xskillscore.crps_gaussian

xskillscore.crps_gaussian(observations, mu, sig, dim=None, weights=None, keep_attrs=False)

Continuous Ranked Probability Score with a Gaussian distribution.

Parameters
  • observations (xarray.Dataset or xarray.DataArray) – The observations or set of observations.

  • mu (xarray.Dataset or xarray.DataArray) – The mean of the forecast normal distribution.

  • sig (xarray.Dataset or xarray.DataArray) – The standard deviation of the forecast distribution.

  • dim (str or list of str, optional) – Dimension over which to compute mean after computing crps_gaussian. 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.

Return type

xarray.Dataset or xarray.DataArray reduced by dimension dim

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))])
>>> mu = forecasts.mean('member')
>>> sig = forecasts.std('member')
>>> crps_gaussian(observations, mu, sig, dim='x')
<xarray.DataArray (y: 3)>
array([1.0349773 , 0.36521376, 0.39017126])
Coordinates:
  * y        (y) int64 0 1 2

See also

properscoring.crps_gaussian