climpred.bootstrap.bootstrap_perfect_model

climpred.bootstrap.bootstrap_perfect_model(init_pm, control, metric='pearson_r', comparison='m2e', dim=None, reference=None, resample_dim='member', sig=95, iterations=500, pers_sig=None, **metric_kwargs)[source]
Bootstrap compute with replacement. Wrapper of

py:func:bootstrap_compute for perfect-model framework.

Parameters

  • hind (xr.Dataset) – prediction ensemble.

  • verif (xr.Dataset) – Verification data.

  • hist (xr.Dataset) – historical/uninitialized simulation.

  • metric (str) – metric. Defaults to ‘pearson_r’.

  • comparison (str) – comparison. Defaults to ‘m2e’.

  • dim (str) – dimension to apply metric over. default: [‘init’, ‘member’].

  • 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.

  • 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

  • sig (int) – Significance level for uninitialized and initialized skill. Defaults to 95.

  • pers_sig (int) – Significance level for persistence skill confidence levels. Defaults to sig.

  • iterations (int) – number of resampling iterations (bootstrap with replacement). Defaults to 500.

  • metric_kwargs (**) – additional keywords to be passed to metric (see the arguments required for a given metric in Metrics).

Returns

(xr.Dataset): bootstrapped results for the three different kinds of

predictions:

  • initialized for the initialized hindcast hind and describes skill due

to initialization and external forcing

  • uninitialized for the uninitialized/historical and approximates skill

from external forcing

  • persistence for the persistence forecast

  • climatology

the different results:

  • skill: skill values

  • p: p value

  • low_ci and high_ci: high and low ends of confidence intervals based

on significance threshold sig

Return type

results

Reference:

  • Goddard, L., A. Kumar, A. Solomon, D. Smith, G. Boer, P. Gonzalez, V. Kharin, et al. “A Verification Framework for Interannual-to-Decadal Predictions Experiments.” Climate Dynamics 40, no. 1–2 (January 1, 2013): 245–72. https://doi.org/10/f4jjvf.

See also

  • climpred.bootstrap.bootstrap_compute

  • climpred.prediction.compute_perfect_model