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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.
The 2010 eruption of Eyjafjallajökull volcano, Iceland caused prolonged closure of European airspace, costing the global airline industry an estimated $200 million per day and disrupting 10 million passengers. We have developed and tested models that predict the dispersal of volcanic ash and developed instrumentation to monitor ash clouds during flight bans and used it to test the models. Our research played a key role in establishing the need for a flight ban and in the adoption of a more flexible approach to its staged lifting as the emergency continued. It also led to increased levels of readiness and to new emergency procedures being put in place across Europe which have minimised the economic costs and human inconvenience without an unacceptable rise in the risks to passengers and crew. The new procedures safely eliminated unnecessary disruption to flights in the latter days of the crisis and during the subsequent eruption of another Icelandic volcano, Grímsvötn in 2011.
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.
Research conducted within the Aon Benfield UCL Hazard Centre has underpinned the development of innovative extreme weather services for the real-time monitoring of global tropical storms and European extreme weather. These services have achieved significant commercial and humanitarian impacts worldwide. Within the REF impact period these impacts included £1.319 million of income generated by sales of commercial products; 24,000 subscribers receiving free storm alerts and/or seasonal forecasts; seasonal forecasts distributed to reinsurance companies worldwide; and a contribution to lives saved in Bangladesh from tropical storm Mahasen (2013). Twenty-two international organisations have also benefited from the commercial extreme weather services; for example, they support the claims division at RSA in assessing risk, allocating resources and detecting fraudulent weather claims; and they enable the Norwegian Hull Club to alert its portfolio of over 9,200 vessels worldwide to steer clear of approaching dangerous storms.
Over one quarter of the estimated 886 million undernourished people in the world live in sub-Saharan Africa and their lives and livelihoods depend critically on rain-fed agriculture. However this region has lacked the equipment and the infrastructure to monitor rainfall. Over the past 20 years, the Unit's TAMSAT (Tropical Applications of Meteorology using SATellite Data and Ground-Based Observations) research group has developed a reliable and robust means for monitoring rainfall, appropriate for use in Africa. In addition, the Unit pioneered the use of such data to predict crop yields over large areas. TAMSAT data and methods are now used in food security (to anticipate drought and predict crop and livestock yields); in health planning (to predict outbreaks of rain-promoted diseases such as malaria); in aid (to guide the allocation and distribution of relief food and water); and in economic planning (to plan mitigation activities and investment in infrastructure). The Unit's programme of development and validation has extended the method to all of Africa, at all times of year. Our work with national meteorological services in Africa has helped them to build their own capabilities and to both contribute to TAMSAT and exploit it. The data provided by TAMSAT has had major impact in increasing the resilience of African populations to weather and climate, saving and improving the quality of lives, and strengthening economies in developing nations.
Researchers at the University of Reading have developed and implemented ground and satellite-based techniques that improve the monitoring of impending volcanic eruptions and their aftermath. Our systems have been mainly used in collaboration with the Montserrat Volcano Observatory (MVO) and the local government civil protection committee on Montserrat. In July 2008 the early rescinding of a precautionary evacuation was made possible by these techniques, thereby minimising disruption and lost economic revenue. The deployment of a permanent, operational ground-based instrument on Montserrat provides a capability that will reassure inhabitants and the island's commercial sector of future timely warnings, thereby enhancing their quality of life and allowing companies to return to the island.
Research carried out at the University of Leeds has led to the development of a system for predicting severe air turbulence at airports and elsewhere. The research modelled highly localised `rotor streaming' turbulence which is too small-scale to predict using today's numerical weather prediction models. The Met Office now uses the highly efficient 3DVOM computer prediction model, based on the Leeds research, to improve its operational weather forecasting, especially for providing warnings of `gustiness' to the public and airports and to highlight risks of overturning of high-sided vehicles. In addition, the model is used by forecasters to predict dangerous turbulence at Mount Pleasant Airport in the Falkland Islands, and has led to the prevention of around five flight diversions per year at an estimated cost saving of £1.25 million.
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.
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.
Data assimilation is playing an ever increasing role in weather forecasting. Implementing four- dimensional variational data assimilation (4DVAR) is part of the long term strategy of the UK Met Office.
In this case study, an idealised 4DVAR scheme, developed by a team from the Universities of Surrey and Reading working with the UK Met Office, based on the integration of Hamiltonian dynamics and nonlinearity into data assimilation, has now been taken up by the Met Office. It is being used to evaluate options for improving operational 4DVAR. The simplicity of the scheme developed by this team has facilitated careful analyses of some generic problems with the operational model. The outcome includes direct impact on the environment and indirect impact on the economy, both through improvements in weather forecasting.