def get_default_skill(n_cascade_levels=6, n_models=1):
    """
    Get the default climatological skill values as given in :cite:`BPS2006`.
    Take subset of n_cascade_levels or add entries with small values (1e-4) if
    n_cascade_levels differs from 8.

    Parameters
    ----------
    n_cascade_levels: int, optional
      Number of cascade levels. Defaults to 6, see issue #385 on GitHub.
    n_models: int, optional
      Number of NWP models. Defaults to 1.

    Returns
    -------
    default_skill: array-like
      Array of shape [model, scale_level] containing the climatological skill
      values.

    """

    default_skill = np.array(
        [0.848, 0.537, 0.237, 0.065, 0.020, 0.0044, 0.0052, 0.0040]
    )
    n_skill = default_skill.shape[0]
    if n_cascade_levels < n_skill:
        default_skill = default_skill[0:n_cascade_levels]
    elif n_cascade_levels > n_skill:
        default_skill = np.append(
            default_skill, np.repeat(1e-4, n_cascade_levels - n_skill)
        )
    return np.resize(default_skill, (n_models, n_cascade_levels))
Get the default climatological skill values as given in :cite:`BPS2006`.
Take subset of n_cascade_levels or add entries with small values (1e-4) if
n_cascade_levels differs from 8.

Parameters
----------
n_cascade_levels: int, optional
Number of cascade levels. Defaults to 6, see issue #385 on GitHub.
n_models: int, optional
Number of NWP models. Defaults to 1.

Returns
-------
default_skill: array-like
Array of shape [model, scale_level] containing the climatological skill
values.
pysteps/blending/clim.py