Integrating storm surge modeling with traffic data analysis to evaluate the effectiveness of hurricane evacuation

An integrated storm surge modeling and traffic analysis were conducted in this study to assess the effectiveness of hurricane evacuations through a case study of Hurricane Irma. The Category 5 hurricane in 2017 caused a record evacuation with an estimated 6.8 million people relocating statewide in Florida. The Advanced Circulation (ADCIRC) model was applied to simulate storm tides during the hurricane event. Model validations indicated that simulated pressures, winds, and storm surge compared well with observations. Model simulated storm tides and winds were used to estimate the area affected by Hurricane Irma. Results showed that the storm surge and strong wind mainly affected coastal counties in south-west Florida. Only moderate storm tides (maximum about 2.5 m) and maximum wind speed about 115 mph were shown in both model simulations and Federal Emergency Management Agency (FEMA) post-hurricane assessment near the area of hurricane landfall. Storm surges did not rise to the 100-year flood elevation level. The maximum wind was much below the design wind speed of 150–170 mph (Category 5) as defined in Florida Building Code (FBC) for south Florida coastal areas. Compared with the total population of about 2.25 million in the six coastal counties affected by storm surge and Category 1–3 wind, the statewide evacuation of approximately 6.8 million people was found to be an over-evacuation due mainly to the uncertainty of hurricane path, which shifted from south-east to south-west Florida. The uncertainty of hurricane tracks made it difficult to predict the appropriate storm surge inundation zone for evacuation. Traffic data were used to analyze the evacuation traffic patterns. In south-east Florida, evacuation traffic started 4 days before the hurricane’s arrival. However, the hurricane path shifted and eventually landed in south-west Florida, which caused a high level of evacuation traffic in south-west Florida. Over-evacuation caused Evacuation Traffic Index (ETI) to increase to 200% above normal conditions in some sections of highways, which reduced the effectiveness of evacuation. Results from this study show that evacuation efficiency can be improved in the future by more accurate hurricane forecasting, better public awareness of real-time storm surge and wind as well as integrated storm surge and evacuation modeling for quick response to the uncertainty of hurricane forecasting.

Robust Predictors for Seasonal Atlantic Hurricane Activity Identified with Causal Effect Networks

Atlantic hurricane activity varies substantially from year to year and so do the associated damages. Longer-term forecasting of hurricane risks is a key element to reduce damages and societal vulnerabilities by enabling targeted disaster preparedness and risk reduction measures. While the immediate synoptic drivers of tropical cyclone formation and intensification are increasingly well understood, precursors of hurricane activity on longer time-horizons are still not well established. Here we use a causal network-based algorithm to identify physically motivated late-spring precursors of seasonal Atlantic hurricane activity. Based on these precursors we construct seasonal forecast models with competitive skill compared to operational forecasts. We present a skillful model to forecast July to October cyclone activity at the beginning of April. Earlier seasonal hurricane forecasting provides a multi-month lead time to implement more effective disaster risk reduction measures. Our approach also highlights the potential of applying causal effects network analysis in seasonal forecasting.

Can You See It? Perceived Variance in Scatterplot Visualization

Understanding uncertainty in spatial domains (such as hurricane forecasting) is both important and challenging, often leading to overconfidence and underestimation of variability. The underlying source of the difficulty is not well understood, as this complex domain requires perceptual, attentional, and memory related cognitive abilities. The present study explores the impact of perception on understanding of variability in an abstract spatial task. Individuals were shown a scatter chart type display of possible endpoints of an uncertain trajectory and asked to make two separate judgements of variability: adjusting a circle to encompass 75% of endpoints and estimating the likelihood the trajectory would fall within an experimenter defined probe circle. Calibration to changes in variability were calculated for each individual on both measures and compared against each other. Results indicate a lack of sensitivity in interpreting visualized spatial uncertainty among many individuals, with performance differing depending on the probe method employed. Implications are discussed.

Anchoring and Adjustment in Uncertain Spatial Trajectory Prediction

Objective: The aim of this study was to explore the impact of prior information on spatial prediction and understanding of variability. Background: In uncertain spatial prediction tasks, such as hurricane forecasting or planning search-and-rescue operations, decision makers must consider the most likely case and the distribution of possible outcomes. Base performance on these tasks is varied (and in the case of understanding the distribution, often poor). Humans must update mental models and predictions with new information, sometimes under cognitive workload. Method: In a spatial-trajectory prediction task, participants were anchored on accurate or inaccurate information, or not anchored, regarding the future behavior of an object (both average behavior and the variability). Subsequently, they predicted an object’s future location and estimated its likelihood at multiple locations. In a second experiment, participants repeated the process under varying levels of external cognitive workload. Results: Anchoring influenced understanding of most likely predicted location, with fairly rapid adjustment following inaccurate anchors. Increasing workload resulted in decreased overall performance and an impact on the adjustment component of the task. Overconfidence was present in all conditions. Conclusion: Prior information exerted short-term influence on spatial predictions. Cognitive load impaired users’ ability to effectively adjust to new information. Accurate graphical anchors did not improve user understanding of variability. Application: Prior briefings or forecasts about spatiotemporal trajectories affect decisions even in the face of initial contradictory information. To best support spatial prediction tasks, efforts also need to be made to separate extraneous load-causing tasks from the process of integrating new information. Implications are discussed.

Will Global Warming Make Hurricane Forecasting More Difficult?

Hurricane track forecasts have improved steadily over the past few decades, yet forecasting hurricane intensity remains challenging. Of special concern are the rare instances of tropical cyclones that intensify rapidly just before landfall, catching forecasters and populations off guard, thereby risking large casualties. Here, we review two historical examples of such events and use scaling arguments and models to show that rapid intensification just before landfall is likely to become increasingly frequent and severe as the globe warms.

Observing System Simulation Experiments to Assess the Potential Impact of New Observing Systems on Hurricane Forecasting

Observing System Simulation Experiments (OSSEs) are an important tool for evaluating the potential impact of new or proposed observing systems, as well as for evaluating trade-offs in observing system design, and in developing and assessing improved methodology for assimilating new observations. Extensive OSSEs have been conducted at the National Aeronautical and Space Administration (NASA) Goddard Space Flight Center (GSFC) and the National Oceanic and Atmospheric Administration (NOAA) Atlantic Oceanographic and Meteorological Laboratory (AOML) over the last three decades. These OSSEs determined correctly the quantitative potential for several proposed satellite observing systems to improve weather analysis and prediction prior to their launch; evaluated trade-offs in orbits, coverage, and accuracy for space-based wind lidars; and were used in the development of the methodology that led to the first beneficial impacts of satellite surface winds on numerical weather prediction. This paper summarizes early applications of global OSSEs to hurricane track forecasting and new experiments using both global and regional models. These latter experiments are aimed at assessing potential impact on hurricane track and intensity prediction over the oceans and at landfall.