Daniel Williamson

Daniel Williamson
University of Exeter | UoE · College of Engineering, Mathematics and Physical Sciences

PhD

About

54
Publications
4,888
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
962
Citations

Publications

Publications (54)
Article
Efforts to promote travel behaviour change have frequently deployed social marketing strategies that are based on characterising populations into discrete target groups through quantitative segmentation techniques. Such techniques provide an important basis for understanding behavioural choices and motivations, frequently using psychological constr...
Article
Full-text available
We outline the principles of the natural capital approach to decision making and apply these to the contemporary challenge of very significantly expanding woodlands as contribution to attaining net zero emissions of greenhouse gases. Drawing on the case of the UK, we argue that a single focus upon carbon storage alone is likely to overlook the othe...
Article
Full-text available
Land surface models are typically integrated into global climate projections, but as their spatial resolution increases the prospect of using them to aid in local policy decisions becomes more appealing. If these complex models are to be used to make local decisions, then a full quantification of uncertainty is necessary, but the computational cost...
Preprint
Full-text available
Any experiment with climate models relies on a potentially large set of spatio-temporal boundary conditions. These can represent both the initial state of the system and/or forcings driving the model output throughout the experiment. Whilst these boundary conditions are typically fixed using available reconstructions in climate modelling studies, t...
Article
Shared mobility spaces have become increasingly popular internationally as attempts to increase the uptake of active travel modes (walking, cycling and running) have turned pavements, shopping streets and public spaces into multi-mode mobility spaces. From a sustainability perspective, policy makers in the UK have argued that shared spaces afford g...
Preprint
Full-text available
Land surface models are typically integrated into global climate projections, but as their spatial resolution increases the prospect of using them to aid in local policy decisions becomes more appealing. If these complex models are to be used to make local decisions, then a full quantification of uncertainty is necessary, but the computational cost...
Preprint
We propose a novel deep Gaussian process (DGP) inference method for computer model emulation using stochastic imputation. By stochastically imputing the latent layers, the approach transforms the DGP into the linked GP, a state-of-the-art surrogate model formed by linking a system of feed-forward coupled GPs. This transformation renders a simple wh...
Preprint
Full-text available
The rapid emergence of SARS-CoV-2 mutants with new phenotypic properties is a critical challenge to the control of the ongoing pandemic. B.1.1.7 was monitored in the UK through routine testing and S-gene target failures (SGTF), comprising over 90% of cases by March 2021. Now, the reverse is occurring: SGTF cases are being replaced by an S-gene posi...
Article
Full-text available
We demonstrate a new approach for climate model tuning in a realistic situation. Our approach, the mathematical foundations and technical details of which are given in Part I, systematically uses a single‐column configuration of a global atmospheric model on test cases for which reference large‐eddy‐simulations are available. The space of free para...
Article
The smart cities agenda has garnered considerable interest recently as the spread of mobile technologies and notions of ‘big data’ have opened possibilities for promoting greater efficiencies in urban metabolisms. This has been particularly prominent in the realm of environmental sustainability, where smart technologies have been viewed as a way of...
Article
Full-text available
Process‐scale development, evaluation, and calibration of physically based parameterizations of clouds and radiation are powerful levers for improving weather and climate models. In a series of papers, we propose a strategy for process‐based calibration of climate models that uses machine learning techniques. It relies on systematic comparisons of...
Article
Full-text available
Abstract The development of parameterizations is a major task in the development of weather and climate models. Model improvement has been slow in the past decades, due to the difficulty of encompassing key physical processes into parameterizations, but also of calibrating or “tuning” the many free parameters involved in their formulation. Machine...
Article
Full-text available
Abstract The representation of stable boundary layers (SBLs) still challenges turbulence parameterizations implemented in current weather or climate models. The present work assesses whether these model deficiencies reflect calibration choices or intrinsic limits in currently‐used turbulence parameterization formulations and implementations. This q...
Preprint
In many real-world applications, we are interested in approximating black-box, costly functions as accurately as possible with the smallest number of function evaluations. A complex computer code is an example of such a function. In this work, a Gaussian process (GP) emulator is used to approximate the output of complex computer code. We consider t...
Preprint
Classification is a vital tool that is important for modelling many complex numerical models. A model or system may be such that, for certain areas of input space, the output either does not exist, or is not in a quantifiable form. Here, we present a new method for classification where the model outputs are given distinct classifying labels, which...
