Estimation of Point-to-Area Rainfall Frequency Relations in the Context of Climate Change
Bibliographic record
Abstract
The main objective of the present paper is to propose a methodology for constructing the point-to-area rainfall relations in the context of climate change. In particular, a comparative study was carried out to assess the accuracy and reliability of the proposed method as compared to other existing methods using rainfall and climate data available from different sources in the southern Quebec region in Canada: observed daily rainfall data from raingages, NCEP re-analysis data, Canadian Regional Climate Model (CRCM) output, and data given by different General Circulation Models (GCMs). The popular SDSM regression-based statistical downscaling method was used to describe the linkage between large-scale climate variables given by the considered GCMs and local rainfall characteristics. Results of this illustrative application have indicated that the use of the statistical downscaling method could provide accurate point-to-area rainfall relations as compared to the observed empirical ones, while without downscaling the results given by the GCMs and the CRCM were not accurate and displayed a very high level of uncertainty.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".