compute_persistence(name=None, metric='pearson_r', max_dof=False)¶
Verify against a persistence forecast of the observations/verification data.
This simply applies some metric between the observational product and itself out to some lag (e.g., an ACF in the case of ‘pearson_r’).
The persistence forecast is computed starting from the initialization date and moving forward one time step. Some protocols suggest that the “lead one” persistence forecast is actually from the time step prior to initialization. This will be implemented as an option in a future version of
- name (str, default None) – Short name of observations/verification data
with which to compute the persistence forecast. If
None, compute for all observations/verification data.
- metric (str, default 'pearson_r') – Metric to apply for verification.
- max_dof (bool, default False) – If
True, maximize the degrees of freedom for each lag calculation.
Dataset of persistence forecast results (if
None), or dictionary of Datasets with keys corresponding to observations/verification data short name.
- Chapter 8 (Short-Term Climate Prediction) in Van den Dool, Huug. Empirical methods in short-term climate prediction. Oxford University Press, 2007.
- name (str, default None) – Short name of observations/verification data with which to compute the persistence forecast. If