Generalized Wind Climate (GWC)#

The generalized wind climate is a key part of the WAsP methodology. It represents the wind in a virtual world with no terrain and homogeneous roughness values. GWC contains Weibull parameters across additional dimensions for generalized heights and roughnesses.

A generalized wind climate tries to be representative of a larger area, while an observed or predicted wind climate is representative only of a specific location.

Data Structure#

GWC objects are xarray.Dataset with the following schema:

Dimensions:  (gen_roughness, gen_height, sector, ...)
Variables:
    A       (gen_roughness, gen_height, sector, ...)  - Weibull scale
    k       (gen_roughness, gen_height, sector, ...)  - Weibull shape
    wdfreq  (gen_roughness, gen_height, sector, ...)  - direction frequency

In addition to the core coordinates, GWC objects contain:

  • gen_height - generalized heights (meters above constant terrain)

  • gen_roughness - homogeneous roughness lengths

Note

In classic WAsP, you were limited to exactly five of each parameter. In WindKit, you can use as many or as few as needed.

I/O#

Format

Read

Write

Description

.lib

yes

yes

WAsP Library format

.gwc

yes

no

WAsP Generalized Wind Climate

.nc

yes

via .to_netcdf()

NetCDF format

Reading:

windkit.read_gwc() reads generalized wind climates:

# Read from LIB file
gwc = wk.read_gwc("generalized.lib")

Writing:

windkit.gwc_to_file() writes generalized wind climates to .lib files:

wk.gwc_to_file(gwc, "output.lib")

Creating from Arrays#

gen_heights = np.array([10, 25, 50, 100, 200])
gen_roughnesses = np.array([0.0, 0.03, 0.1, 0.3, 1.5])

A = np.random.rand(5, 5, 12) * 10
k = np.random.rand(5, 5, 12) + 1
wdfreq = np.random.rand(5, 5, 12)
wdfreq = wdfreq / wdfreq.sum(axis=2, keepdims=True)

gwc = xr.Dataset(
    data_vars=dict(
        A=(("gen_height", "gen_roughness", "sector"), A),
        k=(("gen_height", "gen_roughness", "sector"), k),
        wdfreq=(("gen_height", "gen_roughness", "sector"), wdfreq),
    ),
    coords=dict(
        gen_height=(("gen_height",), gen_heights),
        gen_roughness=(("gen_roughness",), gen_roughnesses),
        sector=(("sector",), (wdbins[1:] + wdbins[:-1]) / 2.0),
        sector_floor=(("sector_floor",), np.mod(wdbins[:-1], 360)),
        sector_ceil=(("sector_ceil",), np.mod(wdbins[1:], 360)),
    )
)
gwc

<xarray.Dataset> Size: 8kB
Dimensions:        (gen_height: 5, gen_roughness: 5, sector: 12,
                    sector_floor: 12, sector_ceil: 12)
Coordinates:
  * gen_height     (gen_height) int64 40B 10 25 50 100 200
  * gen_roughness  (gen_roughness) float64 40B 0.0 0.03 0.1 0.3 1.5
  * sector         (sector) float64 96B 0.0 30.0 60.0 90.0 ... 270.0 300.0 330.0
  * sector_floor   (sector_floor) float64 96B 345.0 15.0 45.0 ... 285.0 315.0
  * sector_ceil    (sector_ceil) float64 96B 15.0 45.0 75.0 ... 315.0 345.0
Data variables:
    A              (gen_height, gen_roughness, sector) float64 2kB 8.333 ... ...
    k              (gen_height, gen_roughness, sector) float64 2kB 1.642 ... ...
    wdfreq         (gen_height, gen_roughness, sector) float64 2kB 0.08418 .....

Usage in WAsP Methodology#

GWC is central to WAsP’s horizontal extrapolation:

  1. Generalization - Remove local terrain effects from observed wind climate to create GWC

  2. Downscaling - Apply terrain effects at new locations using the GWC

This is done in PyWAsP:

import pywasp as pw

# Generalize observed wind climate
gwc = pw.wasp.generalize(bwc, topo_map)

# Downscale to new locations
wwc = pw.wasp.downscale(gwc, topo_map, output_locs)

See WAsP Flow Model for details.

Validation#

wk.is_gwc(gwc)

True