Catalog > NOAA HRRR > NOAA HRRR forecast, 48 hour

NOAA HRRR forecast, 48 hour

updating
Spatial domain Continental United States
Spatial resolution 3km
Time domain Forecasts initialized 2018-07-13 12:00:00 UTC to Present
Time resolution Forecasts initialized every 6 hours.
Forecast domain Forecast lead time 0-48 hours ahead
Forecast resolution Hourly

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The High-Resolution Rapid Refresh (HRRR) is a NOAA real-time 3-km resolution, hourly updated, cloud-resolving, convection-allowing atmospheric model, initialized by 3km grids with 3km radar assimilation. Radar data is assimilated in the HRRR every 15 min over a 1-h period adding further detail to that provided by the hourly data assimilation from the 13km radar-enhanced Rapid Refresh.

This dataset is an archive of past and present HRRR forecasts. Forecasts are identified by an initialization time (init_time) denoting the start time of the model run. Each forecast has an hourly forecast step along the lead_time dimension. This dataset contains only the 00, 06, 12, and 18 hour UTC initialization times which produce the full length, 48 hour forecast.

This dataset uses the native HRRR Lambert Conformal Conic projection, with spatial indexing along the x and y dimensions. The example notebook shows how to use the embedded spatial reference to select geographic areas of interest.

Examples

Open notebook in github
Open notebook in colab
dynamical.org - NOAA HRRR forecast, 48 hour
Maximum temperature in a forecast
import xarray as xr  # xarray>=2025.1.2 and zarr>=3.0.8 for zarr v3 support

ds = xr.open_zarr("https://data.dynamical.org/noaa/hrrr/forecast-48-hour/[email protected]")
ds["temperature_2m"].sel(init_time="2025-01-01T00", x=0, y=0, method="nearest").max().compute()

Dimensions

min max units
init_time 2018-07-13T12:00:00 Present seconds since 1970-01-01
lead_time 0 days 00:00:00 2 days 00:00:00 seconds
x -2700000 2700000 m
y -1600000 1600000 m

Variables

units dimensions
categorical_freezing_rain_surface 0=no; 1=yes init_time × lead_time × y × x
categorical_ice_pellets_surface 0=no; 1=yes init_time × lead_time × y × x
categorical_rain_surface 0=no; 1=yes init_time × lead_time × y × x
categorical_snow_surface 0=no; 1=yes init_time × lead_time × y × x
composite_reflectivity dBZ init_time × lead_time × y × x
downward_long_wave_radiation_flux_surface W/(m^2) init_time × lead_time × y × x
downward_short_wave_radiation_flux_surface W/(m^2) init_time × lead_time × y × x
expected_forecast_length seconds init_time
geopotential_height_cloud_ceiling gpm init_time × lead_time × y × x
ingested_forecast_length seconds init_time
latitude degrees_north y × x
longitude degrees_east y × x
percent_frozen_precipitation_surface % init_time × lead_time × y × x
precipitable_water_atmosphere kg/(m^2) init_time × lead_time × y × x
precipitation_surface mm/s init_time × lead_time × y × x
pressure_reduced_to_mean_sea_level Pa init_time × lead_time × y × x
pressure_surface Pa init_time × lead_time × y × x
relative_humidity_2m % init_time × lead_time × y × x
temperature_2m C init_time × lead_time × y × x
total_cloud_cover_atmosphere % init_time × lead_time × y × x
valid_time seconds since 1970-01-01 init_time × lead_time
wind_u_10m m/s init_time × lead_time × y × x
wind_u_80m m/s init_time × lead_time × y × x
wind_v_10m m/s init_time × lead_time × y × x
wind_v_80m m/s init_time × lead_time × y × x

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

Details

Sources

The source grib files this archive is contructed from are provided by NOAA Open Data Dissemniation (NODD) and accessed from the AWS Open Data Registry.

Data availability

Forecasts initialized through 2020-12-02T06 UTC include data only for the first 36 hours; steps 37–48 are filled with NaNs. Starting with the 2020-12-02T12 UTC initialization, forecasts cover the full 48 hours.

Storage

Storage for this dataset is generously provided by Source Cooperative, a Radiant Earth initiative.

Compression

The data values in this dataset have been rounded in their binary floating point representation to improve compression. See Klöwer et al. 2021 for more information on this approach. The exact number of rounded bits can be found in our reformatting code.

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