multi-resolution output: Forecasts are generated at multiple spatial resolutions from a single forward pass:

    • 0.05° (~5 km): Station-trained 2m temperature and dew point.
    • 0.1° (~10 km): Gridded surface wind (10m and 100m), pressure, sea surface temperature, cloud layers, solar radiation fields, 1-hour IMERG precipitation, and 1-hour experimental satellite-radar precipitation.
    • 0.25° (~25 km): 3D atmospheric pressure levels (available in 6-hourly cycles: 00, 06, 12, 18 UTC).

  • Ground-truth station training: Instead of training exclusively on reanalysis grids, WeatherNext 3 trains dedicated observational heads on raw weather station observations, producing 0.05° output that closely matches what is measured on the ground.

  • Precipitation: WeatherNext 3 trains against three distinct precipitation data sources: ECMWF reanalysis, NASA's satellite-based IMERG, and Google's own satellite-radar precipitation reanalysis. This achieves up to a 50% reduction in Brier score and CRPS compared to numerical weather prediction baselines when evaluated against IMERG observations.

  • Clean energy variables: Outputs comprehensive parameters for renewable energy applications: 100-metre wind speeds for turbine-height forecasting, full cloud layer distributions, and complete solar irradiance components (SSRD, FDIR).

Forecast cycles and horizons

WeatherNext 3 operates across distinct synoptic and interim initialization cycles with varying forecast horizons and available variable scopes. Explore initialization cycles and calculate coverage windows below:

Supported variables

WeatherNext 3 produces operational gridded surface forecasts, high-resolution station observational head outputs, and 3D upper-air atmospheric fields.

1. Gridded surface variables (0.1° resolution)

1-hour step variables are available across all initialization runs (6-hourly and interim hourly) on BigQuery, Earth Engine, and Google Cloud Storage. 6-hour accumulation variables (surface_solar_radiation_downwards_6hr, total_sky_direct_solar_radiation_at_surface_6hr, total_precipitation_6hr) are available only in 6-hourly synoptic inits exclusively on Google Cloud Storage (Full Ensemble Zarr).

Variable Name Description Units Resolution
Temperature & Humidity
temperature_2m 2 metre air temperature K 0.1° (~10 km)
dewpoint_temperature_2m 2 metre dew point temperature K 0.1° (~10 km)
Wind (Surface & Turbine Hub Height)
wind_speed_10m 10 metre scalar wind speed (derived: √(u2 + v2)) m/s 0.1° (~10 km)
wind_speed_100m 100 metre scalar wind speed (derived: √(u2 + v2)) m/s 0.1° (~10 km)
u_component_of_wind_10m 10 metre U (eastward) wind component m/s 0.1° (~10 km)
v_component_of_wind_10m 10 metre V (northward) wind component m/s 0.1° (~10 km)
u_component_of_wind_100m 100 metre U (eastward) wind component m/s 0.1° (~10 km)
v_component_of_wind_100m 100 metre V (northward) wind component m/s 0.1° (~10 km)
Solar Irradiance (Renewable Energy)
surface_solar_radiation_downwards_1hr 1-hour surface downward solar radiation (SSRD / GHI) J/m2 0.1° (~10 km)
total_sky_direct_solar_radiation_at_surface_1hr 1-hour total-sky direct solar radiation (FDIR / Direct Beam) J/m2 0.1° (~10 km)
surface_solar_radiation_downwards_6hr 6-hour surface downward solar radiation (SSRD / GHI) (6-hourly cycles only on GCS Zarr) J/m2 0.1° (~10 km)
total_sky_direct_solar_radiation_at_surface_6hr 6-hour total-sky direct solar radiation (FDIR) (6-hourly cycles only on GCS Zarr) J/m2 0.1° (~10 km)
Precipitation
total_precipitation_1hr 1-hour accumulated precipitation (model native) m 0.1° (~10 km)
imerg_tp_1hr IMERG satellite-calibrated 1-hour precipitation m 0.1° (~10 km)
experimental_tp_1hr Experimental satellite-radar 1-hour precipitation m 0.1° (~10 km)
total_precipitation_6hr 6-hour accumulated precipitation (6-hourly cycles only on GCS Zarr) m 0.1° (~10 km)
Cloud Layers & Cover
total_cloud_cover Total atmospheric column cloud cover fraction 0–1 0.1° (~10 km)
high_cloud_cover High cloud cover fraction 0–1 0.1° (~10 km)
medium_cloud_cover Medium cloud cover fraction 0–1 0.1° (~10 km)
low_cloud_cover Low cloud cover fraction 0–1 0.1° (~10 km)
Pressure & Ocean
mean_sea_level_pressure Mean sea level pressure Pa 0.1° (~10 km)
sea_surface_temperature Sea surface temperature K 0.1° (~10 km)

2. Station point variables (0.05° resolution)

Trained directly against surface weather station measurements. Available across all inits on BigQuery, Earth Engine, and Google Cloud Storage.

