Numerical Simulations of the 2013 Alberta Flood: Dynamics, Thermodynamics, and the Role of Orography
Bibliographic record
Abstract
Abstract The 19–21 June 2013 Alberta flood was the second costliest ($6 billion CAD) natural disaster in Canadian history, trailing only the 2016 Fort McMurray, Alberta, Canada, wildfires. One of the primary drivers was an extreme rainfall event that resulted in 75–150 mm of precipitation in the foothills west of Calgary, Canada. Here, the mesoscale dynamics and thermodynamics that contributed to the extreme rainfall event are elucidated through high-resolution numerical model simulations. In addition, terrain reduction model sensitivity experiments using Gaussian smoothing techniques quantify the importance of orography in producing the extreme rainfall event. It is suggested that the extreme rainfall event was initially characterized by the formation of a surface cyclone on the eastern side of the Canadian Rockies due to quasigeostrophic (QG) mechanisms. Orographic processes and diabatic heating feedbacks maintained the surface cyclone throughout the event, extending the duration of both easterly upslope flow and QG forcing for ascent in the flood region. The long-duration ascent and associated condensational heat release in the flood region vertically redistributed potential vorticity, anchoring and further extending the duration of the surface cyclone, upslope flow, and the rainfall. Although the magnitudes of ascent and precipitation were smaller in 10% and 25% reduced terrain simulations, only a terrain reduction of greater than 25% drastically altered the location and magnitude of the heaviest precipitation and the associated physical mechanisms.
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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".