Forecasting of Surface Winds over Eastern Canada Using the Canadian Offline Land Surface Modeling System
Bibliographic record
Abstract
Abstract The capability of the Canadian land surface external modeling system known as the Global Environmental Multiscale Surface (GEM-SURF) system with respect to surface wind predictions is evaluated. Based on the Interactions between Soil, Biosphere, and Atmosphere (ISBA) land surface scheme, and an exponential power law adjusted to the local stability conditions for the prediction of surface winds, the system allows decoupling of surface processes from those of the free atmosphere and enables high resolutions at the surface as dictated by the small-scale heterogeneities of the surface boundary. The simulations are driven by downscaled forecasts from the Regional Deterministic Prediction System, the 15-km Canadian regional operational modeling system. High-resolution, satellite-derived datasets of orography, vegetation, and soil cover are used to depict the surface boundary. The integration domains cover Canada’s eastern provinces at resolutions ranging from that of the driving model to resolutions similar to those of the geophysical datasets. The GEM-SURF predictions outperform those of the driving operational model. Reduction of the standard error and improvement of the model skill is seen as resolution increases, for all wind speeds. Further, the bias error is reduced in association with a rise in the corresponding value of the roughness length. For all examined resolutions GEM-SURF’s predictions are shown to be superior to those obtained through a simple statistical downscaling. In the prospect of the future development of a multicomponent system that provides wind forecasts at levels of wind energy generation, GEM-SURF’s potential for improved scores at the surface and its limited requirements in computer resources make it a suitable surface component of such a system.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".