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Statistical modelling of storms by Professor David Stephenson and co-workers in the mathematics institute at the U. of Exeter, has improved the understanding and thereby the pricing of insurance risk due to European windstorms and tropical cyclones. Temporal clustering in these catastrophic natural hazards has been quantified using novel process-based statistical models, which have then been implemented by industry to improve insurance pricing, e.g. on the integrated financial platform used by Willis actuaries to provide a more reliable view of risk as required by EU solvency 2 regulation. This research has also raised awareness in the industry about storm clustering, and has stimulated significant improvements in the main vendor catastrophe models, which are the main tools used by insurance companies to price European windstorm insurance.
Starting in 2001, researchers from the Unit undertook a retrospective analysis of data from the Great Storm of October 1987 which led to them identifying and understanding a region of extremely strong winds within some storms. They termed these winds a "sting jet". In collaboration with the Met Office, the researchers developed ways to identify sting jets in current and imminent weather and, later, methods to forecast these extremely damaging events up to a several days in advance. These techniques are now used in the UK National Severe Weather Warning Service (NSWWS) and in European storm forecasts. Since the development of this new early warning capability, events have been too few to compile proper statistics; however, there is general agreement amongst the emergency services, local government officials and insurers that the improved warnings of extreme winds have saved lives, minimised disruption and generated considerable cost savings.
Research within the Unit was used to create the "TRACK" storm-tracking and analysis software package, which is used to automatically identify storms from both observed and simulated weather data. The software has been used in academic research to improve understanding of how storms develop and how they may change over time, but TRACK has also found widespread applications outside academia. It has been used to quantify errors in current operational weather forecasts, enabling users to produce more accurate storm forecasts better tailored to their needs. It has been used to develop catalogues of historical storms used in the insurance industry for risk assessment. TRACK has also been used to evaluate the performance of climate models and inform their development and improvement.
Research by Professor Leonard Smith and the LSE Centre for the Analysis of Time Series (CATS) on forecasting in non-linear and often chaotic systems, with particular attention to weather, has led to advances in three areas: 1) national and international weather industry products and services that are built upon state-of-the-art research and knowledge, 2) dissemination of state-of-the-art practice in forecast production and verification to national, regional and local weather centres around the world, and 3) the introduction of, and new applications in, state-of-the-art forecasting methods in industries facing high uncertainty and risk, e.g. insurance and energy.
Route-based weather forecasting is being increasingly adopted by local authorities and other organisations to help achieve more efficient and effective operational delivery of their winter resilience measures. This approach takes advantage of GPS and GIS technologies to provide weather forecasting information for particular routes, rather than relying on forecasts for a wider area. Transport agencies have adopted products based on this approach over the last five years to improve their decision making and achieve cost savings. These benefits are passed on to the public who receive a more efficient service without compromising their safety. Research by Chapman and Thornes identified the original concept used in these products, leading to a patent and spin-out company. Subsequently, the ideas were taken up by major companies in the weather forecasting industry who have marketed a series of products based on this innovative approach.
Satellite measurements of sea surface temperature (SST) make a much greater impact on weather forecasting and climate change detection since University of Southampton (UoS) research revolutionised the way SST data are processed. Multiple satellite observations can now be combined into the more complete and detailed SST maps needed by fine resolution meteorological models and used for marine industry operations. Pioneering methodology using a new shipborne radiometer tests the quality of SST maps more rigorously than was previously possible. It provides the first traceable validation of data from the UK's AATSR sensor, confirming their fundamental reliability for observing climate change.