Application of weather post-processing methods for operational ensemble hydrological forecasting on multiple catchments in Canada
Bibliographic record
Abstract
Hydrological forecasts often contain biases or uncertainty that make them less useful to water resources system managers. They can, however, be further improved using post-processing methods. Post-processing has the capability to reduce overall bias and improve the uncertainty quantification (spread), in order to enhance the usefulness of the forecasts in decision-making. In this study, a Quantile Mapping (QM) post-processing method was implemented on meteorological forecasts assessed in three different configurations: A monthly, a seasonal, and an annual quantile mapping schemes. The evaluation was carried out over 22 watersheds with different basin areas in the south of Canada. Post-processing methods were trained on ECMWF operational forecasts from 2015-2019 inclusively, then applied on forecasts from 2020 and fed to 8 assimilated hydrological models on each catchment. The hydrological forecasts for the year 2020 were generated at a lead time of 8 days and a timestep of 6 hours. The methodology and results were evaluated using the Continuous Ranked Probability Score (CRPS) metric. Results show that all three QM combinations improve the performance of the forecasts at the most distant lead times, showing significant improvements from day 4. The annual QM implementation was shown to perform the best, followed by seasonal or monthly, depending on the watershed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".