Alignment of skill dimension naming

Handled in three different ways that can easily be aligned, but how?

• hindcast.verify(reference=['historical','persistence']).skill # initialized, historical, persistence
• PM allows both: historical and uninitialized pm.verify(reference=['historical','unintialized','persistence']).skill # ['initialized','historical','unintialized','persistence']
• bootstrap returns skill dimension: init, uninit, pers

proposed solution:

• hindcast: rename historical to uninitialized matching the dataset name
• PM: disallow historical
• rename bootstrap accordingly to initialized, uninitialized, persistence

are you OK with this? @bradyrx

[bradyrx]

I’ll switch to “Observations”. Addressed in https://github.com/pangeo-data/climpred/pull/459 since it’s minor.

[bradyrx]

probably we discussed this before. we have .add_observations() but technically this can also be a reconstruction. what about renaming to .add_verification() but leaving .add_observations() but with a deprec. future. warning? @bradyrx

I think we switched it to this since this is the most common way people are verifying a hindcast – against observations. People using this will understand that “reconstruction” is observations in the eyes of this system.

.add_verification() I don’t think is as clean of a reference since verification is the process of scoring the hindcast against your observational product. Really you’d want .add_verification_data() which is too long. I would prefer .add_observations() here just to keep it simple.