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Applying mathematical science to global problems

We occupy a distinctive position in the mathematical sciences community in terms of variety of applications of core mathematics research to other sciences, such as: 

  • applied and medical statistics
  • biology, medicine, neuroscience and pharmacology
  • data assimilation and modelling of Earth observations
  • dynamics and numerical modelling of atmosphere and ocean
  • polymer science.

The importance of our work on the Mathematics of Weather and Climate was recognised by the award of our EPSRC-funded Centre for Doctoral Training in Mathematics of Planet Earth. This is a joint initiative with Imperial College that provides PhD training in the mathematical and computational techniques needed to understand, predict and quantify risk and uncertainty for extreme weather and climate change.

The research of our Data Assimilation group - the largest in Europe - ties together not only the main themes in meteorology (weather, climate and earth observation) but reaches across into mathematics, statistics and beyond. 

Other groups with an interdisciplinary focus include Statistics, Applied Statistics, Mathematical Biology, Complex Fluids and Theoretical Polymer Physics groups. 
 
Our work with other disciplines is strongly underpinned by fundamental research in core mathematics. This includes research in areas such as algebraic and analytic number theory, applied and computational complex analysis, nonlinear partial differential equations, numerical analysis and computational modelling, operator theory, spectral theory, stochastic analysis and theoretical probability.

Diagram showing direction of flowResearch into oil boringResearch into Twitter use

research highlights

Understanding uncertainties brings about better weather forecasts

Advances in the use of satellite and radar data, led by Professors Sarah Dance and Nancy Nichols, are helping agencies across the world, from the UK Met Office to NASA, to make better weather forecasts. Their research has led to better ways to estimate correlations, or statistical patterns, in observation errors to improve weather predictions and has influenced weather forecasting strategy, diagnostic activities, and training for the next generation of meteorologists. Find out more.

Better clinical trial design speeds drug development

In order for a new treatment or therapy to be used in medical practice, clinical trials must be run during the research and development process, often taking years to complete and costing millions of pounds. Approximately 30,000 new clinical trials worldwide are now registered annually, with approximately 300,000 clinical studies registered globally since 2000. To improve efficiency of the clinical trial process, such that treatments can be identified and ruled out more quickly and cost-effectively, research by Professor Sue Todd has made important statistical contributions to the field of multi-arm multi-stage adaptive designs. Find out more.

Earth’s tipping points analysed

Reading research combining mathematics and climate science has analysed how random events and human action could combine to reach a ‘tipping point’, where the earth’s current climate transitions to a very different one. The research, led by Valerio Lucarini, Professor of Statistical Mechanics, can be applied to the Earth’s climate, landscape features or ecosystems such as rainforests to aid our understanding of how they can be altered or destroyed after reaching a point of no return. The study builds on a 2017 paper published in Nonlinearity, which used a dynamic method to identify the tipping point between two competing states. This work led to new understanding of the global stability properties of the climate. Find out more in our news story.

Better flash flood forecasting

Advance warning of flash floods even a few hours in advance can save lives and reduce damage to property, but better forecasts of severe weather depend on accurate data describing the current state of atmospheric variables like wind, pressure, temperature and humidity. These initial conditions are usually estimated using a sophisticated mathematical technique known as data assimilation that blends observations with model information, taking account of the uncertainties in the data. Project FRANC, led by Professor Sarah Dance from our Data Assimilation Centre is exploring new techniques to extract the maximum amount of information from observations of winds, temperature, precipitation and moist processes. FRANC is one of three Reading-led projects in the major NERC-led Flooding From Intense Rainfall programme to reduce the risk of damage and deaths caused by surface water and flash floods. Learn more.

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