Retrieve precipitation & temp. data

# Import Toolbox
import cdstoolbox as ct

# Initialise the application
@ct.application(
    title='Add title',
    description='Add description')
    
# Add an output widget
## Bar or line chart based on Plotly
@ct.output.livefigure()

# Define the application function
def name_of_application ():

# Retrieve ERA5 monthly averaged data on single levels (this example: monthly averaged temperature)
## grid:[3, 3] --> to define the spatial resolution, if no grid is specified then default solution for ERA5 (0.25 x 0.25 deg.) is used
t2m_monthly_mean = ct.catalogue.retrieve(
    'reanalysis-era5-single-levels-monthly-means',
    {
        'product_type': 'monthly_averaged_reanalysis',
        'variable': '2m_temperature',
        'year': ['2009','2010','2011','2012','2013','2014','2015','2016','2017','2018'],
        'month': [
            '01','02','03',
            '04','05','06',
            '07','08','09',
            '10','11','12'
        ],
        'time': '00:00',
        'grid': [3, 3],
    }
)

# Retrieve ERA5 hourly data on single levels (this example: total precipitation flux, unit: m/s)
tp_hourly = ct.catalogue.retrieve(
    'reanalysis-era5-single-levels',
    {
        'product_type':'reanalysis',
        'variable':'total_precipitation',
        'year':[
            '2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018'
        ],
        'month':[
            '01', '02', '03',
            '04', '05', '06',
            '07', '08', '09',
            '10', '11', '12'
        ],
        'day':[
            '01', '02', '03',
            '04', '05', '06',
            '07', '08', '09',
            '10', '11', '12',
            '13', '14', '15',
            '16', '17',  '18',
            '19', '20', '21',
            '22', '23', '24',
            '25', '26', '27',
            '28', '29', '30',
            '31'
        ],
        'time':[
            '00:00','01:00','02:00',
            '03:00','04:00','05:00',
            '06:00','07:00','08:00',
            '09:00','10:00','11:00',
            '12:00','13:00','14:00',
            '15:00','16:00','17:00',
            '18:00','19:00','20:00',
            '21:00','22:00','23:00'
        ],
        'grid':[3, 3]
    }
)

# Resample total precipitation hourly data (convert flux into a cumulative column of water per hour (mm))
tp_hourly_column = tp_hourly * 3600 * 1000
tp_hourly_column = ct.cdm.update_attributes(tp_hourly_column, {'units': 'mm'})
tp_monthly_sums = ct.cube.resample(tp_hourly_column, freq='month', dim='time', closed='right', how='sum')
## Total precipitation is a forecast parameter and the 00:00 time step gives the precipitation falling between 23:00 and 00:00 of the previous day. 
## Setting closed='right' ignores this first 00:00 time step for each day, and instead takes the 00:00 time step from the following day.

# Check retrieved data
print(<tp_monthly_sums>)

# Generate climatology means for each month
t2m_mean_climatology = ct.climate.climatology_mean(t2m_monthly_mean, frequency='month')
tp_climatology = ct.climate.climatology_mean(tp_monthly_sums, frequency='month')

# Select a location
t2m_mean_loc = ct.geo.extract_point(t2m_mean_climatology, lon=12.5., lat=41.9)
tp_loc = ct.geo.extract_point(tp_climatology, lon=12.5, lat=41.9)

# Generate chart
## Define chart layout
layout_kwargs = {
    'width': 1000,
    'height': 700,
    'legend': {
        'orientation': 'h',
        'y': -0.15,
        'x': 0.1
    },
    'yaxis': {
        'overlaying': 'y2',
        'zeroline': False,
        'title': '2m air temperature in deg C'
    },
    'margin': {'t': 25},
    'xaxis': {
        'tickvals': [1,2,3,4,5,6,7,8,9,10,11,12],
        'ticktext': ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun','Jul','Aug','Sep','Oct','Nov','Dec'],
        'title': 'Month'
    },
    'yaxis2': {
        'showgrid': False,
        'side': 'right',
        'zeroline': False,
        'title': 'Total precipitation in mm'
    }
}

## Define chart object
fig = ct.chart.bar(
    tp_loc,
    bar_kwargs={
        'name':'Total precipitation',
        'yaxis': 'y2'
    },
    marker={'color':'lightsteelblue'},
    layout_kwargs=layout_kwargs
)

# Add a line plot for 2m mean temperature to the plot
fig = ct.chart.line(
    t2m_mean_loc,
    fig=fig,
    scatter_kwargs={
        'name':'Mean temperature',
        'mode':'lines'
    },
    marker={'color':'firebrick'},
    layout_kwargs=layout_kwargs
)

return fig