I try to make statistical downscaling experiments readable after the training run is over. When data boundaries, configuration changes, and evaluation scripts are scattered across a repository, even a useful result can become hard to interpret weeks later.
Why Experiment Boundaries Matter
A stronger workflow makes the main boundaries visible:
- Train and validation regions or time windows are defined explicitly.
- Normalization statistics are fit only on training data.
- Configuration changes are recorded with the run.
- Results are compared across repeatable evaluation scripts.
- Checkpoints can resume interrupted training without silently changing the setup.
Example Configuration Shape
data:
source: ERA5
target: PRISM
normalize_with: train_split
model:
family: ConvLSTM
evaluation:
metrics:
- RMSE
seeds: [3, 7, 11]Reporting
When I report a result, I want the comparison, split, and preprocessing to be explicit. A single metric is useful, but it is not the whole story. Spatial diagnostics, border behavior, and failure cases often explain more than an aggregate number alone.
| Practice | Reason |
|---|---|
| Training-only normalization | Reduces leakage risk |
| Multiple seeds | Helps avoid single-run interpretation |
| Structured logs | Makes comparisons easier to inspect |
| Checkpoint metadata | Makes resumed training easier to trust |
For active research, this discipline keeps my implementation work connected to scientific interpretation without overstating unfinished results.