Generating Outputs with MERRA2 Data

Generating Outputs with MERRA2 Data#

MERRA2 Configurations and Outputs#

In this section, we provide a list of currently possible geodata configs and outputs for MERRA2 data from NASA’s GES DISC.

Dataset Configurations#

Currently, geodata is able to use the following MERRA2 datasets:

Variable Configurations#

MERRA2 surface flux data currently supports the following variable configs:

Wind (surface_flux_hourly, surface_flux_monthly, slv_flux_hourly):

  • ustar: surface_velocity_scale (m s-1)

  • z0m: surface_roughness (m)

  • disph: zero_plane_displacement_height (m)

  • rhoa: air_density_at_surface (kg m-3)

  • ulml: surface_eastward_wind (m s-1)

  • vlml: surface_northward_wind (m s-1)

  • tstar: surface_temperature_scale (K)

  • hlml: surface_layer_height (m)

  • tlml: surface_air_temperature (K)

  • pblh: planetary_boundary_layer_height (m)

  • hflux: sensible_heat_flux_from_turbulence (W m-2)

  • eflux: total_latent_energy_flux (W m-2)

Wind (Extra) (Only found in slv_flux_hourly):

  • u2m: 2-meter eastward wind (m s-1)

  • v2m: 2-meter northward wind (m s-1)

  • u10m: 10-meter eastward wind (m s-1)

  • v10m: 10-meter northward wind (m s-1)

  • u50m: 50-meter eastward wind (m s-1)

  • v50m: 50-meter northward wind (m s-1)

Solar (slv_radiation_hourly, slv_radiation_monthly)

  • albedo: surface albedo

  • swgdn: surface incoming shortwave flux (W m-2)

  • swtdn: toa incoming shortwave flux (W m-2)

  • t2m: 2-meter air temperature (K)

Temperature (surface_flux_dailymeans)

  • hournorain: time-during an hour with no precipitation (s)

  • tprecmax: Maximum precipitation rate during the period (kg m-2 s-1)

  • t2max: max 2-meter air temperature (K)

  • t2mmean: mean 2-meter air temperature (K)

  • t2mmin: min 2-meter air temperature (K)

Aerosols (surface_aerosol_hourly)

  • bcsmass: black carbon surface mass concentration (kg m-3)

  • dusmass25: dust surface mass concentration - pm 2.5 (kg m-3)

  • ocsmass: organic carbon surface mass concentration (kg m-3)

  • so4smass: SO4 Surface Mass Concentration (kg m-3)

  • sssmass25: Sea Salt Surface Mass Concentration - pm 2.5 (kg m-3)

Outputs#

MERRA2 surface flux data currently supports the following outputs:

Wind (surface_flux_hourly, surface_flux_monthly):

  • Wind generation time-series

  • Wind speed time-series

  • Wind power density time-series

Solar (slv_radiation_hourly, slv_radiation_monthly)

  • PV generation time-series

Temperature (surface_flux_dailymeans)

  • Celsius Temperature

Aerosols (surface_aerosol_hourly)

  • PM2.5 time series

Generating Wind Outputs with MERRA2 Data#

geodata currently supports the following wind outputs using MERRA2 hourly, single-level surface flux diagnostics or also using MERRA2 monthly mean, single-level surface flux diagnostics.

  • Wind generation time-series (wind)

  • Wind speed time-series (windspd)

  • Wind power density time-series (windpwd)

Wind Generation Time-series#

Convert wind speeds for turbine to wind energy generation.

cutout.wind(turbine, nsmooth=False, var_height)

Parameters#

  • turbine - string or 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 or 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, var_height="lml")
ds_wind.to_dataframe(name="wind")

Wind Speed Time-series#

Extract wind speeds at given height (ms-1)

cutout.windspd(**params)

Parameters#

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

    • turbine - string or 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.

    • hub-height - num - 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", var_height="lml")
ds_windspd.to_dataframe(name="windspd")

Wind Power Density Time-series#

Extract wind power density at given height, according to: WPD = 0.5 * Density * Windspd^3

cutout.windwpd(**params)

Parameters#

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

    • turbine - string or 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.

    • hub-height - float or 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_windwpd = cutout.windwpd(turbine="Suzlon_S82_1.5_MW", var_height="lml")
ds_windwpd.to_dataframe(name="windwpd")

Generating Solar Outputs with MERRA2 Data#

geodata currently supports the following solar outputs using the following configs:

PV Generation Time-series#

Convert downward-shortwave, upward-shortwave radiation flux and ambient temperature into a pv generation time-series.

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.

Example Code and Result#

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

Generating Aerosol Outputs with MERRA2 Data#

geodata currently supports the following aerosol output using the following config:

PM2.5 time series#

Generate PM2.5 time series according to [1]: PM2.5 = [Dust2.5] + [SS2.5] + [BC] + 1.4*[OC] + 1.375*[SO4]

Example Code and Result#

ds_pm25 = cutout.pm25()
ds_pm25.to_dataframe(name='pm25')

Generating Temperature Outputs with MERRA2 Data#

Celsius Temperature Time-series#

Extract temperature in the cutout in Celsius.

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