How to use this plot
Make sure you have the required datafields (air_pressure, air_temperature, cloud_area_fraction_in_atmosphere_layer)
You can use it as is by appending this code into your mswms_settings.py: 

from mslib.mswms.mpl_vsec_styles import VS_CloudsStyle_01
register_vertical_layers = [] if not register_vertical_layers else register_vertical_layers
register_vertical_layers.append((VS_CloudsStyle_01, [next(iter(data))]))

If you want to modify the plot
1. Download this file
2. Put this file into your mswms_settings.py directory, e.g. ~/mss
3. Append this code into your mswms_settings.py: 

from Side_VS_CC01 import VS_CloudsStyle_01
register_vertical_layers = [] if not register_vertical_layers else register_vertical_layers
register_vertical_layers.append((VS_CloudsStyle_01, [next(iter(data))]))
Side_VS_CC01.py 
"""
    This file is part of MSS.

    :copyright: Copyright 2021-2022 by the MSS team, see AUTHORS.
    :license: APACHE-2.0, see LICENSE for details.

    Licensed under the Apache License, Version 2.0 (the "License");
    you may not use this file except in compliance with the License.
    You may obtain a copy of the License at

       http://www.apache.org/licenses/LICENSE-2.0

    Unless required by applicable law or agreed to in writing, software
    distributed under the License is distributed on an "AS IS" BASIS,
    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
    See the License for the specific language governing permissions and
    limitations under the License.
"""

import warnings
import matplotlib
import matplotlib.pyplot as plt
import mpl_toolkits.axes_grid1.inset_locator
from matplotlib import patheffects
import numpy as np
from mslib.mswms.mpl_vsec import AbstractVerticalSectionStyle
from mslib.mswms.utils import Targets, get_style_parameters, get_cbar_label_format, make_cbar_labels_readable
from mslib.utils import thermolib
from mslib.utils.units import convert_to

class VS_CloudsStyle_01(AbstractVerticalSectionStyle):
    """
    Clouds
    Vertical section of cloud cover.
    """

    name = "VS_CC01"
    title = "Cloud Cover (0-1) Vertical Section"
    abstract = "Cloud cover (0-1) with temperature (K) and potential temperature (K)"

    # Variables with the highest number of dimensions first (otherwise
    # MFDatasetCommonDims will throw an exception)!
    required_datafields = [
        ("ml", "air_pressure", "Pa"),
        ("ml", "air_temperature", "K"),
        ("ml", "cloud_area_fraction_in_atmosphere_layer", 'dimensionless')]

    def _prepare_datafields(self):
        """Computes potential temperature from pressure and temperature if
        it has not been passed as a data field.
        """
        self.data['air_potential_temperature'] = thermolib.pot_temp(
            self.data['air_pressure'], self.data['air_temperature'])

    def _plot_style(self):
        """
        Make a cloud cover vertical section with temperature/potential
        temperature overlay.
        """
        ax = self.ax
        curtain_p = self.data["air_pressure"]
        curtain_t = self.data["air_temperature"]
        curtain_pt = self.data["air_potential_temperature"]
        curtain_cc = self.data["cloud_area_fraction_in_atmosphere_layer"]

        # Contour spacing for temperature lines.
        delta_t = 2 if (np.log(self.p_bot) - np.log(self.p_top)) < 2.2 else 4
        delta_pt = 5 if (np.log(self.p_bot) - np.log(self.p_top)) < 2.2 else 10
        # Filled contour plot of cloud cover.
        # INFO on COLORMAPS:
        #    http://matplotlib.sourceforge.net/examples/pylab_examples/show_colormaps.html
        cs = ax.contourf(self.horizontal_coordinate, curtain_p, curtain_cc,
                         np.arange(0.2, 1.1, 0.1), cmap=plt.cm.winter)
        # Contour line plot of temperature.
        cs_t = ax.contour(self.horizontal_coordinate,
                          curtain_p, curtain_t, np.arange(236, 330, delta_t),
                          colors='red', linestyles='solid', linewidths=1)  # gist_earth
        ax.clabel(cs_t, fontsize=8, fmt='%.0f')
        cs_t = ax.contour(self.horizontal_coordinate, curtain_p, curtain_t, [234],
                          colors='red', linestyles='solid', linewidths=2)  # gist_earth
        ax.clabel(cs_t, fontsize=8, fmt='%.0f')
        cs_t = ax.contour(self.horizontal_coordinate, curtain_p, curtain_t,
                          np.arange(160, 232, delta_t),
                          colors='red', linestyles='dashed', linewidths=1)  # gist_earth
        ax.clabel(cs_t, fontsize=8, fmt='%.0f')
        # Contour line plot of potential temperature.
        cs_pt = ax.contour(self.horizontal_coordinate, curtain_p, curtain_pt,
                           np.arange(200, 700, delta_pt), colors='grey',
                           linestyles='solid', linewidths=1)
        ax.clabel(cs_pt, fontsize=8, fmt='%.0f')

        # Pressure decreases with index, i.e. orography is stored at the
        # zero-p-index (data field is flipped in mss_plot_driver.py if
        # pressure increases with index).
        self._latlon_logp_setup(orography=curtain_p[0, :])

        # Add colorbar.
        if not self.noframe:
            self.fig.subplots_adjust(left=0.08, right=0.95, top=0.9, bottom=0.14)
            cbar = self.fig.colorbar(cs, fraction=0.05, pad=0.01)
            cbar.set_label("Cloud cover (0-1)")
        else:
            axins1 = mpl_toolkits.axes_grid1.inset_locator.inset_axes(
                ax, width="1%", height="30%", loc=1)
            cbar = self.fig.colorbar(cs, cax=axins1, orientation="vertical")
            axins1.yaxis.set_ticks_position("left")
            make_cbar_labels_readable(self.fig, axins1)