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David Stephenson

Professor David Stephenson

Professor David Stephenson

Research Fellow

Interests and expertise (Subject groups)

Grants awarded

Stochastic modelling for improving understanding of storm risk

Scheme: Wolfson Research Merit Awards

Organisation: University of Exeter

Dates: May 2015-Apr 2020

Value: £42,500

Summary: Cyclones are fascinating large storms that can have devastating effects on society. For example, Hurricane Sandy (25/10/2012), caused major flooding in New York resulting in $25-30 billion in insured losses, and 286 deaths in 7 countries. More recently in winter 2013/14, more than 10 severe storms passed over Europe, destroying infrastructure and disrupting transport and business leading to the loss of more than 15 lives and total insured losses of more than $3.3 billion. Losses due to such events are expected to rise dramatically due to climate-change related trends in weather extremes, increasing exposure in developing countries, and increasing world population. A major theme in my research has been to model storms as random processes. As well as providing new insight into storm behaviour, this can help insurers and other stakeholders develop a more reliable view of storm risk, which then enables them to develop better resilience and protection for society. I am particularly interested in collective risk, in other words, the chance of having a cluster of many extreme storms in a season. Multiple events in a short space of time put undue stress on society, which needs to develop more resilient mechanisms for coping with such possibilities (e.g. capital transfer to cover large claims). My research has used statistical concepts such as the Poisson distribution to quantify how much clustering there has been in storms. Interestingly, I found that there is a lot of clustering especially for more extreme windstorms, which has enabled insurers to revise their estimates of storm risk using improved catastrophe models. I like to work in this area because I am passionate about using mathematical concepts such as probability to explain and predict features in the real world. I continue to be amazed by how abstract mathematical equations can be so effective at mimicking the processes that produce real world data. I find it very rewarding to think that my work helps to make science more informative for decision-making, thereby providing better protection and resilience for people confronted with natural hazards. I enjoy collaborating with people in other disciplines such as meteorologists, statisticians, catastrophe modellers, and actuaries each having their own distinct cultures. My longer-term aim is to develop statistical frameworks that will help integrate data from meteorological observations and climate model simulations taking into account all of the sources of uncertainty. By analogy, if one thinks of climate models as maps of the climate system, my goal is to develop more informative map legends to help people better understand and relate these maps to the true climate. This will allow society to fully exploit climate model simulations to quantify the risk due to extreme storms in the forthcoming year, as well as better adapting for future extremes that are expected to intensify due to global warming.

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