David Topping is a senior lecturer in the Centre for Atmospheric Science at the University of Manchester. He completed his PhD in 2005 on ’Modelling the hygroscopic properties of atmospheric aerosol particles’ at the University of Manchester Institute of Science and Technology (UMIST) after finishing a degree in Physics at the same institute. Following this he became a fellow of the UK National Centre for Atmospheric Science (NCAS) before taking on the role of a senior research fellow part funded by the School of Earth and Environmental Science (SEES)/Centre for Atmospheric Science (CAS) at the University of Manchester.
Air pollution and climate change are two key socio-environmental drivers that represent some of the biggest multidisciplinary challenges in science, society and the economy today. The need to understand the chemical and physical processes in the atmosphere that dictate the impacts of both has created a wide range of experimental platforms over the past two decades. However, whilst these facilities persistently identify and hypothesise new processes and compounds deemed important to improve our understanding of change, the research community is now struggling to use the data and subsequent information in a truly meaningful way.
David's work while a fellow of the Turing includes evaluating how machine learning might mitigate existing 'complexity' bottlenecks in atmospheric modelling, experimental data analysis and impact assessment. This includes, for example, replacing parameterised or iterative models of key atmospheric processes, traditionally solved using stiff ODE methods, using surrogate models. This also includes extracting new information from existing instrumentation through a wide range of both supervised and unsupervised methods. Alongside this, he will collaborate on methods that combine both air pollution data and human symptomatic responses.