Wind Climates
In PyWAsP’s Terminology and Data Structures
sections we outlined how we define the meaning and data structures of different kinds of wind climate objects.
Here we present how common wind climate workflows can be carried out. PyWAsP relies heavily on WindKit
for a lot of this functionality, so to illustrate it let’s start by importing windkit, pywasp and xarray.
In [1]: import pywasp as pw
In [2]: import windkit as wk
In [3]: import xarray as xr
Wind climate I/O
Reading and writing wind climates is a core part of WindKit
Format |
Wind Climate |
Read |
Write |
|---|---|---|---|
|
BWC, GWC, WWC |
yes |
yes |
|
BWC |
yes |
yes |
|
BWC |
yes |
no |
|
BWC |
yes |
no |
|
GWC |
yes |
yes |
|
GWC |
yes |
no |
|
WWC |
yes |
yes |
|
WWC |
yes |
yes |
|
WWC |
yes |
no |
|
WWC |
yes |
no |
All supported BWC’s and GWC’s can be read using windkit.read_bwc() and windkit.read_gwc(), respectively.
For WWC’s, windkit.read_wwc() can be used to read most supported formats, which include:
For writing wind climate files, windkit have the function windkit.bwc_to_file(),
windkit.gwc_to_file(), and windkit.wwc_to_file().
To explify, lets start by reading in a BWC stored in the .omwc file format:
In [4]: bwc = wk.read_bwc("source/tutorials/data/SerraSantaLuzia.omwc", crs="EPSG:4326")
In [5]: print(bwc)
<xarray.Dataset> Size: 4kB
Dimensions: (point: 1, sector: 12, wsbin: 32)
Coordinates:
height (point) float64 8B 25.3
south_north (point) float64 8B 41.74
west_east (point) float64 8B -8.823
crs int8 1B 0
wsceil (wsbin) float64 256B 1.0 2.0 3.0 4.0 ... 29.0 30.0 31.0 32.0
wsfloor (wsbin) float64 256B 0.0 1.0 2.0 3.0 ... 28.0 29.0 30.0 31.0
sector_ceil (sector) float64 96B 15.0 45.0 75.0 ... 285.0 315.0 345.0
sector_floor (sector) float64 96B 345.0 15.0 45.0 ... 255.0 285.0 315.0
* wsbin (wsbin) float64 256B 0.5 1.5 2.5 3.5 ... 28.5 29.5 30.5 31.5
* sector (sector) float64 96B 0.0 30.0 60.0 90.0 ... 270.0 300.0 330.0
Dimensions without coordinates: point
Data variables:
wdfreq (sector, point) float64 96B 0.05314 0.03321 ... 0.1148 0.0707
wsfreq (wsbin, sector, point) float64 3kB 0.02601 0.04219 ... 0.0 0.0
Attributes:
Conventions: CF-1.8
history: 2025-07-22T14:13:15+00:00:\twindkit==1.0.3.dev1+ga61276...
description: SerraSantaluzia
Package name: windkit
Package version: 1.0.3.dev1+ga612767
Creation date: 2025-07-22T14:13:15+00:00
Object type: Binned Wind Climate
author: Default User
author_email: default_email@example.com
institution: Default Institution
In this case, the .owmc file only contains coordinates, but not the coordinate reference system (CRS), so we have to set that
explicitly with the crs="ESPG:4326" argument.
As Discussed BWC’s are xarray.Dataset’s’ so it is easy to
use the all the built-in methods of xarray to interact with the data through the labeled dimensions and coordinates.
For example, we can plot the omni-directional wind speed frequencies by multiplying the wind speed frequencies, that are stored
as sector-relative frequencies, with the wind direction frequencies, summing the sectors, and then plotting with the wind speed
bins on the x-axis:
In [6]: (bwc.wsfreq * bwc.wdfreq).sum(dim="sector").plot.line(x="wsbin")
Out[6]: [<matplotlib.lines.Line2D at 0x704a7fa45a90>]
Geospatial transforms
WindKit's spatial module makes it easy to work with geospatial data, like the point associated with
the BWC above, and to perform typical transforms, such as reprojection, spatial clipping, and spatial masking.
