Configuration Recipes

Common edits to config.yml.

Disable the CNN (run LSEQM only)

Set blend_alpha to 1.0:

deep_learning:
  blend_alpha: 1.0

The CNN still trains but its output is multiplied by zero in the blend. To skip training entirely, comment out the CNN steps in notebooks/02_lseqmdl_bias_correction.ipynb.

Disable the confidence mask (uniform blend)

station_density:
  use_confidence_mask: false

The blend then uses a flat blend_alpha everywhere. Useful for an ablation: compare LSEQM+DL with the mask off and on to quantify what the mask is doing.

Change the GPD threshold

statistical_params:
  gpd_threshold_percentile: 90   # was 80

Higher thresholds give a cleaner separation between the gamma body and the GPD tail but reduce the number of exceedances per dekad. If you hit “too few exceedances” errors, lower the threshold or raise wet_day_threshold so that dry days are excluded.

Switch to non-interactive (batch) mode

runtime:
  interactive: false
  existing_file_action: "skip"      # or "overwrite", "abort"
  existing_model_action: "use_existing"

In skip mode, a re-run will not regenerate files that already exist. Use this when iterating on a single dekad - flip to overwrite for the cell you are changing.

Tighten the upper cap

statistical_params:
  upper_cap_threshold_percentile: 99.0   # was 99.9

Clips ~1% of the tail. Use if you see unrealistic spikes after the GPD step (rare but possible when exceedances are very heavy-tailed).

Run on Colab without a local checkout

directories:
  main_dir: "/content/hybrid-bias-correction"
  input_dir: "{main_dir}/data/example_bali"
  output_dir: "{main_dir}/data/example_bali/output"

The Bali config already has these values commented in; uncomment the Colab line and comment the WSL line.

Use a denser CNN

deep_learning:
  num_filters_1: 64        # was 32
  num_filters_2: 128       # was 64
  dense_layer_size: 256    # was 128

This nearly quadruples the parameter count of the Flatten -> Dense layer. Only worth doing if the val_loss plateau on the default network is well above your noise floor.