Vertical extrapolation

Example of using PyWAsP to perform vertical extrapolation.

Prepare TopographyMap

First, we need to prepare the topography map. This is done by reading the elevation and roughness maps and creating a TopographyMap object.

import numpy as np
import windkit as wk
import pywasp as pw

ssl = wk.load_tutorial_data("serra_santa_luzia")
# The wind directions are relative to the grid north of the UTM 29N maps
bwc = ssl.bwc.assign_attrs(wind_dir_crs="EPSG:32629")

topo_map = pw.wasp.TopographyMap(ssl.elev, ssl.rgh)

Define output locations

Second, we need to define the output locations. This is done by creating a dataset with the coordinates of the output locations.

output_locs = wk.spatial.create_dataset(
    west_east=bwc.west_east.values,
    south_north=bwc.south_north.values,
    height=np.linspace(10.0, 100.0, 10),
    crs="EPSG:32629",
    struct="stacked_point",
)
print(output_locs)

<xarray.Dataset> Size: 177B
Dimensions:      (height: 10, stacked_point: 1)
Coordinates:
  * height       (height) float64 80B 10.0 20.0 30.0 40.0 ... 80.0 90.0 100.0
    south_north  (stacked_point) float64 8B 4.621e+06
    west_east    (stacked_point) float64 8B 5.147e+05
    crs          int8 1B 0
Dimensions without coordinates: stacked_point
Data variables:
    output       (height, stacked_point) float64 80B 0.0 0.0 0.0 ... 0.0 0.0 0.0
Attributes:
    Conventions:  CF-1.8
    history:      2026-10-01T12:45:43+00:00:\twindkit==0.1.0.dev1+g565f937b5\...

Calculate resource grid

Finally, we can calculate the resource grid. This is done by calling the predict_wwc function. This function takes the output locations, the boundary conditions, and the topography map as input. The output is a weibull wind climate dataset at the output locations.

wwc = pw.wasp.predict_wwc(bwc, topo_map, output_locs)

Plot the mean wind speed

We can plot the mean wind speed to see the result.

wwc["wspd"].plot.line(y="height")

[<matplotlib.lines.Line2D object at 0x7c8a8baea710>]