Document saved statistics variable names

[pragnyanramtha]

Describe your changes

This updates the README section for output.splitting.compute_statistics to describe how computed statistics are stored in the output dataset.

The added documentation explains the {output_variable}__{split_name}__{operation} naming pattern, gives concrete examples such as state__train__mean, and notes which dimensions remain after reduction. It also clarifies that diff_ statistics are calculated only for variables that span the splitting dimension.

Issue Link

Closes #98

Type of change

• Documentation (Addition or improvements to documentation)

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• My branch is up-to-date with the target branch.
• I have performed a self-review of my code.
• I have updated the documentation to cover introduced code changes.
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Validation:

• git diff --check
• python -m pre_commit run trailing-whitespace --files README.md
• python -m pre_commit run end-of-file-fixer --files README.md

[Copilot]

Pull request overview

Documentation-only update clarifying how computed split statistics are stored in generated output datasets.

Changes:

• Adds naming pattern documentation for saved statistics variables.
• Provides concrete statistic variable examples.
• Documents retained dimensions and diff_ statistic behavior.

Comments suppressed due to low confidence (2)

README.md:286

• Typo: “normalisating” should be “normalising”.

4. Splitting and calculation of statistics of the output variables, using the `splitting` section. The `output.splitting.splits` attribute defines the individual splits to create (for example `train`, `val` and `test`) and `output.splitting.dim` defines the dimension to split along. The `compute_statistics` can be optionally set for a given split to calculate the statistical properties requested (for example `mean`, `std`) any method available on `xarray.Dataset.{op}` can be used. In addition methods prefixed by `diff_` (so the operational would be listed as `diff_{op}`) compute a statistic based on difference of consecutive time-steps, e.g. `diff_mean` to compute the `mean` of the difference between consecutive timesteps (these are used for normalisating increments). The `dims` attribute defines the dimensions to calculate the statistics over (for example `grid_index` and `time`).

README.md:286

• This is a run-on sentence; the compute_statistics explanation and the xarray.Dataset.{op} note should be separated or joined with punctuation so the documentation reads clearly.

4. Splitting and calculation of statistics of the output variables, using the `splitting` section. The `output.splitting.splits` attribute defines the individual splits to create (for example `train`, `val` and `test`) and `output.splitting.dim` defines the dimension to split along. The `compute_statistics` can be optionally set for a given split to calculate the statistical properties requested (for example `mean`, `std`) any method available on `xarray.Dataset.{op}` can be used. In addition methods prefixed by `diff_` (so the operational would be listed as `diff_{op}`) compute a statistic based on difference of consecutive time-steps, e.g. `diff_mean` to compute the `mean` of the difference between consecutive timesteps (these are used for normalisating increments). The `dims` attribute defines the dimensions to calculate the statistics over (for example `grid_index` and `time`).

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