The effect of likelihood and impact information on public response to severe weather warnings.

Meteorological services are increasingly moving away from issuing weather warnings based on the exceedance of meteorological thresholds (e.g., windspeed), toward risk-based (or "impact-based") approaches. The UK Met Office's National Severe Weather Warning Service has been a pioneer of this approach, issuing yellow, amber, and red warnings based on an integrated evaluation of information about the likelihood of occurrence and potential impact severity. However, although this approach is inherently probabilistic, probabilistic information does not currently accompany public weather warning communications. In this study, we explored whether providing information about the likelihood and impact severity of forecast weather affected subjective judgments of likelihood, severity, concern, trust in forecast, and intention to take protective action. In a mixed-factorial online experiment, 550 UK residents from 2 regions with different weather profiles were randomly assigned to 1 of 3 Warning Format conditions (Color-only, Text, Risk Matrix) and presented with 3 warnings: high-probability/moderate-impact (amber HPMI); low-probability/high-impact (amber); high-probability/high-impact (red). Amongst those presented with information about probability and impact severity, red high-likelihood/high-impact warnings elicited the strongest ratings on all dependent variables, followed by amber HPMI warnings. Amber low-likelihood/high-impact warnings elicited the lowest perceived likelihood, severity, concern, trust, and intention to take protective responses. Taken together, this indicates that UK residents are sensitive to probabilistic information for amber warnings, and that communicating that severe events are unlikely to occur reduces perceived risk, trust in the warning, and behavioral intention, even though potential impacts could be severe. We discuss the practical implications of this for weather warning communication.

Quantifying the effects of cold waves on carbon monoxide poisoning: A time-stratified case-crossover study in Jinan, China.

Background: Previous studies have shown that carbon monoxide (CO) poisoning occurs mostly in winter and is associated with severe cold weather (e.g., ice storms, temperature drops). However, according to previous studies, the impact of low temperature on health has a delayed effect, and the existing research cannot fully reveal the delayed effect of cold waves on CO poisoning. Objectives: The purpose of this study is to analyze the temporal distribution of CO poisoning in Jinan and to explore the acute effect of cold waves on CO poisoning. Methods: We collected emergency call data for CO poisoning in Jinan from 2013 to 2020 and used a time-stratified case-crossover design combined with a conditional logistic regression model to evaluate the impact of the cold wave day and lag 0-8 days on CO poisoning. In addition, 10 definitions of a cold wave were considered to evaluate the impact of different temperature thresholds and durations. Results: During the study period, a total of 1,387 cases of CO poisoning in Jinan used the emergency call system, and more than 85% occurred in cold months. Our findings suggest that cold waves are associated with an increased risk of CO poisoning in Jinan. When P01, P05, and P10 (P01, P05, and P10 refer to the 1st, 5th, and 10th percentiles of the lowest temperature, respectively) were used as temperature thresholds for cold waves, the most significant effects (the maximum OR value, which refers to the risk of CO poisoning on cold wave days compared to other days) were 2.53 (95% CI:1.54, 4.16), 2.06 (95% CI:1.57, 2.7), and 1.49 (95% CI:1.27, 1.74), respectively. Conclusion: Cold waves are associated with an increased risk of CO poisoning, and the risk increases with lower temperature thresholds and longer cold wave durations. Cold wave warnings should be issued and corresponding protective policies should be formulated to reduce the potential risk of CO poisoning.

The association between weather warnings and hip fractures in the Republic of Ireland.

This study examined the relationship between hip fractures and weather warnings with the hypothesis higher rates of fractures occur during warnings. National hip fracture database and weather warning records were utilised. Higher rates of hip fractures were found with severe warnings. This has implications for planning in future severe warnings. BACKGROUND: Hip fractures represent a significant burden on the Irish Health system with 3666 in 2020. The Irish National Meteorological Service operates a colour coded warning system. Yellow being least severe, while orange represents weather with capacity to impact individuals in affected areas and red represents advice to protect themselves and property. Previous studies investigated the seasonality of hip fractures, which remains but none have investigated the relationship between weather warnings and rates of hip fractures. The hypothesis was that increasing weather warnings would be associated with increased hip fractures. The aim was to investigate the relationship between weather warnings and hip fractures in the Republic of Ireland. METHODS: Comparison of national weather warning archives from 2013 to 2019 to Fracture Database records. Reviews assessed whether fractures occurred on days a weather warning was in place in the individual's local county. A statistical analysis of warning features and their relationship to hip fractures was then performed. Fractures and weather warnings were stratified by county with both a panel and case crossover analysis performed. RESULTS: There was a tendency towards increased incidence of hip fractures with weather warnings in adjusted analysis (IRR 1.02; 95%CI 0.99-1.06; p-value 0.123). Orange warnings were associated with a statistically higher incidence of fractures (IRR 1.06; 1.01-1.12; p-value 0.026). In both panel and case crossover analysis, both orange and yellow warnings were associated with fractures. Red warnings were associated with a lower incidence of fracture on day of warning (adjusted incidence rate ratio 0.92; 0.70-1.22; p-value 0.569) but a higher incidence on the following day (adjusted incidence rate ratio 1.14; 0.88-1.46; p-value 0.313). CONCLUSION: An increased incidence of hip fractures appears to occur during weather warnings. Consideration should be given when preparing for periods of extreme weather, ensuring sufficiently resources.

Leveraging Artificial Intelligence and Fleet Sensor Data towards a Higher Resolution Road Weather Model.

Road weather conditions such as ice, snow, or heavy rain can have a significant impact on driver safety. In this paper, we present an approach to continuously monitor the road conditions in real time by equipping a fleet of vehicles with sensors. Based on the observed conditions, a physical road weather model is used to forecast the conditions for the following hours. This can be used to deliver timely warnings to drivers about potentially dangerous road conditions. To optimally process the large data volumes, we show how artificial intelligence is used to (1) calibrate the sensor measurements and (2) to retrieve relevant weather information from camera images. The output of the road weather model is compared to forecasts at road weather station locations to validate the approach.

Quantifying the Effects of Climate Factors on Carbon Monoxide Poisoning: A Retrospective Study in Taiwan.

Background: Carbon monoxide (CO) poisoning is the leading cause of poisoning death worldwide, but associations between CO poisoning and weather remain unclear. Objective: To quantify the influence of climate parameters (e.g., temperature, relative humidity, and wind speed) on the incidence risk of acute CO poisoning in Taiwan. Methods: We used negative binomial mixed models (NBMMs) to evaluate the influence of weather parameters on the incidence risk of acute CO poisoning. Subgroup analyses were conducted, based on the seasonality and the intentionality of acute CO poisoning cases. Results: We identified a total of 622 patients (mean age: 32.9 years old; female: 51%) with acute CO poisoning in the study hospital. Carbon monoxide poisoning was associated with temperature (beta: -0.0973, rate ratio (RR): 0.9073, p < 0.0001) but not with relative humidity (beta: 0.1290, RR: 1.1377, p = 0.0513) or wind speed (beta: -0.4195, RR: 0.6574, p = 0.0806). In the subgroup analyses, temperature was associated with the incidence of intentional CO poisoning (beta: 0.1076, RR: 1.1136, p = 0.0333) in spring and unintentional CO poisoning (beta: -0.1865, RR: 0.8299, p = 0.0184) in winter. Conclusion: Changes in temperature affect the incidence risk for acute CO poisoning, but the impact varies with different seasons and intentionality in Taiwan. Our findings quantify the effects of climate factors and provide fundamental evidence for healthcare providers to develop preventative strategies to reduce acute CO poisoning events.