Source code for pycif.plugins.transforms.complex.diagmet.utils.mean_z0_shf_extra_urban_temp

import pandas as pd
import numpy as np
from ......utils.check.errclass import CifError




def mean_z0_shf_extra_urban_temp(transf, inout_datastore, ddi, mapper):
    """Apply urban heat island corrections to roughness length, sensible heat flux, and 2-m temperature.

    Combines the ``LANDUSE`` land-cover fractions and the monthly ``LANDPAR``
    roughness lengths into four grid-cell diagnostics — urban wind-reduction
    factor ``awf``, area-weighted roughness length ``az0``, additional urban
    heat-flux fraction ``auf``, and urban-corrected minimum PBL height ``pm`` —
    stored in ``transf.diag_misc`` for use by later steps, and applies ``awf``
    to the lowest-level winds. See
    :doc:`/documentation/doc-models/chimere/diagmet` (section 4) for the full
    derivation.

    Args:
        transf (Plugin): diagmet transform instance.
        inout_datastore (dict): mutable datastore.
        ddi (datetime): current sub-simulation date.
        mapper (dict): transform mapper.
    """
    # Read LANDUSE
    domain = mapper["inputs"][("meteo", "winz")]["domain"]
    nlat, nlon = domain.zlat.shape
    landuse = pd.read_csv(
        transf.file_landuse, header=None, sep=r"\s+"
    ).values
    
    if landuse.size != nlat * nlon * 9:
        raise CifError(f"Warning: The landuse file in file {transf.file_landuse} has not the expected shape ({nlat}/{nlon}). Make sure the LANDUSE file matches the domain")
    
    fland = np.transpose(landuse.reshape((nlat, nlon, 9)), axes=(2, 0, 1))
    
    # Read LANDPAR
    zom = pd.read_csv(transf.file_landpar, sep=r"\s+", index_col=0)

    # Parameter
    pblmin = transf.pblmin

    # Urban corrections for wind, fluxes
    uwinfac = int(not transf.uwinfac)
    uflxadd = int(transf.uflxadd)
    upblmin = int(transf.upblmin)

    # Compute awf, az0, auf, pm
    awf = fland[np.arange(9) != 4].sum(axis=0) + uwinfac * fland[4]
    az0 = (fland * zom.loc[ddi.month].values[:, np.newaxis, np.newaxis]).sum(axis=0)
    auf = uflxadd * fland[4]
    pm = pblmin * fland[np.arange(9) != 4].sum(axis=0) + max(upblmin, pblmin) * fland[4]
    
    # Save variables to diag_misc for use by other functions
    transf.diag_misc.update({"awf": awf, "az0": az0, "auf": auf, "pm": pm})

    # Correction of wind speeds
    inout_datastore["outputs"][("meteo", "winm")][ddi]["spec"][:, 0] *= awf
    inout_datastore["outputs"][("meteo", "winz")][ddi]["spec"][:, 0] *= awf