HRRR Archive

Archived forecasts from NOAA’s High Resolution Rapid Refresh (HRRR) are available via AWS.

Currently, data from the following grib typeOfLevel filters are available:

• isobaricInhPa see available pressure levels below

• surface, where variables with stepType of accum or avg are prefixed in ufs2arco with those labels (e.g., instead of tp for total precipitation, look for accum_tp).

• heightAboveGround, where we append the height to any variables that do not have the height in their name (e.g., u at level=80 gets renamed to u80)

Note that there are some variables are available during some years but not others. For now, only variables that are available during the entirety of 2015-2024 are available.

Available Pressure Levels

Warning

Not all variables are available at all of these levels. Eventually, we hope to document what’s available for each variable, but until then, go for trial and error (unavailable levels will be filled with NaNs), or refer to the original data source links above.

Available Pressure Levels (hPa)

50	75	100	125	150
175	200	225	250	275
300	325	350	375	400
425	450	475	500	525
550	575	600	625	650
675	700	725	750	775
800	825	850	875	900
925	950	975	1000

Available Variables

Note

There are some variables are available during some years but not others. For now, only variables that are available during the entirety of 2015-2024 are available.

Variables from HRRR available in ufs2arco

Variable	Long Name
accum_sdwe	Water equivalent of accumulated snow depth (deprecated) accumulated over forecast
accum_tp	Total Precipitation accumulated over forecast
blh	Boundary layer height
cape	Convective available potential energy
cfrzr	Categorical freezing rain
cicep	Categorical ice pellets
cin	Convective inhibition
cpofp	Percent frozen precipitation
crain	Categorical rain
csnow	Categorical snow
d2m	2 metre dewpoint temperature
gh	Geopotential height
gust	Wind speed (gust)
lsm	Land-sea mask
max_10si	Time-maximum 10 metre wind speed
mslma	MSLP (MAPS System Reduction)
orog	Orography
prate	Precipitation rate
prmsl	Pressure reduced to MSL
q	Specific humidity
refc	Maximum/Composite radar reflectivity
refd1000	1000 metre Derived radar reflectivity
refd4000	4000 metre Derived radar reflectivity
sde	Snow depth
sdswrf	Surface downward short-wave radiation flux
sdwe	Water equivalent of accumulated snow depth (deprecated)
sh2	2 metre specific humidity
siconc	Sea ice area fraction
snowc	Snow cover
sp	Surface pressure
t	Temperature
t2m	2 metre temperature
tcc	Total Cloud Cover
t_surface	Temperature at surface
u	U component of wind
u10	10 metre U wind component
u80	80 metre U component of wind
v	V component of wind
v10	10 metre V wind component
v80	80 metre V component of wind
veril	Vertically-integrated liquid
vgtyp	Vegetation Type
vis	Visibility
w	Vertical velocity

Temporally Accumulated Variables

Variables that are accumulated over a specific period have been prepended with "accum_". To be specific, reading these fields with xarray + cfgrib might look something like this:

import xarray as xr
ds = xr.open_dataset(
    "mygribfile.grib2",
    engine="cfgrib",
    decode_timedelta=True,
    filter_by_keys={
        "typeOfLevel": "surface",
        "stepType": "accum",
    },
)

All variable names with this stepType are prepended with "accum_" within ufs2arco (see e.g., "accum_tp" in the table above).

For these variables, an additional option can be provided to read different accumulation periods. For example, if we were reading data from forecast hour 6, we could modify the filter_by_keys option above to read surface variables accumulated from the forecast initialization to hour 6 by providing the stepRange option:

filter_by_keys={
    "typeOfLevel": "surface",
    "stepType": "accum",
    "stepRange": "0-6"
}

or, for example when reading HRRR data we could read the accumulation over the previous hour as so,

filter_by_keys={
    "typeOfLevel": "surface",
    "stepType": "accum",
    "stepRange": "5-6"
}

The default behavior for xarray+cfgrib (which ufs2arco uses internally) appears to grab the accumulation over the full forecast period. However, to provide a different accumulation period for the stepRange argument, add the accum_hrs to the yaml recipe. For example with accumulated total precipitation,

accum_hrs:
  accum_tp: 1

This would grab the total precipitation averaged over the most recent forecast hour. So, at forecast hour 6, this is the same as providing {"stepRange":"5-6"} when reading a single grib file.