climpred.prediction.compute_perfect_model

climpred.prediction.compute_perfect_model(init_pm, control=None, metric='pearson_r', comparison='m2e', dim=['member', 'init'], add_attrs=True, **metric_kwargs)[source]

Compute a predictability skill score for a perfect-model framework simulation dataset.

Parameters

  • init_pm (xarray object) – ensemble with dims lead, init, member.

  • control (xarray object) – NOTE that this is a legacy argument from a former release. control is not used in compute_perfect_model anymore.

  • metric (str) – metric name, see climpred.utils.get_metric_class() and (see Metrics).

  • comparison (str) – comparison name defines what to take as forecast and verification (see climpred.utils.get_comparison_class() and Comparisons).

  • dim (str or list of str) – dimension to apply metric over. default: [‘member’, ‘init’]

  • add_attrs (bool) – write climpred compute args to attrs. default: True

  • metric_kwargs (**) – additional keywords to be passed to metric. (see the arguments required for a given metric in metrics.py)

Returns

skill score with dimensions as input ds

without dim.

Return type

skill (xarray object)