High Likelihood Of Occurrence Research Articles

On the Significance of Multiple Consecutive Days of Tornado Activity

Abstract Motivated by the temporal behavior of recent high-end tornado events, a 30-yr historical record of tornadoes in the United States is examined for multiple-day periods of tornado activity. Comprising the 3129 tornado days during 1983–2012 are 1406 unique, nonoverlapping periods. Only 24% of these periods have lengths of 3 or more days. However, the conditional probability of such a multiday period given an outbreak day (OB; one with 20 or more tornado reports) is 74%, and given a significant tornado day [SIGTOR; one rated Fujita/enhanced Fujita (F/EF) ≥ 3] is 60%. Alternative ways of expressing these conditional probabilities all lead to the conclusion that SIGTORs and/or OBs are more likely to be contained within multiday periods of tornadoes than within 1–2-day periods. Two additional conclusions are offered: 1) SIGTORs and OBs have a relatively higher likelihood of occurrence during the latter half of the multiday periods, and 2) multiday periods have a relatively higher likelihood of occurrence during the warm months of April–July. A hypothesized connection, illustrated using reanalysis data from 2013, is proposed between such behaviors and the characteristics of the larger-scale meteorological forcing. Some speculations are made about possible relationships between multiday periods of tornado activity and convective feedbacks, extended predictability, and modes of internal climate variability.

Performance prediction using supply chain uncertainty modelling

This paper presents a new quantitative method and a holistic approach to assess the impact of supply chain uncertainty on customer delivery performance. We adopt system theory, Multi Criterion Decision Making (MCDM) theory – Analytical Hierarchy Process (AHP) and Theory of Constraints (TOC) as the underpinning theoretical frameworks for this new method. Some 72 UK companies have verified this method and we call this – the Supply Chain Uncertainty Impact Assessment (SCUIA) method. Some 66 types of supply chain uncertainty are assessed. This method involves three levels of assessment, namely: (1) relative percentage occurrences of each supply chain uncertainty, (2) relative upstream and downstream impact of each supply chain uncertainty and (3) relative likelihood of each underlying causes of supply chain uncertainty. The findings show that underlying causes of supply chain uncertainty with higher likelihood of occurrences do not necessarily result in greater impact on the downstream delivery performance in the supply chain. It can be concluded that decision makers should not use the likelihood of occurrence or impact criteria in isolation when devising solutions to tackle supply chain uncertainty, but to tackle those underlying causes of supply chain uncertainty that have both greater impact and higher likelihood of occurrence.