Extreme Weather Warnings: Perspectives on the Role of Broadcast Meteorologists
Bibliographic record
Abstract
A common theme across the globe is off-the-mark extreme weather warnings that lead to significant social, economic, physical, and environmental damages and, more broadly, the loss of public trust. The question is, how can the weather warning system be improved? The study is based on recent extreme weather-related warnings in Canada, including the 2021 tornado warning in Barrie, Ontario [CBC News (2021). Barrie, Ont., devastated by tornado that left 5-kilometre-long path of destruction, CBC News, Retrieved from https://www.cbc.ca/news/canada/toronto/barrie-tornado-ef-2-clean-up-1.6105258 .], the 2021 severe flooding due to atmospheric river phenomenon in British Columbia [Vancouver S (2021). Significant atmospheric river causing rainfall warnings across southern BC. Retrieved from https://vancouversun.com/news/local-news/significant-atmospheric-river-causing-rainfall-warnings-across-southern-b-c .], the 2023 tornado warning in Ottawa, ON [CBC News (2023). Tornado in Barrhaven damages about 125 homes, CBC News, Retrieved from https://www.cbc.ca/news/canada/ottawa/tornado-ottawa-barrhaven-july-13-1.6905782 .], and in 2023, flood alerts were found to be confusing and distracting in response efforts in Nova Scotia. Environment and Climate Change Canada (ECCC), a federal agency, monitors weather through instruments, radar, and satellite coverage. News outlets, media platforms, emergency management professionals and other stakeholders use this information in their work, functions and services. This research attempts to identify root causes for inefficiencies and areas for improvement in the Canadian system of extreme weather forecasting and warnings and their communication to the public. The approach to achieve this objective includes examining the current warning system, reviewing the fundamentals of risk communication, and in-depth interviews with broadcast meteorologists who routinely deliver the weather to the public across Canada.
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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 | 0.022 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.018 | 0.032 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.020 | 0.018 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".