pywasp.wasp.downscale_from_site_effects

pywasp.wasp.downscale_from_site_effects(gwc, site_effects, conf=None, interp_method='nearest', mesoclimate=None, mesoclimate_interp_method='nearest', return_site_effects=False, add_met=False)[source]

Downscale a generalized wind climate using precalculated site effects

Parameters:

  • gwc (xarray.Dataset) – Generalized wind climate xr.Dataset to downscale

  • site_effects (xarray.Dataset) – Site effects dataset created from TopographyMap.rose_to_site_effects or TopographyMap.get_site_effects.

  • conf (pw.wasp.Config) – Configuration information from WAsP

  • interp_method (str, optional) – String indicating interpolation method, by default “nearest”. Options are {“nearest”, “linear”, “cubic”, “natural”, “given”}. If “given”, the function will not interpolate the generalized wind climate, but assumes that the gwc and output_locs have the same spatial structure. If “nearest”, it will use the nearest neighbor interpolation. If “linear”, it will use linear interpolation. If “cubic”, it will use cubic interpolation. If “natural”, it will use natural neighbor interpolation.

  • mesoclimate (xarray.Dataset, default None) – If None use the CFSR 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_effects (bool) – Include the site_effects in the output?

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

Returns:

xarray.Dataset – PyWAsP formated xr.Dataset containing sectorwise A, k, frequency at site

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.