climpred.reference.compute_persistence#

climpred.reference.compute_persistence(initialized: Dataset, verif: Dataset, metric: str | Metric = 'acc', comparison: str | Comparison = 'm2o', dim: str | List[str] | None = 'init', alignment: str = 'same_verifs', **metric_kwargs: Any) → Dataset[source]#

Compute the skill of a persistence forecast from a simulation.

This function unlike compute_persistence_from_first_lead() is not sensitive to comparison. Requires climpred.set_options(PerfectModel_persistence_from_initialized_lead_0=False).

Parameters:

  • initialized – The initialized ensemble.

  • verif – Verification data.

  • metric – Metric name to apply at each lag for the persistence computation. Default: "pearson_r".

  • alignment – which inits or verification times should be aligned?

    • "maximize": maximize the degrees of freedom by slicing initialized and verif to a common time frame at each lead.

    • "same_inits": slice to a common init frame prior to computing metric. This philosophy follows the thought that each lead should be based on the same set of initializations.

    • "same_verif": slice to a common/consistent verification time frame prior to computing metric. This philosophy follows the thought that each lead should be based on the same set of verification dates.

  • dim – dimension to apply metric over.

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

Returns:

pers –

Results of persistence forecast with the input metric

applied.

Reference:

  • Chapter 8 (Short-Term Climate Prediction) in Van den Dool, Huug. Empirical methods in short-term climate prediction. Oxford University Press, 2007.

See also