Another CDS Toolbox app to visualise the PV Power Potential ERA5 reanalyses #CDSToolbox. To test the application add a new application in your toolbox workspace and copy & paste the following code. NOTE: the units in the colorbar are wrong (the potential is dimensionless) but I couldn't find a way to remove it.

show_pv_potential.py

import cdstoolbox as ct

@ct.application(title='PV Power Potential', 
               description = 'Computation of PV Power potential for a specific day using the dimensionless described in Mavromatakis et al. https://doi.org/10.1016/j.renene.2009.11.010',
               abstract = 'Lorem Ipsum')
@ct.input.dropdown('show_year', values=range(2010, 2017))
@ct.input.dropdown('region', values=['Europe', 'China'])
@ct.input.text('month', type = int, label='Month', default=7)
@ct.input.text('day', type = int, label='Day', default=1)

@ct.output.figure()
def plot_map(show_year, region, month, day):


    ssr = ct.catalogue.retrieve(
        'reanalysis-era5-single-levels',
        {
            'variable': 'surface_solar_radiation_downwards',
            'grid': ['0.5', '0.5'],
            'area': '65/-20/30/45' if (region == 'Europe') else '60/75/15/135',
            'product_type': 'reanalysis',
            'year': show_year,
            'month': month,
            'day': day,
            'time': ['06:00', '08:00', '10:00', '12:00','14:00','16:00', '18:00'],
        })
    t2m = ct.catalogue.retrieve(
        'reanalysis-era5-single-levels',
        {
            'variable': '2m_temperature',
            'grid': ['0.5', '0.5'],
            'area': '65/-20/30/45' if (region == 'Europe') else '60/75/15/135',
            'product_type': 'reanalysis',
            'year': show_year,
            'month': month,
            'day': day,
            'time': ['06:00', '08:00', '10:00', '12:00','14:00','16:00', '18:00'],
        })
    tcell = ct.operator.add(ct.climate.daily_mean(t2m), ct.operator.mul(ct.climate.daily_mean(ssr), 1e-5))
    eta = ct.operator.add(ct.operator.mul(ct.operator.sub(tcell, 273.15+25), 0.0045), 1)
    pvpot = ct.operator.mul(eta, ct.operator.div(ct.climate.daily_mean(ssr), 1000*3600))

    fig = ct.cdsplot.geomap(pvpot, pcolormesh_kwargs={'cmap': 'YlOrRd', 'levels':8, 'cbar_kwargs': {'label': 'PV Potential'}})
    return fig