The statistical design of assessments of impacts from explosive volcanic eruptions

Lead Research Organisation: University of Bristol
Department Name: Mathematics


Explosive volcanic eruptions generate far-reaching impacts, in particular the dispersion of volcanic ash in the atmosphere and its deposition on the ground which can have significant effects on agriculture, infrastructure, and human health. The dispersion of ash in the atmosphere is dependent on the magnitude of the eruption, and is driven by wind fields which are highly variable and difficult to forecast. Prediction of atmospheric ash dispersion is further complicated by the rarity of such eruption events, as well as the lack of direct observations of ash concentration at sufficiently high spatial and temporal resolution during such eruptions.
This project will focus on the statistical design of operational assessments of volcanic ash dispersion, including:
1. Statistically-coherent design of probabilistic volcanic ash hazard assessments. Current practice in producing a probabilistic ash hazard assessment uses an ensemble of simulations with sizes set by time or computing constraints, with the result that the variance of individual impact thresholds vary within and between assessments. This component of the research will use fundamental statistical approaches to standardise the design of probabilistic ash hazard assessments, and explore (i) variance reduction through stratification via the use of weather patterns as forcing data for ensembles, and (ii) appropriate statistical methods for larger (and consequently rarer) eruptions;
2. Bayesian statistical methods to characterise uncertainty in estimation of the intensity of explosive eruptions and ensemble forecast design. Current practice in operational response to volcanic eruptions and in eruption forecast design uses observations of the eruption plume height to estimate the mass eruption rate through a semi-empirical relationship. This component of the research will use applied Bayesian regression methods to characterise the uncertainty in the height-eruption rate semi-empirical relationship, and thus in the design of ensemble forecasts and operational ash dispersion modelling.

Planned Impact

The COMPASS Centre for Doctoral Training will have the following impact.

Doctoral Students Impact.

I1. Recruit and train over 55 students and provide them with a broad and comprehensive education in contemporary Computational Statistics & Data Science, leading to the award of a PhD. The training environment will be built around a set of multilevel cohorts: a variety of group sizes, within and across year cohort activities, within and across disciplinary boundaries with internal and external partners, where statistics and computation are the common focus, but remaining sensitive to disciplinary needs. Our novel doctoral training environment will powerfully impact on students, opening their eyes to not only a range of modern technical benefits and opportunities, but on the power of team-working with people from a range of backgrounds to solve the most important problems of the day. They will learn to apply their skills to achieve impact by collaborative working with internal and external partners, such as via our Rapid Response Teams, Policy Workshops & Statistical Clinics.

I2. As well as advanced training in computational statistics and data science, our students will be impacted by exposure to, and training in, important cognate topics such as ethics, responsible innovation, equality, diversity and inclusion, policy, effective communication and dissemination, enterprise, impact and consultancy skills. It is vital for our students to understand that their training will enable them to have a powerful impact on the wider world, so, e.g., AI algorithms they develop should not be discriminatory, and statistical methodologies should be reproducible, and statistical results accurately and comprehensibly communicated to the general public and policymakers.

I3. The students will gain experience via collaborations with academic partners within the University in cognate disciplines, and a wide range of external industrial & government partners. The students will be impacted by the structured training programmes of the UK Academy of Postgraduate Training in Statistics, the Bristol Doctoral College, the Jean Golding Institute, the Alan Turing Institute and the Heilbronn Institute for Mathematical Sciences, which will be integrated into our programme.

I4. Having received an excellent training, the students will then impact powerfully on the world in their future fruitful careers, spreading excellence.

Impact on our Partners & ourselves.

I5. Direct impacts will be achieved by students engaging with, and working on projects with, our academic partners, with discipline-specific problems arising in engineering, education, medicine, economics, earth sciences, life sciences and geographical sciences, and our external partners Adarga, the Atomic Weapons Establishment, CheckRisk, EDF, GCHQ, GSK, the Office for National Statistics, Sciex, Shell UK, Trainline and the UK Space Agency. The students will demonstrate a wide range of innovation with these partners, will attract engagement from new partners, and often provide attractive future employment matches for students and partners alike.

Wider Societal Impact

I6. COMPASS will greatly benefit the UK by providing over 55 highly trained PhD graduates in an area that is known to be suffering from extreme, well-known, shortages in the people pipeline nationally. COMPASS CDT graduates will be equipped for jobs in sectors of high economic value and national priority, including data science, analytics, pharmaceuticals, security, energy, communications, government, and indeed all research labs that deal with data. Through their training, they will enable these organisations to make well-informed and statistically principled decisions that will allow them to maximise their international competitiveness and contribution to societal well-being. COMPASS will also impact positively on the wider student community, both now and sustainably into the future.


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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/S023569/1 01/04/2019 30/09/2027
2437902 Studentship EP/S023569/1 01/10/2020 20/05/2025 Shannon Williams