Generating Outputs with ERA5 Data

Generating Outputs with ERA5 Data#

ERA5 Configurations and Outputs#

In this section, we provide a list of currently possible geodata configs and outputs using various ERA5 datasets downloaded from Copernicus’s Climate Data Store.

Dataset Configurations#

geodata is currently optimized to work with the following ERA5 datasets:

Most of the conversion/generation methods supports the first two configurations, wind_solar_hourly and wind_solar_monthly, only. The wind_3d_hourly configuration is used for 3D wind speed interpolation and extrapolation. For more information on that, see this page.

Wind Generation Time-series#

Convert wind speeds for turbine to wind energy generation. This method supports the configs wind_solar_hourly and wind_solar_monthly.

cutout.wind(turbine: str | dict[str, str], smooth: bool = False)

Parameters#

  • turbine - str | dict - Name of a turbine known by the reatlas client or a turbineconfig dictionary with the keys ‘hub_height’ for the hub height and ‘V’, ‘POW’ defining the power curve. For a full list of currently supported turbines, see the list of turbines here.

  • smooth - bool | dict - If True, smooth power curve with a gaussian kernel as determined for the Danish wind fleet to Delta_v = 1.27 and sigma = 2.29. A dict allows to tune these values.

Note

You can also specify all of the general conversion arguments documented in the convert_and_aggregate function (e.g. var_height='lml').

Example Code#

ds_wind = cutout.wind(turbine="Suzlon_S82_1.5_MW", smooth=True)
ds_wind.to_dataframe(name="wind")

Wind Speed Time-series#

Extract wind speeds at given height (ms-1). This method supports the configs wind_solar_hourly and wind_solar_monthly.

cutout.windspd(**params)

Parameters#

  • **params - Must have 1 of the following:

    • turbine - str | dict - Name of a turbine known by the reatlas client or a turbineconfig dictionary with the keys ‘hub_height’ for the hub height and ‘V’, ‘POW’ defining the power curve. For a full list of currently supported turbines, the list of turbines here.

    • hub-height - int - Extrapolation height (m)

Note

You can also specify all of the general conversion arguments documented in the convert_and_aggregate function (e.g. var_height='lml').

Example Code#

ds_windspd = cutout.windspd(turbine='Vestas_V66_1750kW')
ds_windspd.to_dataframe(name='windspd')

Solar photovoltaic generation time-series#

Convert downward-shortwave, upward-shortwave radiation flux and ambient temperature into a pv generation time-series. This method supports the configs wind_solar_hourly and wind_solar_monthly.

cutout.pv(panel, orientation, clearsky_model)

Parameters#

  • panel - str - Specify a solar panel type on which to base the calculation. geodata contains an internal solar panel dictionary with keys defining several solar panel characteristics used for the time-series calculation. For a complete list of included panel types, see the list of panel types here.

  • orientation - str, dict or callback - Panel orientation can be chosen from either latitude_optimal, a constant orientation such as {'slope': 0.0,'azimuth': 0.0}, or a callback function with the same signature as the callbacks generated by the geodata.pv.orientation.make_* functions.

  • (optional) clearsky_model - string or None - Either the simple or the enhanced Reindl clearsky model. The default choice of None will choose dependending on data availability, since the enhanced model also incorporates ambient air temperature and relative humidity.

Example Code and Result#

ds_pv = cutout.pv(panel="KANEKA", orientation = "latitude_optimal")
ds_pv.to_dataframe(name='pv')

Generating Temperature Outputs with MERRA2 Data#

Celsius Temperature Time-series#

Extract temperature in the cutout in Celsius. This method supports the configs wind_solar_hourly and wind_solar_monthly.

ds_temperature = cutout.temperature()
ds_temperature.to_dataframe(name='temperature')

Example Output#

With the full list of supported ERA5 outputs above, we can see an example of generating output ERA5 data from the Copernicus Data Store.

Setup#

Let’s assume we’ve created an ERA5 cutout along the following lines:

cutout = geodata.Cutout(name="era5-europe-example",
    module="era5",
    weather_data_config="era5_monthly",
    xs=slice(30, 41.56244222),
    ys=slice(33.56459975, 35),
    years=slice(2011, 2011),
    months=slice(1,1)
)

Note that unlike datasets, we don’t need explicitly prepare a cutout. Geodata will prepare the cutout automatically if it’s not ready. We can now use this cutout to generate datasets.

Creating a Solar photovoltaic (pv) generation time-series#

To create a pv generation time-series, we can use the following code with our ERA5 cutout: This method supports the configs wind_solar_hourly and wind_solar_monthly.

ds = cutout.pv(panel="KANEKA", orientation="latitude_optimal")

Some information about the parameters:

  • panel - string - Specify a solar panel type on which to base the calculation. geodata contains an internal solar panel dictionary with keys defining several solar panel characteristics used for the time-series calculation. For a complete list of included panel types, see the list of panel types here.

  • orientation - str, dict or callback - Panel orientation can be chosen from either latitude_optimal, a constant orientation such as {'slope': 0.0,'azimuth': 0.0}, or a callback function with the same signature as the callbacks generated by the geodata.pv.orientation.make_* functions.

  • (optional) clearsky_model - string or None - Either the simple or the enhanced Reindl clearsky model. The default choice of None will choose dependending on data availability, since the enhanced model also incorporates ambient air temperature and relative humidity.

The convert function returns an xarray dataset, which is an in-memory representation of a NetCDF file:

<xarray.DataArray 'AC power' (y: 5, time: 744, x: 47)>
array([[[0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.],
        [0., 0., 0., ..., 0., 0., 0.],

Coordinates:
    lon      (x) float32 30.0 30.25 30.5 30.75 31.0 ... 40.75 41.0 41.25 41.5
    lat      (y) float32 34.815 34.565 34.315 34.065 33.815
  * x        (x) float32 30.0 30.25 30.5 30.75 31.0 ... 40.75 41.0 41.25 41.5
  * y        (y) float32 34.815 34.565 34.315 34.065 33.815
  * time     (time) datetime64[ns] 2011-01-01 ... 2011-01-31T23:00:00

To convert this array to a more conventional dataframe, we can run:

df = ds.to_dataframe(name='pv')

which converts the xarray dataset into a pandas dataframe.

The result is a dataset with an observation for each time period (in this ERA5 case, hourly) and geographic point with the calculated pv generation for each observation.

Finally, we can run something like:

df.to_csv('era5_pv_data.csv')

to output the data to csv for use in other applications.