Power spectra plots

[frazane]

Adds an optional power-spectra diagnostic to the experiment pipeline, so we can compare the effective resolution of model outputs against the truth and spot over-smoothing or spurious small-scale noise that point metrics like RMSE do not reveal.

The diagnostic computes 2D variance spectra (DCT by default, FFT optional) per participant and lead time, averages over initialisation times, and overlays each model against the truth with grid-resolution, effective-resolution and Nyquist reference lines.

What changed

• New src/spectra package: numpy-only spectral core (DCT/FFT with variance-conserving normalisation and radial averaging), ICON native-to-regular regridding with runtime grid detection, plus field extraction and IO helpers.
• Snakemake wiring (rules/spectra.smk): per-participant compute, aggregate over inits, overlay plot, gated behind an experiment target.
• Config: new experiment.spectra section (enable flag, method, lead times, variables, optional init-hour subset) with validation and regenerated JSON schema.
• Unit tests for the spectral core, regridding and IO.

Notes

• Disabled by default; opt in with experiment.spectra.enabled: true.
• ICON native grids are regridded to a regular grid before the transform; the eckit grid file is fetched automatically.

Example output, T_2M at +24h: