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Despite improvements in forecasting extreme weather events, noncompliance with weather warnings among the public remains a problem. Although there are likely many reasons for noncompliance with weather warnings, one important factor might be people's past experiences with false alarms. The research presented here explores the role of false alarms in weather‐related decision making. Over a series of trials, participants used an overnight low temperature forecast and advice from a decision aid to decide whether to apply salt treatment to a town's roads to prevent icy conditions or take the risk of withholding treatment, which resulted in a large penalty when freezing temperatures occurred. The decision aid gave treatment recommendations, some of which were false alarms, i.e., treatment was recommended but observed temperatures were above freezing. The rate at which the advice resulted in false alarms was manipulated between groups. Results suggest that very high and very low false alarm rates led to inferior decision making, but that lowering the false alarm rate slightly did not significantly affect compliance or decision quality. However, adding a probabilistic uncertainty estimate in the forecasts improved both compliance and decision quality. These findings carry implications about how weather warnings should be communicated to the public.

Abstract
What is the best way to communicate the risk of rare but extreme weather to the public? One suggestion is to communicate the relative risk of extreme weather in the form of odds ratios; but, to the authors' knowledge, this suggestion has never been tested systematically. The experiment reported here provides an empirical test of this hypothesis. Participants performed a realistic computer simulation task in which they assumed the role of the manager of a road maintenance company and used forecast information to decide whether to take precautionary action to prevent icy conditions on a town's roads. Participants with forecasts expressed as odds ratios were more likely to take appropriate precautionary action on a single target trial with an extreme low temperature forecast than participants using deterministic or probabilistic forecasts. However, participants using probabilistic forecasts performed better on trials involving weather within the normal range than participants with only deterministic forecast information. These results may provide insight into how best to communicate extreme weather risk. This paper offers clear evidence that people given relative risk information are more inclined to take precautionary action when threatened with an extreme weather event with a low probability than people given only single-value or probabilistic forecasts.

Abstract
The likelihood of floods and other potentially destructive natural phenomena is often expressed as a return period or recurrence interval, such as a 100-yr flood. However, the expression might give users the impression that the event will occur exactly once within the described period, obscuring the intended probabilistic meaning. If so, users may think a flood is less likely when one has just occurred or more likely when it has not, leading to a "flood is due" effect. This hypothesis was tested experimentally in two studies reported here. Participants were given either a return period or a probability expression and asked to rate flood likelihood and concern. Flood recency was also manipulated. The results from both studies support a flood is due effect when the return period expression is used. In the return period condition alone, participants rated floods as more likely and expressed greater concern when no flood had occurred recently. When no likelihood information was conveyed in the control condition, the opposite effect was observed. Participants rated flood likelihood as higher and expressed greater concern when a flood had occurred recently. Participants using the percent chance expression were least affected by flood recency. This adds to the growing body of research suggesting that nonexperts can benefit from probabilistic weather forecasts.