def lt_dependent_cor_nwp(lt, correlations, outdir_path, n_model=0, skill_kwargs=None):
    """Determine the correlation of a model field for lead time lt and
    cascade k, by assuming that the correlation determined at t=0 regresses
    towards the climatological values.


    Parameters
    ----------
    lt : int
        The lead time of the forecast in minutes.
    correlations : array-like
        Array of shape [n_cascade_levels] containing per cascade_level the
        correlation between the normalized cascade of the observed (radar)
        rainfall field and the normalized cascade of the model field.
    outdir_path: string
      Path to folder where the historical skill are stored. Defaults to
      path_workdir from rcparams.
    n_model: int, optional
        The index number of the (NWP) model when the climatological skill of
        multiple (NWP) models is stored. For calculations with one model, or
        when n_model is not provided, n_model = 0.
    skill_kwargs : dict, optional
        Dictionary containing e.g. the outdir_path, nmodels and window_length
        parameters.

    Returns
    -------
    rho : array-like
        Array of shape [n_cascade_levels] containing, for lead time lt, per
        cascade_level the correlation between the normalized cascade of the
        observed (radar) rainfall field and the normalized cascade of the
        model field.

    References
    ----------
    :cite:`BPS2004`
    :cite:`BPS2006`
    """

    if skill_kwargs is None:
        skill_kwargs = dict()

    # Obtain the climatological values towards which the correlations will
    # regress
    clim_cor_values, regr_pars = clim_regr_values(
        n_cascade_levels=len(correlations),
        outdir_path=outdir_path,
        n_model=n_model,
        skill_kwargs=skill_kwargs,
    )
    # Determine the speed of the regression (eq. 24 in BPS2004)
    qm = np.exp(-lt / regr_pars[0, :]) * (2 - np.exp(-lt / regr_pars[1, :]))
    # Determine the correlation for lead time lt
    rho = qm * correlations + (1 - qm) * clim_cor_values

    return rho
Determine the correlation of a model field for lead time lt and
cascade k, by assuming that the correlation determined at t=0 regresses
towards the climatological values.


Parameters
----------
lt : int
The lead time of the forecast in minutes.
correlations : array-like
Array of shape [n_cascade_levels] containing per cascade_level the
correlation between the normalized cascade of the observed (radar)
rainfall field and the normalized cascade of the model field.
outdir_path: string
Path to folder where the historical skill are stored. Defaults to
path_workdir from rcparams.
n_model: int, optional
The index number of the (NWP) model when the climatological skill of
multiple (NWP) models is stored. For calculations with one model, or
when n_model is not provided, n_model = 0.
skill_kwargs : dict, optional
Dictionary containing e.g. the outdir_path, nmodels and window_length
parameters.

Returns
-------
rho : array-like
Array of shape [n_cascade_levels] containing, for lead time lt, per
cascade_level the correlation between the normalized cascade of the
observed (radar) rainfall field and the normalized cascade of the
model field.

References
----------
:cite:`BPS2004`
:cite:`BPS2006`
pysteps/blending/skill_scores.py