To illustrate this, let’s transform the BWC from lat-long coordinates to UTM 29N coordinates:
In [7]: bwc = wk.spatial.reproject(bwc, to_crs="EPSG:32629")
In [8]: print(bwc)
<xarray.Dataset> Size: 4kB
Dimensions: (point: 1, sector: 12, wsbin: 32)
Coordinates:
height (point) float64 8B 25.3
crs int8 1B 0
wsceil (wsbin) float64 256B 1.0 2.0 3.0 4.0 ... 29.0 30.0 31.0 32.0
wsfloor (wsbin) float64 256B 0.0 1.0 2.0 3.0 ... 28.0 29.0 30.0 31.0
sector_ceil (sector) float64 96B 15.0 45.0 75.0 ... 285.0 315.0 345.0
sector_floor (sector) float64 96B 345.0 15.0 45.0 ... 255.0 285.0 315.0
* wsbin (wsbin) float64 256B 0.5 1.5 2.5 3.5 ... 28.5 29.5 30.5 31.5
* sector (sector) float64 96B 0.0 30.0 60.0 90.0 ... 270.0 300.0 330.0
west_east (point) float64 8B 5.147e+05
south_north (point) float64 8B 4.621e+06
Dimensions without coordinates: point
Data variables:
wdfreq (sector, point) float64 96B 0.05314 0.03321 ... 0.1148 0.0707
wsfreq (wsbin, sector, point) float64 3kB 0.02601 0.04219 ... 0.0 0.0
Attributes:
Conventions: CF-1.8
history: 2025-07-22T14:13:15+00:00:\twindkit==1.0.3.dev1+ga61276...
description: SerraSantaluzia
Package name: windkit
Package version: 1.0.3.dev1+ga612767
Creation date: 2025-07-22T14:13:15+00:00
Object type: Binned Wind Climate
author: Default User
author_email: default_email@example.com
institution: Default Institution
Note
Note that the west_east and south_north coordinate values have changed after reprojection.
To set and get the crs of an object, use windkit.spatial.set_crs() and windkit.spatial.get_crs().
Calculate statistics
WindKit includes functions to calculate common statistics from wind climate objects, such as the mean wind speed or mean power density:
In [9]: mean_ws = wk.mean_wind_speed(bwc)
In [10]: print(mean_ws)
<xarray.DataArray (point: 1)> Size: 8B
array([6.28573163])
Coordinates:
height (point) float64 8B 25.3
crs int8 1B 0
west_east (point) float64 8B 5.147e+05
south_north (point) float64 8B 4.621e+06
Dimensions without coordinates: point
Attributes:
history: 2025-07-22T14:13:15+00:00:\twindkit==1.0.3.dev1+ga612767\tbysec...
Note
The omni-directional statistics are returned by default. To return statistics by
wind direction sector, use the argument bysector=True
Fit weibull distribution
WindKit can fit weibull parameters to the wind speed frequencies in a binned wind climate, thereby
transforming it to a weibull wind climate. This can be done using windkit.weibull_fit():
In [11]: wwc = wk.weibull_fit(bwc)
In [12]: print(wwc)
<xarray.Dataset> Size: 601B
Dimensions: (sector: 12, point: 1)
Coordinates:
height (point) float64 8B 25.3
crs int8 1B 0
sector_ceil (sector) float64 96B 15.0 45.0 75.0 ... 285.0 315.0 345.0
sector_floor (sector) float64 96B 345.0 15.0 45.0 ... 255.0 285.0 315.0
west_east (point) float64 8B 5.147e+05
south_north (point) float64 8B 4.621e+06
* sector (sector) float64 96B 0.0 30.0 60.0 90.0 ... 270.0 300.0 330.0
Dimensions without coordinates: point
Data variables:
wdfreq (sector, point) float64 96B 0.05314 0.03321 ... 0.1148 0.0707
A (sector, point) float64 96B 5.47 5.346 5.746 ... 7.312 6.022
k (sector, point) float64 96B 1.924 2.145 2.594 ... 2.185 1.97
Attributes:
Conventions: CF-1.8
history: 2025-07-22T14:13:15+00:00:\twindkit==1.0.3.dev1+ga61276...
