climpred.prediction.compute_persistence(hind, verif, metric='pearson_r', max_dof=False, **metric_kwargs)[source]

Computes the skill of a persistence forecast from a simulation.

  • hind (xarray object) – The initialized ensemble.
  • verif (xarray object) – Verification data.
  • metric (str) – Metric name to apply at each lag for the persistence computation. Default: ‘pearson_r’
  • max_dof (bool) –

    If True, maximize the degrees of freedom by slicing hind and verif to a common time frame at each lead.

    If False (default), then slice to a common time frame prior to computing metric. This philosophy follows the thought that each lead should be based on the same set of initializations.

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

Results of persistence forecast with the input metric applied.

Return type:

pers (xarray object)

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