- climpred.reference.compute_uninitialized(initialized: xarray.Dataset, uninit: xarray.Dataset, verif: xarray.Dataset, metric: Union[str, climpred.metrics.Metric] = 'pearson_r', comparison: Union[str, climpred.comparisons.Comparison] = 'e2o', dim: Optional[Union[str, List[str]]] = 'time', alignment: str = 'same_verifs', **metric_kwargs: Optional[Any])[source]#
Verify an uninitialized ensemble against verification data.
Based on Decadal Prediction protocol, this should only be computed for the first lag and then projected out to any further lags being analyzed.
initialized – Initialized ensemble.
uninit – Uninitialized ensemble.
verif – Verification data with some temporal overlap with the uninitialized ensemble.
metric – Metric used in comparing the uninitialized ensemble with the verification data.
- How to compare the uninitialized ensemble to the verification data:
“e2o” : ensemble mean to verification data (Default)
“m2o” : each member to the verification data
dim – dimension to apply metric over.
alignment – which inits or verification times should be aligned?
"maximize": maximize the degrees of freedom by slicing
verifto a common time frame at each lead.
"same_inits": slice to a common init frame prior to computing metric. This philosophy follows the thought that each lead should be based on the same set of initializations.
same_verif: slice to a common/consistent verification time frame prior to computing metric. This philosophy follows the thought that each lead should be based on the same set of verification dates.
** metric_kwargs – additional keywords to be passed to metric
uninit_skill – Results from comparison at the first lag.