“This Isn’t a Hurricane, This is a Flood Event”: A Qualitative Analysis of National Weather Service Forecaster Messaging during Hurricane Florence
Bibliographic record
Abstract
Abstract Hurricanes threaten communities in complex and evolving ways due to storm characteristics and geography, as well as demographic and cultural factors. Risks to people in the path of these storms are compounded when wind and water hazards co-occur, such as tornadoes and flash floods, a hazard often referred to as TORFFs. For National Weather Service (NWS) forecasters, messaging these co-occurring threats poses many challenges, including the ongoing assessment and prioritization of which threat is likely to have the greatest impacts and the communication of risks to different publics. In this research, we focus on Hurricane Florence, a category 1 hurricane that produced historic flooding and some wind-related threats, including tornadoes, across the mid-Atlantic coast in September 2018. Through inductive, qualitative analysis of 33 semi-structured interviews with NWS forecasters responsible for issuing alerts during Florence, we examine the intricacies of messaging flood and wind threats as they evolved over the hurricane’s life cycle. Our results show that forecasters aimed to amplify messaging for flood threats over wind threats during Florence. Along with forecast details and expected impacts, motivations for this messaging choice included the potential for flood fatalities and concerns that the public would not understand the severity of compounding hurricane threats. One reason for this disconnect may be the emphasis placed by experts in weather prediction on the Saffir–Simpson hurricane wind scale (SSHWS) as a metric of hurricane severity. Forecaster messaging strategies were informed by these concerns, which may also have implications for how messaging should be shaped in the future.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".