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]¶
Wrap py:func:bootstrap_compute for perfect-model framework.
- 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) – dimension to apply metric over. Defaults to:
["init", "member"]
.reference (str, list of str) – Type of reference forecasts with which to verify. One or more of
["persistence", "uninitialized", "climatology"]
. IfNone
or[]
, returns no p value.resample_dim (str or list) – 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
.** 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 kinds of
predictions:
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
for the persistence forecast computed by
compute_persistence or compute_persistence_from_first_lead depending on set_options(“PerfectModel_persistence_from_initialized_lead_0”)
climatology
- the different results:
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]