climpred.bootstrap.bootstrap_compute¶
- climpred.bootstrap.bootstrap_compute(initialized, verif, hist=None, alignment='same_verifs', metric='pearson_r', comparison='m2e', dim='init', reference=None, resample_dim='member', sig=95, iterations=500, pers_sig=None, compute=<function compute_hindcast>, resample_uninit=<function bootstrap_uninitialized_ensemble>, **metric_kwargs)[source]¶
Bootstrap compute with replacement.
- Parameters
initialized (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 or list) – dimension(s) to apply metric over. Defaults to: “init”.
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) – dimension to resample from. Defaults to: “member”
“member”: select a different set of members from initialized
“init”: select a different set of initializations from initialized
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
.compute (Callable) – function to compute skill. Choose from [
climpred.prediction.compute_perfect_model()
,climpred.prediction.compute_hindcast()
].resample_uninit (Callable) – function to create an uninitialized ensemble from a control simulation or uninitialized large ensemble. Choose from: [
bootstrap_uninitialized_ensemble()
,bootstrap_uninit_pm_ensemble_from_control()
].** metric_kwargs (dict) – additional keywords to be passed to metric (see the arguments required for a given metric in Metrics).
- Returns
results –
(xr.Dataset): bootstrapped results for the three different skills:
initialized
for the initialized hindcastinitialized
and
describes skill due to initialization and external forcing
uninitialized
for the uninitialized/historical and approximates skill
from external forcing
persistence
climatology
- the different results:
verify skill
: skill valuesp
: p valuelow_ci
andhigh_ci
: high and low ends of confidence intervals
based on significance threshold
sig
- Reference:
Goddard et al. [2013]