How to calculate seasonal temporal correlations?
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Assessment
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Newbie friendliness
- 35/100
Research direction
Start with iris.analysis.stats.pearsonr and review how iris.coord_categorisation.add_season and Cube.aggregated_by represent seasonal groups. Determine the API needed to calculate Pearson correlations for each grid point without requiring direct Cube.data access. Done means a documented, tested Iris analysis capability matching the requested seasonal correlation workflow.
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Description
Discussed in https://github.com/SciTools/iris/discussions/5520
Originally posted by krikru September 27, 2023
If I have two iris cubes, A and B, both on the same grid and with the same time points, how can I calculate the intra-seasonal (according the seasons that are added when calling add_season) Pearson correlations between A and B for each grid point? Can I do this somehow by only using Iris functions (i.e., not extracting the data property from both cubes and using NumPy), and without using the definition of the Pearson correlation?
Pearson correlation can usually be calculated with pearsonr, but this function doesn't seem to enable aggregation by an auxiliary variable, and I need to aggregate by season.
We might want to include a version of @krikru's code within iris.analysis. Please vote if you'd like this feature!
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