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By reviewing recent literature, it is noticeable that considerable attention has been given to the relationship between all Atlantic hurricanes and those that make landfall in the USA. However, less research has been done regarding landfall frequency and identifying spatial areas that are statistically more likely to produce landfalling hurricanes. The purpose of this paper is to provide a better prediction method for US landfalling hurricanes.

This work is based on the hypothesis that landfall variations along the US coast can be better explained in terms of hurricane origination points over more susceptible areas on the North Atlantic Ocean. Simulation techniques are used to spatially quantify the landfall probability.

Results indicate the existence of a landfall corridor in the Atlantic Basin, which explains some of the variances observed in the landfall process. Two different hypotheses of climate are examined. A long‐term assumption is based on the historical data from 1940 to 2010. The second assumption is based on the Atlantic Multidecadal Oscillation. Since 1995, we are in a warm phase and we assume that sea surface temperatures remain warmer than the long‐term average over the next several years. Results indicate that the average increase on landfall frequency is about 13 per cent.

This paper is the first paper that introduces the concept of landfall origination corridor. It spatially identifies the differences between long term and warm phase of the atmosphere in terms of US landfall occurrence using hurricane origination points.

A reliable forecast of hurricane activity in the Atlantic Basin has the potential to help mitigate the economic losses caused by hurricanes. One of the difficult problems is to make reasonable annual forecast of catastrophe losses based on the short record of historical observations. Atmospheric conditions tend to influence tropical cyclone development. Considering the complex interactions among climatological factors, prediction of future hurricane activity is challenging. In this study, the authors are attempting to predict the number of Atlantic hurricanes for a given year based on two different approaches.

In part I, an autoregressive integrated moving average (ARIMA) is used to model a long‐run behavior of Atlantic hurricane frequency. The authors present a comparison of CSU's forecast with ARIMA model. Part II focuses on the relationship between the climate signals and hurricane activity and introduces a new approach in including climate indices into the prediction model. In this part, principal components analysis (PCA) is used to identify possible patterns in historical data based on six climate indices measured prior to hurricane season. The objective is to reduce the data set to a smaller set while most of the variability observed in the real data is captured. The variances observed in an orthogonal system indicate the order of contribution of each mode shape.

Results from part I suggest that CSU's forecast model, in general, is superior to results obtained by ARIMA. In part II, the correlation between mode (shapes) and the number of Atlantic hurricanes per year is examined. The resulting relationships show that, for the time interval of 1990 through 2011, PCA‐based approach provides better estimates compared to CSU's forecast.

The paper presents a unique prediction approach which is simple, relatively accurate and easy to apply. The results of this study show that complex statistical analyses/models do not necessarily provide better forecasts.

Atlantic Basin Refining, Inc. (ABR), a Virgin Islands company located on the island of St Croix, reached a tentative agreement with Hess and Petroleos de Venezuela SA to purchase the two companies’ joint venture, Hovensa, LLC in November 2014. Hovensa operated the large St Croix oil refinery that had been closed since 2012, but the deal required approval by the Virgin Islands Senate. Although reopening the large refinery would generate a significant boost to the local economy, past operating losses, and financial and legal issues associated with Hovensa, raised concerns about the feasibility of ABR’s proposal. The case is set in late 2014 as the government is working to ensure that the decision to allow ABR to purchase the refinery reflects the long-term interests of the Virgin Islands.

The case was researched using secondary data and all materials are available to the public. This was necessary due to the ongoing legal battle concerning the refinery’s sale. No disguises of people or entities were used. Frequently cited sources include government and court records, newspaper articles, and internet sources.

The case is most appropriate for undergraduate courses in management or finance where capital budgeting decisions are analyzed.

The case draws on literature related to capital budgeting and management.

In general, the insurance industry accepts large risks due to the frequency and severity of extreme events. Because of the short record on hazard data for such events, a large amount of uncertainty has to be dealt with. Given this large uncertainty it is important to better quantify the hazard parameters that are defined as inputs to the catastrophe models. The purpose of this paper is to evaluate the hurricane risk from loss point of view in the USA for both long‐term and warm phase conditions using a simulation‐based stochastic model.

A Poisson process is used to simulate the occurrence of events for both conditions. The generated event‐sets were used along with vulnerability and cost models to estimate the loss to an insurance industry portfolio. The paper discusses the statistics of events categorized by the Saffir‐Simpson Hurricane Wind Scale, annualized and return period losses and compares the results for both assumed long‐term and warm phase climate states.

The analysis shows that the population of landfall data for the two climate conditions is not statistically different. However, if we accept that a difference in the frequency of landfall occurrence between the two assumptions exists, the increase in average annual loss is about 17 per cent.

This paper provides insights to the difference between the two states of atmosphere from the point of view of insured losses for hurricanes and is one of the first papers that offers conclusion on the uncertainty associated with the warm phase data.

The purpose of this paper is to consider the problem of using “black‐box” methods to forecast catastrophe events, and illustrate the value of independent peer review.

The problem with black‐box catastrophe forecasts is the absence of both extensive validation data and impartial peer review. These issues may be addressed by comparing black‐box forecasts with a set of naïve alternative forecasts provided by an independent party. To illustrate this approach, the historical hurricane forecasts of Dr William M. Gray, professor at Colorado State University, are considered and a simple ARIMA analysis is offered as a naïve alternative.

The analysis shows that Dr Gray's complex forecasting methodology does in fact provide reasonable forecasts, and may indeed offer value beyond a naïve alternative model.

The editorial identifies a major problem in catastrophe forecasting, and suggests one way to address this problem.

The purpose of this two‐part series is to consider the role of the “scientific method” (SM) in human understanding, questioning both its consistency in actual practice and its reasonableness as a system of philosophy and action.

Part 2 considers problems of inefficiency and inertia caused by the SM's collectivist, frequentist orientation.

It is argued that problems caused by the SM's frequentist framework may be avoided by a more individualist, Bayesian approach.

The two‐part series challenges certain aspects of the “scientific method” as employed in the practice of modern science.