Forensic Damage Assessment of a $3 Billion Urban Hailstorm – August 5, 2024 Calgary, Alberta, Canada
Bibliographic record
Abstract
On August 5, 2024, a major wind-driven hail event hit northern portions of the City of Calgary, Alberta—Canada's 5th most populous metropolitan area. Large volumes of hail with maximum diameters of 40 to 50 mm were accompanied by outflow winds gusting from 65 km/h to possibly over 100 km/h. This affected residential, commercial and industrial areas, as well as the international airport. With an estimated insured loss of $3.25 billion CDN, this event is both the costliest hailstorm and the costliest severe convective event in Canadian history.On the day following this event, the Northern Hail Project (NHP) deployed its Rapid Response Survey (RRS) for Major Urban Hailstorms, executing what is likely the most thorough damage survey of an urban hailstorm in North America. First, a team was deployed to conduct a scouting mission, locating the worst affected areas, as well as defining the boundaries of urban hail impacts. Following this, a rotating group of three teams was deployed to document— through extensive ground and aerial surveys— impacts in the worst affected areas. These teams made 49 drone flights, conducted over 100 eyewitness interviews, and drove over 1000 km. This was supplemented by outreach (using phone and social media) for locations with limited access, and by consultation with roofing and building repair companies.The survey documented damage to residential and commercial buildings, vehicles and other assets. Information was collected on damage to a variety of residential roofing and siding materials, including hail-resilient products. Single-, double- and triple-pane building windows were broken. Water penetrated the flat roofing systems of commercial and industrial buildings. Vehicle damage (~1/3 of the insured loss) was recorded in residential areas (personal and fleet vehicles) and vehicle storage lots. Notable damage at Calgary’s International Airport included roof damage and water penetration, as well as severe damage to dozens of commercial aircraft.Damage information will be compared to hail data from multiple sources, including hail samples collected by the NHP, and disdrometer stations within the CSSL’s disdrometer network. Findings will be used to support and refine the identification of hail-resistant construction materials, and to inform risk reduction measures and hail event scenario modelling. Our findings indicate that, given the significant amount of damage from this event and the potential for even worse hailstorms to hit the city, much greater damage could result from future events unless widespread damage reduction measures are implemented across Calgary.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".