Application of weather post-processing methods for operational ensemble hydrological forecasting on multiple catchments in Canada
Bibliographic record
Abstract
Hydrological forecasts contain biases that need to be addressed for their effective use in operational decisionmaking \nin water resources management. Performing post-processing allows reducing the overall systematic \nbias while improving the distribution and accuracy of hydrological forecasts. In this study, a Quantile Mapping \n(QM) post-processing method was applied on weather forecasts following three temporal configurations \n(monthly, seasonal, and annual) of the quantile mapping scheme. The evaluation encompasses 20 catchments in \nsouthern Canada, employing a leave-one-out approach with the QM method on ECMWF ensemble weather \nforecasts spanning 2015–2020 inclusively. These processed forecasts are subsequently utilized as forcings for \neight hydrological models, generating ensemble streamflow forecasts over a 6-year period with a lead time of 10 \ndays and a sub-daily timestep of 6 h. The performance of the QM method is mainly assessed using the Continuous \nRanked Probability Score (CRPS) metric, in complement with a forecast reliability score (ABDU) and a forecast \nsharpness metric (NMIQR). Significant improvements are discerned in precipitation forecasts upon the application \nof QM. Notably, these improvements are translated into enhanced hydrological forecasts for over half of \nthe catchments studied (55 %). Surprisingly, no discernible differences in performance are observed among the \nthree QM configurations in most catchments. Interestingly, there are watersheds where the implementation of \nQM exhibit either poorer or no change in performance and sharpness compared to raw forecasts
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".