Performance comparison of four daily weather generators for historical period and downscaling future GCM scenarios
Bibliographic record
Abstract
ABSTRACT Weather generators (WG) are one of the major tools for downscaling future scenarios of general circulation models (GCMs) for regional, especially hydrological, climate change impact assessment. A key capability of WGs in the downscaling process is their ability to transfer changes in the various statistical characteristics of weather variables, as projected by GCMs, to the downscaled series. The purpose of this paper is to evaluate the performance of four WGs for transferring the delta change of various statistical characteristics of weather variables predicted by GCMs to downscaled series. The performances of the WGs for downscaling shared socioeconomic pathway (SSP) 119, SSP 370, and SSP 585 scenarios of the Canadian Earth System Model version-5 (CanESM5) in six sites are evaluated. The WGs include LARS-WG, M-LARS-WG, IWG2, and D-IWG. Based on the results, overall, the performance of all the WGs in downscaling future GCM scenarios is reduced compared to the historical series simulation. However, IWG2 and M-LARS-WG by having monthly components performed better than D-IWG and LARS-WG, respectively. So, it is suggested that in addition to evaluating the performance of WGs in the simulation of historical variables, their performance in downscaling the future scenarios of the climate models should also be evaluated.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".