pywasp.wasp.downscale

pywasp.wasp.downscale(genwc, topo_map, output_locs, conf=None, genwc_interp=None, mesoclimate=None, mesoclimate_interp_method='nearest', return_site_factors=False, add_met=True, cfd_volume=None)[source]

Calculate site_effects, downscaled wind climate, and meteorlogical fields in a single step

Parameters:

  • genwc (xarray.DataSet) – Generalized wind climate xr.DataSet to downscale

  • topo_map (TopographyMap) – TopographyMap of the region to model

  • output_locs (xarray.Dataset) – Locations to calculate at created using create_dataset

  • conf (Config) – Configuration information from WAsP

  • genwc_interp (str, optional) – String indicating interpolation method, by default None

  • mesoclimate (xarray.DataSet, default None) – If None use the ERA5 reanalysis to obtain the mesoclimate, otherwise one can create a dataset using the get_climate method

  • mesoclimate_interp_method (str, optional) – Interpolation method for mesoclimate, by default ‘nearest’

  • return_site_factors (bool) – Include the site_factors in the output?

  • add_met (bool) – Calculate and include meteorlogical fields from add_met_fields in the output?

  • cfd_volume (xarray.DataSet, default None) – WAsP CFD volume xarray dataset that is used for obtaining site effects

Returns:

xarray.DataSet – PyWAsP formated xr.DataSet containing sectorwise A, k, frequency, total A and k at site. Optionally include speedups, rix, elevation and other site_factors, and/or wind speeds, air and power densities.

Notes

Run WAsP’s wprms_nt function to perform the “down” part of the WAsP framework. This will take the generalized data and convert it to a site specific weibull distribution based on the local conditions.

genwc_interp can be either ‘None’ (i.e., genwc will be interpolated on the same grid as output_locs) or ‘NN’ (i.e., Nearest Neighbor)