climpred.metrics._reliability

climpred.metrics._reliability(forecast: xarray.Dataset, verif: xarray.Dataset, dim: Optional[Union[str, List[str]]] = None, **metric_kwargs: Any) → xarray.Dataset[source]

Reliability.

Returns the data required to construct the reliability diagram for an event. The the relative frequencies of occurrence of an event for a range of forecast probability bins.

Parameters

  • forecast – Raw forecasts with member dimension if logical provided in metric_kwargs. Probability forecasts in [0, 1] if logical is not provided.

  • verif – Verification data without member dim. Raw verification if logical provided, else binary verification.

  • dim – Dimensions to aggregate. Requires member if logical provided in metric_kwargs``to create probability forecasts. If ``logical not provided in metric_kwargs, should not include member.

  • logical – Function with bool result to be applied to verification data and forecasts and then mean("member") to get forecasts and verification data in interval [0, 1]. Passed via metric_kwargs.

  • probability_bin_edges (array_like, optional) – Probability bin edges used to compute the reliability. Bins include the left most edge, but not the right. Passed via metric_kwargs. Defaults to 6 equally spaced edges between 0 and 1+1e-8.

Returns

reliability – The relative frequency of occurrence for each probability bin

Notes

perfect

flat distribution

See also

Example

Define a boolean/logical: Function for binary scoring:

>>> def pos(x):
...     return x > 0  # checking binary outcomes
...

Option 1. Pass with keyword logical: (especially designed for PerfectModelEnsemble, where binary verification can only be created after comparison))

>>> HindcastEnsemble.verify(
...     metric="reliability",
...     comparison="m2o",
...     dim=["member", "init"],
...     alignment="same_verifs",
...     logical=pos,
... )
<xarray.Dataset>
Dimensions:               (lead: 10, forecast_probability: 5)
Coordinates:
  * lead                  (lead) int32 1 2 3 4 5 6 7 8 9 10
  * forecast_probability  (forecast_probability) float64 0.1 0.3 0.5 0.7 0.9
    SST_samples           (lead, forecast_probability) float64 22.0 5.0 ... 13.0
    skill                 <U11 'initialized'
Data variables:
    SST                   (lead, forecast_probability) float64 0.09091 ... 1.0
Attributes:
    prediction_skill_software:     climpred https://climpred.readthedocs.io/
    skill_calculated_by_function:  HindcastEnsemble.verify()
    number_of_initializations:     64
    number_of_members:             10
    alignment:                     same_verifs
    metric:                        reliability
    comparison:                    m2o
    dim:                           ['member', 'init']
    reference:                     []
    logical:                       Callable

Option 2. Pre-process to generate a binary forecast and verification product:

>>> HindcastEnsemble.map(pos).verify(
...     metric="reliability",
...     comparison="m2o",
...     dim=["init", "member"],
...     alignment="same_verifs",
... )
<xarray.Dataset>
Dimensions:               (lead: 10, forecast_probability: 5)
Coordinates:
  * lead                  (lead) int32 1 2 3 4 5 6 7 8 9 10
  * forecast_probability  (forecast_probability) float64 0.1 0.3 0.5 0.7 0.9
    SST_samples           (lead, forecast_probability) float64 22.0 5.0 ... 13.0
    skill                 <U11 'initialized'
Data variables:
    SST                   (lead, forecast_probability) float64 0.09091 ... 1.0
Attributes:
    prediction_skill_software:     climpred https://climpred.readthedocs.io/
    skill_calculated_by_function:  HindcastEnsemble.verify()
    number_of_initializations:     64
    number_of_members:             10
    alignment:                     same_verifs
    metric:                        reliability
    comparison:                    m2o
    dim:                           ['init', 'member']
    reference:                     []

Option 3. Pre-process to generate a probability forecast and binary verification product. because member not present in hindcast, use comparison="e2o" and dim="init":

>>> HindcastEnsemble.map(pos).mean("member").verify(
...     metric="reliability",
...     comparison="e2o",
...     dim="init",
...     alignment="same_verifs",
... )
<xarray.Dataset>
Dimensions:               (lead: 10, forecast_probability: 5)
Coordinates:
  * lead                  (lead) int32 1 2 3 4 5 6 7 8 9 10
  * forecast_probability  (forecast_probability) float64 0.1 0.3 0.5 0.7 0.9
    SST_samples           (lead, forecast_probability) float64 22.0 5.0 ... 13.0
    skill                 <U11 'initialized'
Data variables:
    SST                   (lead, forecast_probability) float64 0.09091 ... 1.0
Attributes:
    prediction_skill_software:     climpred https://climpred.readthedocs.io/
    skill_calculated_by_function:  HindcastEnsemble.verify()
    number_of_initializations:     64
    alignment:                     same_verifs
    metric:                        reliability
    comparison:                    e2o
    dim:                           init
    reference:                     []

previous

climpred.metrics._discrimination

next

climpred.metrics._rank_histogram