pywasp.wasp.downscale(genwc, topo_map, output_locs, conf=None, interp_method='given', 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

  • 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

  • interp_method (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 pw.wasp.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 or list of xarray.Datasets, default None) – WAsP CFD volume xarray dataset that is used for obtaining site effects


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.


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.