climpred.bootstrap.bootstrap_hindcast¶
-
climpred.bootstrap.
bootstrap_hindcast
(hind, hist, reference, metric='pearson_r', comparison='e2r', dim='init', sig=95, bootstrap=500, pers_sig=None, **metric_kwargs)[source]¶ - Bootstrap compute with replacement. Wrapper of
- py:func:bootstrap_compute for hindcasts.
Parameters: - hind (xr.Dataset) – prediction ensemble.
- reference (xr.Dataset) – reference simulation.
- hist (xr.Dataset) – historical/uninitialized simulation.
- metric (str) – metric. Defaults to ‘pearson_r’.
- comparison (str) – comparison. Defaults to ‘e2r’.
- dim (str) – dimension to apply metric over. default: ‘init’
- 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.
- bootstrap (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
- init_ci (xr.Dataset): confidence levels of init_skill
- uninit_ci (xr.Dataset): confidence levels of uninit_skill
- p_uninit_over_init (xr.Dataset): 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. Defaults to None.
- pers_ci (xr.Dataset): confidence levels of pers_skill
- p_pers_over_init (xr.Dataset): 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. Defaults to None.
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_hindcast