windkit.create_tswc#

windkit.create_tswc(output_locs, date_range=None, not_empty=True, seed=9876538, weibull_A=8.0, weibull_k=2.1, speed_tau=14400.0, direction_mean=240.0, direction_kappa=1.5, direction_tau=21600.0, spatial_correlation_length=10000.0)[source]#

Create time series wind climate dataset.

When not_empty=True (default), the data variables are filled with temporally and spatially correlated synthetic wind data using an AR(1) process in Gaussian space transformed to a Weibull distribution for speed, and a circular AR(1) with von Mises innovations for direction.

Parameters:

  • output_locs (xarray.Dataset) – Output geospatial information.

  • date_range (pandas.DatetimeIndex or None) – Time range as a pandas DateTimeIndex. If None is passed, a default range of one year (2001) at 10 min resolution is created.

  • not_empty (bool) – If True, the empty dataset is filled with realistic synthetic wind data. Defaults to True.

  • seed (int) – Seed for the random data, defaults to 9876538.

  • weibull_A (float) – Weibull scale parameter [m/s]. Defaults to 8.0.

  • weibull_k (float) – Weibull shape parameter. Defaults to 2.1.

  • speed_tau (float) – Autocorrelation e-folding timescale for speed [s]. Defaults to 14400 (4 h).

  • direction_mean (float) – Prevailing wind direction [degrees]. Defaults to 240.

  • direction_kappa (float) – Von Mises concentration around prevailing direction. Defaults to 1.5.

  • direction_tau (float) – Autocorrelation timescale for direction [s]. Defaults to 21600 (6 h).

  • spatial_correlation_length (float) – Spatial e-folding length scale for inter-point correlation [same unit as coordinates]. Defaults to 10000.

Returns:

ds – Time series wind climate dataset.

Return type:

xarray.Dataset

Examples

Create a single-point TSWC with default parameters:

>>> import pandas as pd
>>> from windkit.spatial import create_dataset
>>> import windkit as wk
>>> locs = create_dataset(10.0, 55.0, 100.0, 4326).drop_vars("output")
>>> dates = pd.date_range("2020-01-01", periods=8760, freq="h")
>>> tswc = wk.create_tswc(locs, date_range=dates)
>>> tswc
<xarray.Dataset> ...

Create a multi-point TSWC with custom Weibull parameters:

>>> import numpy as np
>>> locs = create_dataset(
...     [10.0, 10.1], [55.0, 55.1], [100.0, 100.0],
...     4326, struct="point",
... ).drop_vars("output")
>>> tswc = wk.create_tswc(locs, date_range=dates, weibull_A=9.5, weibull_k=2.3)

Create an empty TSWC (NaN-filled):

>>> tswc_empty = wk.create_tswc(locs, date_range=dates, not_empty=False)
>>> np.all(np.isnan(tswc_empty["wind_speed"].values))
True
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