climpred.classes.HindcastEnsemble.verify¶
- HindcastEnsemble.verify(reference=None, metric=None, comparison=None, dim=None, alignment=None, **metric_kwargs)[source]¶
Verifies the initialized ensemble against observations.
Note
This will automatically verify against all shared variables between the initialized ensemble and observations/verification data.
- Parameters
reference (str, list of str) – Type of reference forecasts to also verify against the observations. Choose one or more of [‘uninitialized’, ‘persistence’, ‘climatology’]. Defaults to None.
metric (str,
Metric
) – Metric to apply for verification. see metrics.comparison (str,
Comparison
) – How to compare to the observations/verification data. See comparisons.dim (str, list of str) – Dimension(s) to apply metric over.
dim
is passed on to xskillscore.{metric} and includes xskillscore’smember_dim
.dim
should containmember
whencomparison
is probabilistic but should not containmember
whencomparison=e2o
. Defaults toNone
meaning that all dimensions other thanlead
are reduced.alignment (str) –
which inits or verification times should be aligned?
’maximize’: maximize the degrees of freedom by slicing
hind
andverif
to 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 (optional) – arguments passed to
metric
.
- Returns
Dataset with dimension skill reduced by dim containing initialized and reference skill(s) if specified.
Example
Root mean square error (
rmse
) comparing every member with the verification (m2o
) over the same verification time (same_verifs
) for all leads reducing dimensionsinit
andmember
:>>> HindcastEnsemble.verify(metric='rmse', comparison='m2o', ... alignment='same_verifs', dim=['init','member']) <xarray.Dataset> Dimensions: (lead: 10) Coordinates: * lead (lead) int32 1 2 3 4 5 6 7 8 9 10 skill <U11 'initialized' Data variables: SST (lead) float64 0.08516 0.09492 0.1041 ... 0.1525 0.1697 0.1785
Pearson’s Anomaly Correlation (‘acc’) comparing the ensemble mean with the verification (
e2o
) over the same initializations (same_inits
) for all leads reducing dimensioninit
while also calculating reference skill for thepersistence
,climatology
anduninitialized
forecast.>>> HindcastEnsemble.verify(metric='acc', comparison='e2o', ... alignment='same_inits', dim='init', ... reference=['persistence', 'climatology' ,'uninitialized']) <xarray.Dataset> Dimensions: (lead: 10, skill: 4) Coordinates: * lead (lead) int32 1 2 3 4 5 6 7 8 9 10 * skill (skill) <U13 'initialized' 'persistence' ... 'uninitialized' Data variables: SST (skill, lead) float64 0.9023 0.8807 0.8955 ... 0.9078 0.9128 0.9159