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Abstract In addition to measuring forecast accuracy in terms of errors in a tropical system’s forecast track and other meteorological characteristics, it is important to measure the impact of those errors on society. With this in mind, the authors designed a coupled natural–human modeling framework with high-level representations of the natural hazard (hurricane), the human system (information flow, evacuation decisions), the built environment (road infrastructure), and connections between elements (forecasts and warning information, traffic). Using the model, this article begins exploring how tropical cyclone forecast errors impact evacuations and, in doing so, builds toward the development of new verification approaches. Specifically, the authors implement track errors representative of 2007 and 2022, and create situations with unexpected rapid intensification and/or rapid onset, and evaluate their impact on evacuations across real and hypothetical forecast scenarios (e.g., Hurricane Irma, Hurricane Dorian making landfall across east Florida). The results provide first-order evidence that 1) reduced forecast track errors across the 2007–22 period translate to improvements in evacuation outcomes across these cases and 2) unexpected rapid intensification and/or rapid onset scenarios can reduce evacuation rates, and increase traffic, across the most impacted areas. In exploring these relationships, the results demonstrate how experiments with coupled natural–human models can offer a societally relevant complement to traditional metrics of forecast accuracy. In doing so, this work points toward further development of natural–human models and associated methodologies to address these types of questions and improve forecast verification across the weather enterprise. 

In addition to measuring forecast accuracy in terms of errors in a tropical system’s forecast track and other meteorological characteristics, it is important to measure the impact of those errors on society. With this in mind, the authors designed a coupled natural–human modeling framework with high-level representations of the natural hazard (hurricane), the human system (information flow, evacuation decisions), the built environment (road infrastructure), and connec- tions between elements (forecasts and warning information, traffic). Using the model, this article begins exploring how tropical cyclone forecast errors impact evacuations and, in doing so, builds toward the development of new verification approaches. Specifically, the authors implement track errors representative of 2007 and 2022, and create situations with unexpected rapid intensifica- tion and/or rapid onset, and evaluate their impact on evacuations across real and hypothetical forecast scenarios (e.g., Hurricane Irma, Hurricane Dorian making landfall across east Florida). The results provide first-order evidence that 1) reduced forecast track errors across the 2007–22 period translate to improvements in evacuation outcomes across these cases and 2) unexpected rapid intensification and/or rapid onset scenarios can reduce evacuation rates, and increase traffic, across the most impacted areas. In exploring these relationships, the results demonstrate how experiments with coupled natural–human models can offer a societally relevant complement to traditional metrics of forecast accuracy. In doing so, this work points toward further development of natural–human models and associated methodologies to address these types of questions and improve forecast verification across the weather enterprise. 

Abstract Interdependent critical infrastructures in coastal regions, including transportation, electrical grid, and emergency services, are continually threatened by storm-induced flooding. This has been demonstrated a number of times, most recently by hurricanes such as Harvey and Maria, as well as Sandy and Katrina. The need to protect these infrastructures with robust protection mechanisms is critical for our continued existence along the world’s coastlines. Planning these protections is non-trivial given the rare-event nature of strong storms and climate change manifested through sea level rise. This article proposes a framework for a methodology that combines multiple computational models, stakeholder interviews, and optimization to find an optimal protective strategy over time for critical coastal infrastructure while being constrained by budgetary considerations. 

Abstract During the last few decades, scientific capabilities for understanding and predicting weather and climate risks have advanced rapidly. At the same time, technological advances, such as the Internet, mobile devices, and social media, are transforming how people exchange and interact with information. In this modern information environment, risk communication, interpretation, and decision-making are rapidly evolving processes that intersect across space, time, and society. Instead of a linear or iterative process in which individual members of the public assess and respond to distinct pieces of weather forecast or warning information, this article conceives of weather prediction, communication, and decision-making as an interconnected dynamic system. In this expanded framework, information and uncertainty evolve in conjunction with people’s risk perceptions, vulnerabilities, and decisions as a hazardous weather threat approaches; these processes are intertwined with evolving social interactions in the physical and digital worlds. Along with the framework, the article presents two interdisciplinary research approaches for advancing the understanding of this complex system and the processes within it: analysis of social media streams and computational natural–human system modeling. Examples from ongoing research are used to demonstrate these approaches and illustrate the types of new insights they can reveal. This expanded perspective together with research approaches, such as those introduced, can help researchers and practitioners understand and improve the creation and communication of information in atmospheric science and other fields.