AWSHRRRArchive

class ufs2arco.sources.AWSHRRRArchive(t0: dict, fhr: dict, variables: list | tuple | None = None, levels: list | tuple | None = None, use_nearest_levels: bool | None = False, slices: dict | None = None, accum_hrs: dict | None = None)

Access the archive of of NOAA’s High Resolution Rapid Refresh (HRRR) forecast system on AWS.

For more see:

• https://rapidrefresh.noaa.gov/hrrr/

• https://registry.opendata.aws/noaa-hrrr-pds/

Note

I don’t know if the resolution changes at some point, as it does in the GEFS archive.

Methods

AWSHRRRArchive.__init__(t0, fhr[, ...])
AWSHRRRArchive.add_full_extra_coords(xds)	An optional routine that builds extra coordinates if needed, see example in ensemble_forecast.py.
AWSHRRRArchive.apply_slices(xds)	Apply any slices, for now just data selection via "sel" or "isel" Note that this is the first transformation, so slicing options relate to the standard dimensions:
AWSHRRRArchive.open_grib(dims, file_suffix, ...)	Open a single GRIB file.
AWSHRRRArchive.open_sample_dataset(dims, ...)

Attributes

AWSHRRRArchive.available_levels	Built-in immutable sequence.
AWSHRRRArchive.available_variables	Built-in immutable sequence.
AWSHRRRArchive.dynamic_vars
AWSHRRRArchive.file_suffixes
AWSHRRRArchive.horizontal_dims
AWSHRRRArchive.name
AWSHRRRArchive.rename	Use this to map whatever the original source is to the ufs2arco standards...which need to be documented
AWSHRRRArchive.sample_dims
AWSHRRRArchive.static_vars