A Comparison of the Canadian Global and Regional Meteorological Ensemble Prediction Systems for Short-Term Hydrological Forecasting
Bibliographic record
Abstract
Abstract Meteorological ensemble prediction systems (M-EPS) are generally set up at lower resolution than for their deterministic counterparts. Operational hydrologists are thus more prone to selecting deterministic meteorological forecasts for driving their hydrological models. Limited-area implementation of meteorological models may become a convenient way of providing the sought after higher-resolution meteorological ensemble forecasts. This study aims to compare the Canadian operational global EPS (M-GEPS) and the experimental regional EPS (M-REPS) for short-term operational hydrological ensemble forecasting over eight watersheds, for which performance and reliability was assessed. Higher-resolution deterministic forecasts were also available for the study. Results showed that both M-EPS provided better performance than their deterministic counterparts when comparing their mean continuous ranked probability score (MCRPS) and mean absolute error (MAE), especially beyond a 24-h horizon. The global and regional M-EPS led to very similar performance in terms of RMSE, but the latter produced a larger spread and improved reliability. The M-REPS was deemed superior to its operational global counterpart, especially for its ability to better depict forecast uncertainty.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".