The Experimental Warning Program of NOAA’s Hazardous Weather Testbed

NOAA’s Hazardous Weather Testbed (HWT) is a physical space and research framework to foster collaboration and evaluate emerging tools, technology, and products for NWS operations. The HWT’s Experimental Warning Program (EWP) focuses on research, technology, and communication that may improve severe and hazardous weather warnings and societal response. The EWP was established with three fundamental hypotheses: 1) collaboration with operational meteorologists increases the speed of the transition process and rate of adoption of beneficial applications and technology, 2) the transition of knowledge between research and operations benefits both the research and operational communities, and 3) including end-users in experiments generates outcomes that are more reliable and useful for society. The EWP is designed to mimic the operations of any NWS Forecast Office, providing the opportunity for experiments to leverage live and archived severe weather activity anywhere in the United States. During the first decade of activity in the EWP, 15 experiments covered topics including: new radar and satellite applications, storm-scale numerical models and data assimilation, total lightning use in severe weather forecasting, and multiple social science and end-user topics. The experiments range from exploratory and conceptual research to more controlled experimental design to establish statistical patterns and causal relationships. The EWP brought more than 400 NWS forecasters, 60 emergency managers, and 30 broadcast meteorologists to the HWT to participate in live demonstrations, archive events, and data-denial experiments influencing today’s operational warning environment and shaping the future of warning research, technology, and communication for years to come.

Effects of Probabilistic Risk Situation Awareness Tool (RSAT) on Aeronautical Weather-Hazard Decision Making

We argue that providing cumulative risk as an estimate of the uncertainty in dynamically changing risky environments can help decision-makers meet mission-critical goals. Specifically, we constructed a simplified aviation-like weather decision-making task incorporating Next-Generation Radar (NEXRAD) images of convective weather. NEXRAD radar images provide information about geographically referenced precipitation. NEXRAD radar images are used by both pilots and laypeople to support decision-making about the level of risk posed by future weather-hazard movements. Using NEXRAD, people and professionals have to infer the uncertainty in the meteorological information to understand current hazards and extrapolate future conditions. Recent advancements in meteorology modeling afford the possibility of providing uncertainty information concerning hazardous weather for the current flight. Although there are systematic biases that plague people’s use of uncertainty information, there is evidence that presenting forecast uncertainty can improve weather-related decision-making. The current study augments NEXRAD by providing flight-path risk, referred to as the Risk Situational Awareness Tool (RSAT). RSAT provides the probability that a route will come within 20 NMI radius (FAA recommended safety distance) of hazardous weather within the next 45 min of flight. The study evaluates four NEXRAD displays integrated with RSAT, providing varying levels of support. The “no” support condition has no RSAT (the NEXRAD only condition). The “baseline” support condition employs an RSAT whose accuracy is consistent with current capability in meteorological modeling. The “moderate” support condition applies an RSAT whose accuracy is likely at the top of what is achievable in meteorology in the near future. The “high” support condition provides a level of support that is likely unachievable in an aviation weather decision-making context without considerable technological innovation. The results indicate that the operators relied on the RSAT and improved their performance as a consequence. We discuss the implications of the findings for the safe introduction of probabilistic tools in future general aviation cockpits and other dynamic decision-making contexts. Moreover, we discuss how the results contribute to research in the fields of dynamic risk and uncertainty, risk situation awareness, cumulative risk, and risk communication.

Can the Issuance of Hazardous-Weather Warnings Inform the Attribution of Extreme Events to Climate Change?

When extreme weather occurs, the question often arises whether the event was produced by climate change. Two types of errors are possible when attempting to answer this question. One type of error is underestimating the role of climate change, thereby failing to properly alert the public and appropriately stimulate efforts at adaptation and mitigation. The second type of error is overestimating the role of climate change, thereby elevating climate anxiety and potentially derailing important public discussions with false alarms. Long before societal concerns about global warming became widespread, meteorologists were addressing essentially the same trade-off when faced with a binary decision of whether to issue a warning for hazardous weather. Here we review forecast–verification statistics such as the probability of detection (POD) and the false alarm ratio (FAR) for hazardous-weather warnings and examine their potential application to extreme-event attribution in connection with climate change. Empirical and theoretical evidence suggests that adjusting tornado-warning thresholds in an attempt to reduce FAR produces even larger reductions in POD. Similar tradeoffs between improving FAR and degrading POD are shown to apply using a rubric for the attribution of extreme high temperatures to climate change. Although there are obviously significant differences between the issuance of hazardous-weather warnings and the attribution of extreme events to global warming, the experiences of the weather forecasting community can provide qualitative guidance for those attempting to set practical thresholds for extreme-event attribution in a changing climate.