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 |
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) |
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
Access a variable by its name (the bold identifier, e.g. ds["categorical_freezing_rain_surface"]).
Dimensions: init_time × lead_time × y × x
| variable | units |
|---|---|
categorical_freezing_rain_surface
Categorical freezing rain (cfrzr)0=no; 1=yes
|
1 |
categorical_ice_pellets_surface
Categorical ice pellets (cicep)0=no; 1=yes
|
1 |
categorical_rain_surface
Categorical rain (crain)0=no; 1=yes
|
1 |
categorical_snow_surface
Categorical snow (csnow)0=no; 1=yes
|
1 |
composite_reflectivity
Maximum/Composite radar reflectivity (refc)
|
dBZ |
dew_point_temperature_2m
2 metre dewpoint temperature (2d)
|
degree_Celsius |
downward_long_wave_radiation_flux_surface
Surface downward long-wave radiation flux (sdlwrf)
|
W m-2 |
downward_short_wave_radiation_flux_surface
Surface downward short-wave radiation flux (sdswrf)
|
W m-2 |
geopotential_height_cloud_ceiling
Geopotential height (gh)
|
m |
percent_frozen_precipitation_surface
Percent frozen precipitation (cpofp)
|
percent |
precipitable_water_atmosphere
Precipitable water (pwat)
|
kg m-2 |
precipitation_surface
Precipitation rate (prate)Average precipitation rate since the previous forecast step. Units equivalent to mm/s.
|
kg m-2 s-1 |
pressure_reduced_to_mean_sea_level
Pressure reduced to MSL (prmsl)
|
Pa |
pressure_surface
Surface pressure (sp)
|
Pa |
relative_humidity_2m
2 metre relative humidity (2r)
|
percent |
snow_area_fraction_surface
Snow cover (snowc)
|
1 |
snow_thickness_surface
Snow depth (sde)
|
m |
snow_water_equivalent_surface
Snow depth water equivalent (sd)
|
m |
snowfall_surface
Total snowfall rate (tsrate)Average snowfall rate since the previous forecast step.
|
m s-1 |
temperature_2m
2 metre temperature (2t)
|
degree_Celsius |
total_cloud_cover_atmosphere
Total cloud cover (tcc)
|
percent |
wind_gust_surface
Wind speed (gust) (gust)
|
m s-1 |
wind_u_10m
10 metre U wind component (10u)
|
m s-1 |
wind_u_80m
80 metre U wind component (80u)
|
m s-1 |
wind_v_10m
10 metre V wind component (10v)
|
m s-1 |
wind_v_80m
80 metre V wind component (80v)
|
m s-1 |
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.
Chunks & shards
This dataset is stored in Zarr format, which splits each variable into a grid of chunks — the smallest unit read from storage. Chunks are grouped into larger shards (the objects actually written to storage), which keeps the object count manageable for long-archive datasets. 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 | shard |
|---|---|---|
| init_time | 1 (6 hours) | 1 (6 hours) |
| lead_time | 49 (49 hours) | 49 (49 hours) |
| y | 265 (795 km) | 1060 (3177 km) |
| x | 300 (900 km) | 1800 (5397 km) |
| uncompressed | 14.9 MiB | 356.6 MiB |
The same values are published in the dynamical-org:chunking field of this dataset's STAC collection metadata.
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.