The Role of Statistical Models in the Prediction of Tropical Cyclone Motion

Tropical cyclones (tropical storms, hurricanes, typhoons, etc.) occur over many of the earth's tropical marine areas. Responsibility for tracking and predicting the future course of these storms is assigned to one or more domestic or foreign meteorological services. These services routinely activate a number of statistical and dynamical prediction models as objective guidance preparatory to issuing official forecasts on these storms. In this article, the role of the statistical models in this process is examined.     

Doubly periodic non-homogeneous Poisson models for hurricane data

Non-homogeneous Poisson processes with periodic claim intensity rate have been proposed as claim counts in risk theory. Here a doubly periodic Poisson model with short- and long-term trends is studied. Beta-type intensity functions are presented as illustrations. The likelihood function and the maximum likelihood estimates of the model parameters are derived.Doubly periodic Poisson models are appropriate when the seasonality does not repeat exactly the same short-term pattern every year, but has a peak intensity that varies over a longer period. This reflects periodic environments like those forming hurricanes, in alternating El Niño/La Niña years. An application of the model to the data set of Atlantic hurricanes affecting the United States (1899–2000) is discussed in detail.     

Communicating during times of crises: An analysis of news releases from the federal government before,during, and after hurricanes Katrina and Rita

This paper examines the framing of crisis communication strategies before, during, and after Hurricanes Katrina and Rita by analyzing news releases from the Federal Emergency Management Agency, Homeland Security, and U.S. Senators and Governors from Mississippi, Louisiana, and Texas. A qualitative content analysis found that all entities analyzed did not acknowledge their parts in the crises, placed blame on others, and tried to distance themselves from the crises. State leadership also used a unifying approach where they included themselves in the hardships affected residents were experiencing.     

Modelling extreme wind speeds in regions prone to hurricanes

Extreme wind speeds can arise as the result of a simple pressure differential, or a complex dynamic system such as a tropical storm. When sets of record values comprise a mixture of two or more different types of event, the standard models for extremes based on a single limiting distribution are not applicable. We develop a mixture model for extreme winds arising from two distinct processes. Working with sequences of annual maximum speeds obtained at hurricane prone locations in the USA, we take a Bayesian approach to inference, which allows the incorporation of prior information obtained from other sites. We model the extremal behaviour for the contrasting wind climates of Boston and Key West, and show that the standard models can give misleading results at such locations.     

Why Near‐Miss Events Can Decrease an Individual's Protective Response to Hurricanes

Prior research shows that when people perceive the risk of some hazardous event to be low, they are unlikely to engage in mitigation activities for the potential hazard. We believe one factor that can lower inappropriately (from a normative perspective) people's perception of the risk of a hazard is information about prior near‐miss events. A near‐miss occurs when an event (such as a hurricane), which had some nontrivial probability of ending in disaster (loss of life, property damage), does not because good fortune intervenes. People appear to mistake such good fortune as an indicator of resiliency. In our first study, people with near‐miss information were less likely to purchase flood insurance, and this was shown for both participants from the general population and individuals with specific interests in risk and natural disasters. In our second study, we consider a different mitigation decision, that is, to evacuate from a hurricane, and vary the level of statistical probability of hurricane damage. We still found a strong effect for near‐miss information. Our research thus shows how people who have experienced a similar situation but escape damage because of chance will make decisions consistent with a perception that the situation is less risky than those without the past experience. We end by discussing the implications for risk communication.     

Systemic Valuation of Strategic Preparedness Through Application of the Inoperability Input-Output Model with Lessons Learned from Hurricane Katrina

