pywasp.wasp.downscale_from_site_effects

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

Downscale a generalized wind climate using precalculated site effects

Parameters:

  • genwc (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 {'given','nearest','natural','linear'}) – String indicating interpolation method, by default None. ‘None’ tries to select the best interpolation method based on the spatial structure of the data. Please check documentation in the function pw.wasp.interpolate_gwc().

  • 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_factors (bool) – Include the site_factors 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.