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climpred.prediction.compute_perfect_model

climpred.prediction.compute_perfect_model#

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

Compute a predictability skill score in a perfect-model framework.

Parameters:

  • initialized (xr.Dataset) – ensemble with dims lead, init, member.

  • control (xr.Dataset) – 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’]

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

Returns:

skill (xr.Dataset) –

skill score with dimensions as input ds

without dim.

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