The U.S. Department of Homeland Security (DHS) has mandated all regions to "carefully weigh the benefit of each homeland security endeavor and only allocate resources where the benefit of reducing risk is worth the amount of additional cost" (DHS, 2006, p. 64). This mandate illuminates the need to develop methods for systemic valuation of preparedness measures that support strategic decision making. This article proposes an analysis method that naturally emerges from the structure of the inoperability input-output model (IIM) through which various regional- and sector-specific impact analyses can be cost-effectively integrated for natural and man-made disasters. The IIM is described extensively in a companion paper (Lian et al., 2007). Its reliance on data classifications structured by the U.S. Census Bureau and its extensive accounting of economic interdependencies enables us to decompose a risk analysis activity, perform independent assessments, and properly integrate the assessment for a systemic valuation of risk and risk management activity. In this article, we account for and assess some of the major impacts of Hurricanes Katrina and Rita to demonstrate this use of the IIM and illustrate hypothetical, reduced impacts resulting from various strategic preparedness decisions. Our results indicate the capability of the IIM to guide the decision-making processes involved in developing a preparedness strategy.     

Hurricane Isaac: A Longitudinal Analysis of Storm Characteristics and Power Outage Risk

In August 2012, Hurricane Isaac, a Category 1 hurricane at landfall, caused extensive power outages in Louisiana. The storm brought high winds, storm surge, and flooding to Louisiana, and power outages were widespread and prolonged. Hourly power outage data for the state of Louisiana were collected during the storm and analyzed. This analysis included correlation of hourly power outage figures by zip code with storm conditions including wind, rainfall, and storm surge using a nonparametric ensemble data mining approach. Results were analyzed to understand how correlation of power outages with storm conditions differed geographically within the state. This analysis provided insight on how rainfall and storm surge, along with wind, contribute to power outages in hurricanes. By conducting a longitudinal study of outages at the zip code level, we were able to gain insight into the causal drivers of power outages during hurricanes. Our analysis showed that the statistical importance of storm characteristic covariates to power outages varies geographically. For Hurricane Isaac, wind speed, precipitation, and previous outages generally had high importance, whereas storm surge had lower importance, even in zip codes that experienced significant surge. The results of this analysis can inform the development of power outage forecasting models, which often focus strictly on wind‐related covariates. Our study of Hurricane Isaac indicates that inclusion of other covariates, particularly precipitation, may improve model accuracy and robustness across a range of storm conditions and geography.     

Simulation and extremal analysis of hurricane events

In regions affected by tropical storms the damage caused by hurricane winds can be catastrophic. Consequently, accurate estimates of hurricane activity in such regions are vital. Unfortunately, the severity of events means that wind speed data are scarce and unreliable, even by standards which are usual for extreme value analysis. In contrast, records of atmospheric pressures are more complete. This suggests a two-stage approach: the development of a model describing spatiotemporal patterns of wind field behaviour for hurricane events; then the simulation of such events, using meteorological climate models, to obtain a realization of associated wind speeds whose extremal characteristics are summarized. This is not a new idea, but we apply careful statistical modelling for each aspect of the model development and simulation, taking the Gulf and Atlantic coastlines of the USA as our study area. Moreover, we address for the first time the issue of spatial dependence in extremes of hurricane events, which we find to have substantial implications for regional risk assessments.     

The Use of Meteorological Data in the Prediction of Tropical Cyclones

Recent studies have shown that the ability to forecast tropical cyclone motion and intensity has not improved significantly over the past decade, in spite of apparent advances in the statistical and dynamical modeling of the motion- and intensity-change processes. This static situation has focused attention on the meteorological data and analysis techniques that provide input to these models. The quality and quantity of data have apparently not kept pace with the modeling efforts; in fact, increased sophistication in modeling alone may be counterproductive. Steps are being taken in the meteorological community to correct this problem. This study examines the entire data problem, discusses the remedial measures being taken, and points out some of the pitfalls in the statistical use of meteorological data.     

A content analysis of the newspaper coverage of the three major hurricanes in 2005

As the primary means of communication between government agencies and the general population, the media play an important role in shaping how the public perceives a potential risk. Risk communicators should carefully consider media coverage of disaster to successfully communicate with the public in the future. This paper examines the content of Hurricane news stories and the framing patterns (e.g., emotional vs. logical) used by local newspapers 1 week before the occurrences of three major hurricanes in 2005. “Actions to take to prevent the risk” was the most frequently covered content, followed by “anticipated damages.” The majority of the risk content was presented using logical framing patterns.