climpred.classes.HindcastEnsemble.bootstrap¶

HindcastEnsemble.
bootstrap
(metric=None, comparison=None, dim=None, alignment=None, reference=None, iterations=None, sig=95, resample_dim='member', pers_sig=None, **metric_kwargs)[source]¶ Bootstrap with replacement according to Goddard et al. 2013.
 Parameters
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.reference (str, list of str) – Type of reference forecasts with which to verify. One or more of [‘persistence’, ‘uninitialized’]. If None or empty, returns no p value.
alignment (str) –
which inits or verification times should be aligned?
’maximize’: maximize the degrees of freedom by slicing
init
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.
iterations (int) – Number of resampling iterations for bootstrapping with replacement. Recommended >= 500.
sig (int, default 95) – Significance level in percent for deciding whether uninitialized and persistence beat initialized skill.
dimension to resample from. default: ‘member’.
’member’: select a different set of members from hind
’init’: select a different set of initializations from hind
pers_sig (int, default None) – If not None, the separate significance level for persistence.
**metric_kwargs (optional) – arguments passed to
metric
.
 Returns
with dimensions
result
(holdingskill
,p
,low_ci
andhigh_ci
) andskill
(holdinginitialized
,persistence
and/oruninitialized
): result=’verify skill’, skill=’initialized’:
mean initialized skill
 result=’high_ci’, skill=’initialized’:
high confidence interval boundary for initialized skill
 result=’p’, skill=’uninitialized’:
p value of the hypothesis that the difference of skill between the initialized and uninitialized simulations is smaller or equal to zero based on bootstrapping with replacement.
 result=’p’, skill=’persistence’:
p value of the hypothesis that the difference of skill between the initialized and persistence simulations is smaller or equal to zero based on bootstrapping with replacement.
 Return type
xr.Datasets