Simplifying a hydrological ensemble prediction system with a backward greedy selection of members – Part 2: Generalization in time and space
Bibliographic record
Abstract
Abstract. An uncertainty cascade model applied to stream flow forecasting seeks to evaluate the different sources of uncertainty of the complex rainfall-runoff process. The current trend focuses on the combination of Meteorological Ensemble Prediction Systems (MEPS) and hydrological model(s). However, the number of members of such a HEPS may rapidly increase to a level that may not be operationally sustainable. This article evaluates a 94% simplification of an initial 800-member HEPS, forcing 16 lumped rainfall-runoff models with the European Center for Medium-range Weather Forecasts (ECMWF MEPS). More specifically, it tests the time (local) and space (regional) generalization ability of the simplified 50-member HEPS obtained using a methodology that combines 4 main aspects: (i) optimizing information of the short-length series using k-folds cross-validation, (ii) implementing a backward greedy selection technique, (iii) guiding the selection with a linear combination of diversified scores, and (iv) formulating combination case studies at the cross-validation stage. At the local level, the transferability of the 9th day member selection was proven for the other 8 forecast horizons at an 82% success rate. At the regional level, a good performance was also achieved when the 50-member HEPS was applied to a neighbouring catchment within the same cluster. Diversity, defined as hydrological model complementarities addressing different aspects of a forecast, was identified as the critical factor for proper selection applications.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".