Verification of the Weather Research and Forecasting Model for Alberta
Bibliographic record
Abstract
We simulated three heavy summer precipitation events in Alberta, Canada, using the Weather Research and Forecasting (WRF) model and compared the output precipitation data with observations to determine model accuracy. Each storm was simulated multiple times, using five different cumulus parameterization schemes and three grid resolutions. The explicit scheme, when simulated using 6 km grid resolution, was found to be the most accurate scheme when simulating these heavy precipitation events. We also used the WRF model to perform daily forecasts during 2011-2012. The forecasts include 2-meter maximum and minimum temperatures, 10-meter wind speed, sea-level surface pressure, and daily accumulated precipitation. When compared against observations, the WRF forecasts showed seasonal differences and tendencies such as forecasting a smaller range of diurnal temperatures than observed. WRF was found to have high skill scores when forecasting maximum temperature against climatology and persistence forecasts, but was less skillful when forecasting minimum temperatures.
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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 | 0.000 | 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) | 0.001 | 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 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".