Evaluation of Modeled Lake Breezes Using an Enhanced Observational Network in Southern Ontario: Case Studies
Bibliographic record
Abstract
Abstract Canadian Global Environmental Multiscale (GEM) numerical model output was compared with the meteorological data from an enhanced observational network to investigate the model’s ability to predict Lake Ontario lake breezes and their characteristics for two cases in the Greater Toronto Area—one in which the large-scale wind opposed the lake breeze and one in which it was in the same direction as the lake breeze. An enhanced observational network of surface meteorological stations, a C-band radar, and two Doppler wind lidars were deployed among other sensors during the 2015 Pan and Parapan American Games in Toronto. The GEM model was run for three nested domains with grid spacings of 2.5, 1, and 0.25 km. Comparisons between the model predictions and ground-based observations showed that the model successfully predicted lake breezes for the two events. The results indicated that using GEM 1 and 0.25 km increased the forecast accuracy of the lake-breeze location, updraft intensity, and depth. The accuracy of the modeled lake breeze timing was approximately ±135 min. The model underpredicted the surface cooling caused by the lake breeze. The GEM 0.25-km model significantly improved the temperature forecast accuracy during the lake-breeze circulations, reducing the bias by up to 72%, but it mainly underpredicted the moisture and overpredicted the surface wind speed. Root-mean-square errors of wind direction forecasts were generally high because of large biases and high variability of errors.
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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 | 0.003 | 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".