xskillscore.threshold_brier_score

xskillscore.threshold_brier_score(observations, forecasts, threshold, issorted=False, member_dim='member', dim=None, weights=None, keep_attrs=False)

Calculate the Brier scores of an ensemble for exceeding given thresholds.

Parameters:

observations (xarray.Dataset or xarray.DataArray) – The observations or set of observations.
forecasts (xarray.Dataset or xarray.DataArray) – Forecast with required member dimension member_dim.
threshold (scalar or 1d scalar) – Threshold values at which to calculate exceedence Brier scores.
issorted (bool, optional) – Optimization flag to indicate that the elements of ensemble are already sorted along axis.
member_dim (str, optional) – Name of ensemble member dimension. By default, ‘member’.
dim (str or list of str, optional) – Dimension over which to compute mean after computing threshold_brier_score. Defaults to None implying averaging over all dimensions.
weights (xr.DataArray with dimensions from dim, optional) – Weights for weighted.mean(dim). Defaults to None, such that no weighting is applied.
keep_attrs (bool) – If True, the attributes (attrs) will be copied from the first input to the new one. If False (default), the new object will be returned without attributes.

Returns:

(If threshold is a scalar, the result will have the same shape as observations. Otherwise, it will have an additional final dimension corresponding to the threshold levels. Not implemented yet.)

Return type:

xarray.Dataset or xarray.DataArray

Examples

>>> observations = xr.DataArray(
...     np.random.normal(size=(3, 3)),
...     coords=[("x", np.arange(3)), ("y", np.arange(3))],
... )
>>> forecasts = xr.DataArray(
...     np.random.normal(size=(3, 3, 3)),
...     coords=[("x", np.arange(3)), ("y", np.arange(3)), ("member", np.arange(3))],
... )
>>> threshold = [0.2, 0.5, 0.8]
>>> xs.threshold_brier_score(observations, forecasts, threshold)
<xarray.DataArray (threshold: 3)> Size: 24B
array([0.27160494, 0.34567901, 0.18518519])
Coordinates:
  * threshold  (threshold) float64 24B 0.2 0.5 0.8