Assessment of a multimodel ensemble against an operational hydrological forecasting system
Bibliographic record
Abstract
Ensemble forecasts present an alternative to traditional deterministic forecasts by providing information about the likelihood of various outcomes. An ensemble can be constructed wherever errors are likely to occur within a hydrometeorological forecasting chain. This study compares the hydrological performance of a multimodel ensemble against deterministic forecasts issued by an operational forecasting system, in terms of accuracy and reliability. This comparison is carried out on 38 catchments in the province of Québec for more than 2 years of 6-day-ahead forecasts. The multimodel ensemble is comprised of 20 lumped conceptual models pooled together, while the reference forecast originates from an operational semi-distributed model. The results show that probabilistic forecast outperforms its deterministic counterpart and the deterministic operational forecast system, thanks to the role that each member plays inside the multimodel ensemble. This analysis demonstrates that the multimodel ensemble is potentially an operational tool, even though the specific setup for this study still suffers from underdispersion and needs to take into account additional sources of uncertainty to reach an optimal framework.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".