climpred.prediction.compute_perfect_model¶
-
climpred.prediction.
compute_perfect_model
(ds, control, metric='pearson_r', comparison='m2e', dim=None, add_attrs=True, **metric_kwargs)[source]¶ Compute a predictability skill score for a perfect-model framework simulation dataset.
Parameters: - ds (xarray object) – ensemble with dims
lead
,init
,member
. - control (xarray object) – control with dimension
time
. - 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) – 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)
- ds (xarray object) – ensemble with dims