Hydrological post-processing of streamflow forecasts issued from multimodel ensemble prediction systems
Bibliographic record
Abstract
Abstract Hydrological simulations and forecasts are subject to various sources of uncertainties. Thiboult et al. (2016) constructed a 50,000-member great ensemble that ultimately accounts for meteorological forcing uncertainty, initial condition uncertainty, and structural uncertainty. This large 50,000-member ensemble can also be separated into sub-components to untangle the three main sources of uncertainties mentioned above. However, in Thiboult et al. (2016) model outputs were simply pooled together, considering equiprobable members. This paper studies the use of Bayesian model averaging (BMA) to post-process multimodel hydrological forecasts. BMA assigns multiple sets of weights on different models and may then generate more skillful and reliable probabilistic forecasts. BMA weights explicitly quantify the level of confidence one can have regarding each candidate hydrological model and lead to a predictive probabilistic density function (PDF) containing information about uncertainty. The BMA scheme improves the overall quality of forecasts mainly by maintaining the ensemble dispersion with the lead time. It also has the ability to improve the reliability and skill of multimodel systems that only include two sources of uncertainties that the 50,000-member great ensemble using all forecasting tools (i.e., multimodel, EnKF, and meteorological ensemble forcing) could predict jointly. Furthermore, Thiboult et al. (2016) showed that the meteorological forecasts they used were somehow biased and unreliable on some catchments. The BMA scheme is capable to improve the accuracy and reliability of the hydrological forecasts in that case as well.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".