Environment Canada's Experimental Numerical Weather Prediction Systems for the Vancouver 2010 Winter Olympic and Paralympic Games
Bibliographic record
Abstract
The 2010 Winter Olympic and Paralympic Games took place in Vancouver, British Columbia, Canada, on 12–28 February and 12–21 March 2010, respectively. Weather forecasting presents specific challenges at the various Olympic venues, which are located in complex coastal terrain and are often characterized by tricky weather conditions, such as high winds, low visibility, and rapidly varying precipitation types and intensity. In addition to its current operational products, and in order to provide the best possible guidance and support to the Olympic Forecast Team, Environment Canada has developed several experimental numerical weather prediction systems for the games. These include 1) a regional ensemble prediction system (REPS), 2) high-resolution numerical modeling (down to 1-km horizontal grid spacing), and 3) surface modeling at the microscales (100-m grid spacing). The REPS is based on the limited-area version of the Global Environmental Multiscale model (GEM-LAM), with 20 members at 33-km horizontal grid spacing. The high-resolution models include 2.5- and 1-km grid-spacing configurations of the GEM-LAM, with improved cloud microphysics, geophysical fields, and radiation and cloud–radiation interactions. Finally, two innovative approaches are used to adapt and refine forecasts locally and to better predict surface characteristics, such as snow conditions and near-surface air temperatures. A microscale 2D surface system covers the Olympic venues with forcing from the operational regional and global models. Based on a similar strategy, a single-point model uses surface observations as forcing. The configurations of these experimental numerical weather prediction systems are described, together with some examples and verification results from the winters of 2008 and 2009 using the enhanced mesoscale observing network recently set up for the Olympics.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".