climpred.metrics._contingency

climpred.metrics._contingency(forecast, verif, score='table', dim=None, **metric_kwargs)

Contingency table.

Parameters

• forecast (xr.object) – Raw forecasts.

• verif (xr.object) – Verification data.

• dim (list or str) – Dimensions to aggregate.

• score (str) – Score derived from contingency table. Attribute from Contingency. Use score=table to return a contingency table or any other contingency score, e.g. score=hit_rate.

• observation_category_edges (array_like) – Category bin edges used to compute the observations CDFs. Bins include the left most edge, but not the right. Passed via metric_kwargs.

• forecast_category_edges (array_like) – Category bin edges used to compute the forecast CDFs. Bins include the left most edge, but not the right. Passed via metric_kwargs

See also

• Contingency

• http://www.cawcr.gov.au/projects/verification/

• https://xskillscore.readthedocs.io/en/stable/api.html#contingency-based-metrics # noqa

Example

>>> category_edges = np.array([-0.5, 0.0, 0.5, 1.0])
>>> HindcastEnsemble.verify(metric='contingency', score='table', comparison='m2o',
...     dim=['member', 'init'], alignment='same_verifs',
...     observation_category_edges=category_edges,
...     forecast_category_edges=category_edges).isel(lead=[0, 1]).SST
<xarray.DataArray 'SST' (lead: 2, observations_category: 3, forecasts_category: 3)>
array([[[221,  29,   0],
        [ 53, 217,   0],
        [  0,   0,   0]],

       [[234,  16,   0],
        [ 75, 194,   1],
        [  0,   0,   0]]])
Coordinates:
  * lead                          (lead) int32 1 2
    observations_category_bounds  (observations_category) <U11 '[-0.5, 0.0)' ...
    forecasts_category_bounds     (forecasts_category) <U11 '[-0.5, 0.0)' ......
  * observations_category         (observations_category) int64 1 2 3
  * forecasts_category            (forecasts_category) int64 1 2 3
    skill                         <U11 'initialized'

>>> # contingency-based dichotomous accuracy score
>>> category_edges = np.array([9.5, 10.0, 10.5])
>>> PerfectModelEnsemble.verify(metric='contingency', score='hit_rate',
...     comparison='m2c', dim=['member','init'],
...     observation_category_edges=category_edges,
...     forecast_category_edges=category_edges)
<xarray.Dataset>
Dimensions:  (lead: 20)
Coordinates:
  * lead     (lead) int64 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Data variables:
    tos      (lead) float64 1.0 1.0 1.0 1.0 0.9091 ... 1.0 1.0 1.0 nan 1.0