description: SerraSantaluzia
Package name: windkit
Package version: 1.0.3.dev1+ga612767
Creation date: 2025-07-22T14:13:16+00:00
Object type: Weibull Wind Climate
author: Default User
author_email: default_email@example.com
institution: Default Institution
We can check that the estimated mean wind speed of the WWC is close to the BWC above:
In [13]: mean_ws_wwc = wk.mean_wind_speed(wwc)
In [14]: print(mean_ws_wwc)
<xarray.DataArray (point: 1)> Size: 8B
array([6.24057066])
Coordinates:
height (point) float64 8B 25.3
crs int8 1B 0
west_east (point) float64 8B 5.147e+05
south_north (point) float64 8B 4.621e+06
Dimensions without coordinates: point
We see that the mean wind speed of the BWC and WWC differ by about 0.7% (6.29 vs 6.24 m/s). The weibull fit in WindKit (and in WAsP) uses the third moment of wind speed to fit Weibull parameters, thus preserving the mean power density well, but not the mean wind speed. We can check this by calculating and comparing the mean power densities pf the BWC and WWC.
In [15]: diff_prc = 100.0 * (1.0 - wk.mean_power_density(wwc) / wk.mean_power_density(bwc))
In [16]: print(diff_prc)
<xarray.DataArray (point: 1)> Size: 8B
array([2.22044605e-14])
Coordinates:
height (point) float64 8B 25.3
crs int8 1B 0
west_east (point) float64 8B 5.147e+05
south_north (point) float64 8B 4.621e+06
Dimensions without coordinates: point
We see that the difference in mean power density between the two is just 0.04%.
Using wind climates as site objects in PyWake
PyWAsP wind climates objects can be used as site objects for wind farm flow modeling in PyWake.
This is done by instanciating an XRSite using the py_wake.Site.XRSsite.from_pwc() method.
In [17]: import py_wake
In [18]: site = py_wake.site.XRSite.from_pwc(wwc)
In [19]: print(site.ds)
<xarray.Dataset> Size: 832B
Dimensions: (i: 1, wd: 13, ws: 23)
Coordinates:
h (i) float64 8B 25.3
x (i) float64 8B 5.147e+05
y (i) float64 8B 4.621e+06
* wd (wd) float64 104B 0.0 30.0 60.0 90.0 ... 300.0 330.0 360.0
* ws (ws) int64 184B 3 4 5 6 7 8 9 10 ... 19 20 21 22 23 24 25
Dimensions without coordinates: i
Data variables:
Sector_frequency (i, wd) float64 104B 0.05314 0.03321 ... 0.0707 0.05314
Weibull_A (i, wd) float64 104B 5.47 5.346 5.746 ... 7.312 6.022 5.47
Weibull_k (i, wd) float64 104B 1.924 2.145 2.594 ... 1.97 1.924
Speedup (i, wd) float64 104B 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0
TI (i, wd) float64 104B 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0
Attributes:
Conventions: CF-1.8
history: 2025-07-22T14:13:15+00:00:\twindkit==1.0.3.dev1+ga61276...
description: SerraSantaluzia
Package name: windkit
Package version: 1.0.3.dev1+ga612767
Creation date: 2025-07-22T14:13:16+00:00
Object type: Weibull Wind Climate
author: Default User
author_email: default_email@example.com
institution: Default Institution
sector_width: 30.0
Spatial inhomogeneity can be taken into account in PyWake by speedup and turning variables in the site object.
These variables track the relative speed-up and turning of the wind in the spatial domain during the wind farm flow modeling.
The py_wake.Site.XRSsite.from_pwc() method can take different speedup arguments for specifying the way to calculate speed-ups
between spatial points. By default, the “park” method is used, which calculates the sector-wise speed-ups relative
to the maximum mean wind speed in that sector.
Depending on how the speed-up is defined/calculated in the PyWake site object, the post-processing, or aggregation, of wind farm flow results should take this into account.