ufs2arco.sources.AWSGEFSArchive.__init__

ufs2arco.sources.AWSGEFSArchive.__init__#

AWSGEFSArchive.__init__(t0: dict, fhr: dict, member: 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) → None#
Parameters:

  • t0 (dict) – Dictionary with start and end times for initial conditions, and e.g. “freq=6h”. All options get passed to pandas.date_range.

  • fhr (dict) – Dictionary with ‘start’, ‘end’, and ‘step’ forecast hours.

  • member (dict) – Dictionary with ‘start’, ‘end’, and ‘step’ ensemble members.

  • variables (list, tuple, optional) – variables to grab

  • levels (list, tuple, optional) – vertical levels to grab

  • use_nearest_levels (bool, optional) – if True, all level selection with xarray.Dataset.sel(level=levels, method="nearest")

  • slices (dict, optional) – either “sel” or “isel”, with slice, passed to xarray

  • accum_hrs (dict, optional) – period in hours over which to accumulate accumulated variables, e.g. {“accum_tp”: 1} would be the same as passing filter_by_keys={“stepRange”: “5-6”} when reading fhr 6 data using xarray and cfgrib

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