Variable Name Description Units Resolution
station_head_temperature_2m 2 metre air temperature at station locations K 0.05° (~5 km)
station_head_dewpoint_temperature_2m 2 metre dew point temperature at station locations K 0.05° (~5 km)

3. Atmospheric variables (0.25° resolution, pressure levels)

Available across 13 vertical pressure levels (50, 100, 150, 200, 250, 300, 400, 500, 600, 700, 850, 925, 1000 hPa) for 6-hourly inits (00, 06, 12, 18 UTC) exclusively on Google Cloud Storage (Full Ensemble Zarr).

Variable Pattern Description Units Resolution
geopotential_{level} Geopotential (Φ; divide by 9.80665 m/s2 for geopotential height in meters) m2/s2 0.25° (~25 km)
specific_humidity_{level} Specific humidity kg/kg 0.25° (~25 km)
temperature_{level} Air temperature K 0.25° (~25 km)
u_component_of_wind_{level} U (eastward) wind component m/s 0.25° (~25 km)
v_component_of_wind_{level} V (northward) wind component m/s 0.25° (~25 km)
vertical_velocity_{level} Vertical velocity (ω) Pa/s 0.25° (~25 km)

Replace {level} with the pressure level in hPa (for example, geopotential_500 represents geopotential at 500 hPa). In unflattened raw Zarr stores, variables are indexed along the level coordinate dimension.

How to access WeatherNext 3 data

WeatherNext 3 forecast data is available through BigQuery, Earth Engine, and Google Cloud Storage. Visit the quick start guide to compare access options, request allowlist access, and run starter queries.

Migrating from WeatherNext 2

Key variable name changes:

WeatherNext 2 WeatherNext 3
2m_temperature temperature_2m
2m_dewpoint_temperature dewpoint_temperature_2m
10m_u_component_of_wind u_component_of_wind_10m
10m_v_component_of_wind v_component_of_wind_10m
100m_u_component_of_wind u_component_of_wind_100m
100m_v_component_of_wind v_component_of_wind_100m

Variables without a height indicator (for example, mean_sea_level_pressure, total_cloud_cover, geopotential) retain the same names across both versions.

Model lineage & earlier versions

Google has invested in machine learning weather research for many years, producing a series of models that have consistently pushed the state of the art:

  • WeatherNext 3 (August 2026): Flagship operational model. FGN mesh transformer with live satellite assimilation, multi-resolution output (0.05° stations, 0.1° surface, 0.25° pressure levels), and hourly initialization. Recommended for all new projects.
  • WeatherNext 2 (June 2025): Ensemble model based on the Functional Generative Network (FGN) architecture. 0.25° resolution, 6-hourly timesteps, 64 ensemble members.
  • WeatherNext 1 Gen (Dec 2024): Probabilistic diffusion-based ensemble model (GenCast). 0.25° resolution, 12-hourly timesteps, 50 ensemble members.
  • WeatherNext 1 Graph (Nov 2023): Deterministic Graph Neural Network model (GraphCast). 0.25° resolution, 6-hourly timesteps.

Older models remain available for research and benchmarking.

Terms of service and disclaimers

WeatherNext 3 is an automated, experimental AI forecasting system under active research. Predictions and model outputs are provided "as-is" for informational and research purposes and do not constitute official weather forecasts, watches, or warnings. Users are responsible for their use of model outputs and data. For the protection of life and property, never rely on WeatherNext 3 as a sole source of information and always defer to official alerts and advisories from your national meteorological service and local emergency authorities.

Data licensing and terms of use

For complete terms, liability provisions, and acceptable use restrictions, see the Terms of Service and Disclaimers page.

See the Glossary for definitions of meteorological terms, data formats, and Google Cloud platforms.