NOAA HRRR forecast, 48 hour

Spatial domain	Continental United States
Spatial resolution	3 km
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

STAC (browse)

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.

Related Datasets

NOAA HRRR analysis - Analysis data from the High-Resolution Rapid Refresh (HRRR) model operated by NOAA NWS NCEP.

Examples

	Quickstart (Github)
	Quickstart (Colab)

dynamical.org - NOAA HRRR forecast, 48 hour

Maximum temperature in a forecast

import dynamical_catalog  # dynamical-catalog>=0.5.0

ds = dynamical_catalog.open("noaa-hrrr-forecast-48-hour")
ds["temperature_2m"].sel(init_time="2025-01-01T00", x=0, y=0, method="nearest").max().compute()

Dimensions

	min	max	units
init_time	2018-07-13T12:00:00Z	Present	seconds since 1970-01-01

lead_time	0	172800	seconds

x	-2697520.143	2696479.857	m

y	-1587306.153	1586693.847	m

Variables

	units	dimensions
categorical_freezing_rain_surface (`cfrzr`) Categorical freezing rain 0=no; 1=yes	1	init_time × lead_time × y × x

categorical_ice_pellets_surface (`cicep`) Categorical ice pellets 0=no; 1=yes	1	init_time × lead_time × y × x

categorical_rain_surface (`crain`) Categorical rain 0=no; 1=yes	1	init_time × lead_time × y × x

categorical_snow_surface (`csnow`) Categorical snow 0=no; 1=yes	1	init_time × lead_time × y × x

composite_reflectivity (`refc`) Maximum/Composite radar reflectivity	dBZ	init_time × lead_time × y × x

dew_point_temperature_2m (`2d`) 2 metre dewpoint temperature	degree_Celsius	init_time × lead_time × y × x

downward_long_wave_radiation_flux_surface (`sdlwrf`) Surface downward long-wave radiation flux	W m-2	init_time × lead_time × y × x

downward_short_wave_radiation_flux_surface (`sdswrf`) Surface downward short-wave radiation flux	W m-2	init_time × lead_time × y × x

geopotential_height_cloud_ceiling (`gh`) Geopotential height	m	init_time × lead_time × y × x

percent_frozen_precipitation_surface (`cpofp`) Percent frozen precipitation	percent	init_time × lead_time × y × x

precipitable_water_atmosphere (`pwat`) Precipitable water	kg m-2	init_time × lead_time × y × x

precipitation_surface (`prate`) Precipitation rate Average precipitation rate since the previous forecast step. Units equivalent to mm/s.	kg m-2 s-1	init_time × lead_time × y × x

pressure_reduced_to_mean_sea_level (`prmsl`) Pressure reduced to MSL	Pa	init_time × lead_time × y × x

pressure_surface (`sp`) Surface pressure	Pa	init_time × lead_time × y × x

relative_humidity_2m (`2r`) 2 metre relative humidity	percent	init_time × lead_time × y × x

snow_area_fraction_surface (`snowc`) Snow cover	1	init_time × lead_time × y × x

snow_thickness_surface (`sde`) Snow depth	m	init_time × lead_time × y × x

snow_water_equivalent_surface (`sd`) Snow depth water equivalent	m	init_time × lead_time × y × x

snowfall_surface (`tsrate`) Total snowfall rate Average snowfall rate since the previous forecast step.	m s-1	init_time × lead_time × y × x

temperature_2m (`2t`) 2 metre temperature	degree_Celsius	init_time × lead_time × y × x

total_cloud_cover_atmosphere (`tcc`) Total cloud cover	percent	init_time × lead_time × y × x

wind_gust_surface (`gust`) Wind speed (gust)	m s-1	init_time × lead_time × y × x

wind_u_10m (`10u`) 10 metre U wind component	m s-1	init_time × lead_time × y × x

wind_u_80m (`80u`) 80 metre U wind component	m s-1	init_time × lead_time × y × x

wind_v_10m (`10v`) 10 metre V wind component	m s-1	init_time × lead_time × y × x

wind_v_80m (`80v`) 80 metre V wind component	m s-1	init_time × lead_time × y × x

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

Details

License

Dataset licensed under CC BY 4.0.

Attribution and citation

NOAA NWS NCEP HRRR data processed by dynamical.org from NOAA Open Data Dissemination archives.

Or NOAA HRRR from dynamical.org.

Source

The source grib files this archive is constructed from are provided by NOAA Open Data Dissemination (NODD) and accessed from the AWS Open Data Registry. Operational data is additionally accessed from NOAA NOMADS.

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. Icechunk storage generously provided by AWS Open Data.

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.