Google WeatherNext 3 forecast, 15 day, 0.1 degree, virtual

Name: Google WeatherNext 3 forecast, 15 day, 0.1 degree, virtual
Creator: dynamical.org

map-optimized · low-latency

ID	`google-weathernext3-forecast-15-day-0-1-degree-virtual`
Spatial domain	Global
Spatial resolution	0.1 degrees (~10km)
Time domain	Forecasts initialized 2026-01-01 00:00:00 UTC to Present
Time resolution	Forecasts initialized every 6 hours
Forecast domain	Forecast lead time 1-360 hours ahead
Forecast resolution	Hourly

STAC (browse) · validation report

WeatherNext 3 is Google's global probabilistic weather forecasting model. It combines atmospheric analyses with satellite observations and produces forecasts trained against gridded fields and weather station observations.

This data product provides 15-day forecasts of gridded surface weather fields. Google's precomputed ensemble mean and percentiles are selected along the statistic dimension. The spatial dimensions y and x hold latitude and 0-360 degree longitude.

Related Datasets

Examples

	WeatherNext 3 forecasts, virtual (Github)
	WeatherNext 3 forecasts, virtual (Colab)

import dynamical_catalog  # dynamical-catalog>=1.0.0

ds = dynamical_catalog.open("google-weathernext3-forecast-15-day-0-1-degree-virtual", chunks=None)
ds["temperature_2m"].sel(init_time="2026-07-01T00", lead_time="24h", statistic="mean", y=slice(25, 55), x=slice(230, 300))

Google WeatherNext 3 forecast, 15 day, 0.1 degree, virtual · Ensemble mean temperature

import icechunk
import pystac
import xarray as xr

catalog = pystac.Catalog.from_file("https://stac.dynamical.org/catalog.json")
collection = catalog.get_child("google-weathernext3-forecast-15-day-0-1-degree-virtual")
asset = collection.assets["icechunk-https"]

authorize = icechunk.containers_credentials({"https://wn.dynamical.org/chunks/": icechunk.Credentials.HttpAccess()})
repo = icechunk.Repository.open(
    icechunk.http_storage(asset.href),
    authorize_virtual_chunk_access=authorize,
)
session = repo.readonly_session("main")

ds = xr.open_zarr(session.store, chunks=None)
ds["temperature_2m"].sel(init_time="2026-07-01T00", lead_time="24h", statistic="mean", y=slice(25, 55), x=slice(230, 300))

Google WeatherNext 3 forecast, 15 day, 0.1 degree, virtual · Ensemble mean temperature

Explore

Dimensions

	min	max	units
init_time	2026-01-01T00:00:00Z	Present	seconds since 1970-01-01

lead_time	3600	1296000	seconds

statistic		Present	1

x	0	359.9	degree_east

y	-90	90	degree_north

Variables

Access a variable by its name (the bold identifier, e.g. ds["cloud_cover_high"]).

Dimensions: init_time × statistic × lead_time × y × x

variable	units
cloud_cover_high High cloud cover (`hcc`)	percent
cloud_cover_low Low cloud cover (`lcc`)	percent
cloud_cover_medium Medium cloud cover (`mcc`)	percent
dew_point_temperature_2m 2 metre dewpoint temperature (`2d`)	degree_Celsius
downward_direct_short_wave_radiation_flux_surface Surface direct short-wave radiation flux (`aswdir_s`)Average flux since the previous forecast step.	W m-2
downward_short_wave_radiation_flux_surface Surface downward short-wave radiation flux (`sdswrf`)Average flux since the previous forecast step.	W m-2
precipitation_experimental_surface Precipitation rate (`prate`)Average precipitation rate since the previous forecast step. Units equivalent to mm/s. WeatherNext 3 forecast of 1-hour precipitation from its experimental satellite-radar precipitation head; a model forecast, not an observation.	kg m-2 s-1
precipitation_imerg_surface Precipitation rate (`prate`)Average precipitation rate since the previous forecast step. Units equivalent to mm/s. WeatherNext 3 forecast of 1-hour precipitation calibrated to IMERG satellite precipitation; a model forecast, not an observation.	kg m-2 s-1
precipitation_surface Precipitation rate (`prate`)Average precipitation rate since the previous forecast step. Units equivalent to mm/s. WeatherNext 3 model-native 1-hour precipitation.	kg m-2 s-1
pressure_reduced_to_mean_sea_level Pressure reduced to MSL (`prmsl`)	Pa
sea_surface_temperature Sea surface temperature (`sst`)NaN over land where sea surface temperature does not apply.	degree_Celsius
temperature_2m 2 metre temperature (`2t`)	degree_Celsius
total_cloud_cover_atmosphere Total cloud cover (`tcc`)	percent
wind_speed_100m 100 metre wind speed (`100si`)The statistic of ensemble wind speed, not derived from the u/v statistics.	m s-1
wind_speed_10m 10 metre wind speed (`10si`)The statistic of ensemble wind speed, not derived from the u/v statistics.	m s-1
wind_u_100m 100 metre U wind component (`100u`)	m s-1
wind_u_10m 10 metre U wind component (`10u`)	m s-1
wind_v_100m 100 metre V wind component (`100v`)	m s-1
wind_v_10m 10 metre V wind component (`10v`)	m s-1

Don't see what you're looking for? Let us know at [email protected].

Details

License

Dataset licensed under GDM Real-Time Weather Forecasting Experimental Data Terms of Use for Real-Time Data & Forecasts and CC BY 4.0 for Historical Data.

Attribution and citation

Google WeatherNext 3 forecast data processed by dynamical.org from Google's WeatherNext 3 experimental data. Copyright 2024-6 Google LLC. © 2024-6 Google LLC, whose machine learning models were used to create the experimental data made available under the following licence terms https://storage.googleapis.com/weathernext-public/terms-of-use.pdf. This data is intended for experimental modelling only and is not intended, validated, or approved for real world use.

Or Google WeatherNext 3 from dynamical.org.

Source

This data product is built from Google's WeatherNext 3 statistics archive. Google supplies the mean and the 10th, 25th, 50th, 75th and 90th percentiles of its ensemble; these are exposed as mean, p10, p25, p50, p75 and p90 on the statistic dimension. Individual ensemble members are not included. Google's attributions and acknowledgements list third-party materials whose terms may also apply.

Modifications. dynamical.org restructures the source into an Icechunk virtual data product with dynamical.org's variable and dimension names and converts units as data is read. Temperatures are converted from kelvin to degrees Celsius, cloud fractions to percent, hourly precipitation amounts to rates in kg m⁻² s⁻¹, and hourly solar energy to flux in W m⁻².

Data availability

This data product starts with the 2026-01-01T00 UTC initialization and is updated four times a day. Forecasts include steps whose valid time is in the future. Forecasts are initialized at 00, 06, 12 and 18 UTC. Recent initializations may be incomplete while source data arrives; unpublished steps read as NaN.

Chunks

This dataset is stored in Zarr format, which splits each variable into a grid of chunks — the smallest unit read from storage. When possible, aligning your reads with this dataset's chunk grid can significantly improve data access speed.

The element count and coordinate span of this dataset:

dimension	chunk
init_time	1 (6 hours)
statistic	1
lead_time	1 (1 hour)
y	1801 (180.1°)
x	3600 (360°)
uncompressed	24.7 MiB

Validation report

Review the validation report for variable availability, missing data, known quirks, fill values, value distributions, and sample plots.