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’s member_dim. dim should contain member when comparison is probabilistic but should not contain member when comparison='e2o'. Defaults to None meaning that all dimensions other than lead 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 and verif 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.

  • resample_dim (str or list) –

    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 (holding skill, p, low_ci and high_ci) and skill (holding initialized, persistence and/or uninitialized):

  • 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