THE REALISM OF STOCHASTIC WEATHER GENERATORS IN RISK DISCOVERY
Bibliographic record
Abstract
Weather generators reproduce artificial climate time series that are commonly used for hydrological modeling and climate adaptation studies.To examine the representativeness of a stochastically generated climate time series, a novel stochastic method is suggested where these time series are projected in two spaces (the Climate Statistics Space -CSS; and the Risk and Performance Indicators Space -RPIS).A visual inspection as well as the Mahalanobis distance are used to assess the two spaces relative position and their proximity to the points representing the observations.The dimensions of the CSS are a subset of climate statistics, while the dimensions of the RPIS are a set of risk and performance indicators calculated using streamflow time series.A rainfall-runoff model is used to convert all climate time series from the CSS into streamflow time series in the RPIS.Three stochastic weather generators were used in this study: The Weather Generator École de Technologie Supérieure (WeaGETS), the Multisite Stochastic Weather Generator (MulGETS) using two different generation algorithms, and a k-nearest neighbour weather generator.Each generator was used to construct precipitation, maximum and minimum temperatures time series representing the historical period.The suggested approach was tested on a 41-years-long climate and flow time series from South Nation watershed in Eastern Ontario, Canada.The MulGETS model was able to perform well where the point representing the observations was centered inside the cloud of points representing the synthetic time series in some CSS.
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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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".