Application of GIS in downscaling regional climate model results over the province of Ontario
Bibliographic record
Abstract
Climate change is a significant and long-term change in the weather patterns over periods ranging from decades to millions of years. The impacts of climate change have been drawn more and more worldwide attention. To study the impacts, general circulation models (GCMs) were developed to simulate climate change at a global scale. The climate information obtained from GCMs is usually at a fairly coarse resolution. In comparison, regional climate models (RCMs) work in a small area of interest and can provide climate information at resolution as fine as 25 - 50 km. When higher resolution climate information is needed and the applied RCMs are incapable of undertaking the task, statistical downscaling techniques can be introduced to acquire the desired climate information. In this study, three interpolation methods are applied to downscale regional climate model (RCM) results for higher resolution climate information at 10 km. The results indicated that the three interpolation methods could generate high-quality estimates at 10 km grids. The downscaled RCM results approximated to the 10 km official data which was published by Agriculture and Agri-Food Canada, Government of Canada. Compared with the results of IDW and spline methods, the results obtained from kriging method generated smoother interpolation map and showed modest variations in the difference map. All the three interpolation methods could fulfill the task of downscaling the RCM results from 25 km to 10 km. Overall, kriging interpolation method showed better performance than the other two methods in this study.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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 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".