Datasets
Goals and principles for datasets used to compare spatial verification methods.
Bridging The Gap datasets
Comparison datasets for meta-verification
This collection supports meta-verification: comparing spatial verification methods for deterministic and ensemble forecasts. It aims to make existing datasets easier to access, propose new datasets, and help users interpret results and understand the role of each dataset or experiment.
Principles
- Surface variables: temperature, precipitation, and wind.
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Both deterministic and ensemble forecasts.
- Reuse existing datasets whenever possible.
- Avoid external challenges related to observation or analysis quality and common grids.
- Seek temporal and spatial overlap between real-world datasets.
- Keep data size manageable.
Original dataset work
Selected datasets
Ensemble
| Type | Dataset | Description |
|---|---|---|
| Synthetic | 1D idealized probabilistic fields | 1D idealized probabilistic fields |
| Real-world | LAM-EPS | Regional NWP |
| Real-world | TIGGE | Global |
Deterministic
| Type | Dataset | Description |
|---|---|---|
| Synthetic | New geometric test cases | Idealized cases |
| Synthetic | Idealized test cases | — |
| Perturbed | Fake Test Cases | Modified precipitation observations |
| Real-world | WP-MIP | Global; NWP vs AIWP vs hybrid |
Related papers
- ICP and MesoVICT website with their comparison datasets.
- Gilleland et al. (2009) – ICP article.
- Dorninger et al. (2018) – MesoVICT setup article.