Article
Full-text available
The city of Exeter, UK, is experiencing unprecedented growth, putting pressure on traffic infrastructure. As well as traffic network management, understanding and influencing commuter behaviour is important for reducing congestion. Information about current commuter behaviour has been gathered through a large online survey, and similar individuals...
Article
The city of Exeter, UK, is experiencing unprecedented growth, putting pressure on traffic infrastructure. As well as traffic network management, understanding and influencing commuter behaviour is important for reducing congestion. Information about current commuter behaviour has been gathered through a large on‐line survey, and similar individuals...
Article
The use of emergent constraints to quantify uncertainty for policy-relevant quantities such as equilibrium climate sensitivity (ECS) has become increasingly widespread in recent years. Many researchers, however, claim that emergent constraints are inappropriate or even underreport uncertainty. In this paper we contribute to this discussion by exami...
Preprint
Full-text available
Exposure to air pollution in the form of fine particulate matter (PM2.5) is known to cause diseases and cancers. Consequently, the public are increasingly seeking health warnings associated with levels of PM2:5 using mobile phone applications and websites. Often, these existing platforms provide one-size-fits-all guidance, not incorporating user sp...
Preprint
Calibration of expensive computer models with high-dimensional output fields can be approached via history matching. If the entire output field is matched, with patterns or correlations between locations or time points represented, calculating the distance metric between observational data and model output for a single input setting requires a time...
Preprint
The use of emergent constraints to quantify uncertainty for key policy relevant quantities such as Equilibrium Climate Sensitivity (ECS) has become increasingly widespread in recent years. Many researchers, however, claim that emergent constraints are inappropriate or even under-report uncertainty. In this paper we contribute to this discussion by...
Article
Full-text available
We develop Bayesian state space methods for modelling changes to the mean level or temporal correlation structure of an observed time series due to intermittent coupling with an unobserved process. Novel intervention methods are proposed to model the effect of repeated coupling as a single dynamic process. Latent time varying auto‐regressive compon...
Preprint
In many real-world applications, we are interested in approximating functions that are analytically unknown. An emulator provides a "fast" approximation of such functions relying on a limited number of evaluations. Gaussian processes (GPs) are commonplace emulators due to their properties such as the ability to quantify uncertainty. GPs are essenti...
Preprint
We present a new method of modelling numerical systems where there are two distinct output solution classes, for example tipping points or bifurcations. Gaussian process emulation is a useful tool in understanding these complex systems and provides estimates of uncertainty, but we aim to include systems where there are discontinuities between the t...
Article
Full-text available
The calibration of complex computer codes using uncertainty quantification (UQ) methods is a rich area of statistical methodological development. When applying these techniques to simulators with spatial output, it is now standard to use principal component decomposition to reduce the dimensions of the outputs in order to allow Gaussian process emu...
Preprint
Ice sheet models are used to study the deglaciation of North America at the end of the last ice age (past 21,000 years), so that we might understand whether and how existing ice sheets may reduce or disappear under climate change. Though ice sheet models have a few parameters controlling physical behaviour of the ice mass, they also require boundar...
Preprint
Existing methods for diagnosing predictability in climate indices often make a number of unjustified assumptions about the climate system that can lead to misleading conclusions. We present a flexible family of state-space models capable of separating the effects of external forcing on inter-annual time scales, from long-term trends and decadal var...
Article
Full-text available
Current approaches for understanding and influencing transport behaviour often involve creating fixed, homogenous groups of similar surveyed individuals in order to explore specific behavioural profiles, an approach known as segmentation. Most commonly, segmentation is not based on a formal statistical model, but either a simple ‘a priori’ defined...
Article
Full-text available
Weakly stationary Gaussian processes are the principal tool in the statistical approaches to the design and analysis of computer experiments (or Uncertainty Quantification). Such processes are fitted to computer model output using a set of training runs to learn the parameters of the process covariance kernel. The stationarity assumption is often a...
Article
The calibration of complex computer codes using uncertainty quantification (UQ) methods is a rich area of statistical methodological development. When applying these techniques to simulators with spatio-temporal output, it is now standard to use principal component decomposition to reduce the dimensions of the outputs in order to allow Gaussian pro...
Article
Many time series exhibit non-stationary behaviour that can be explained by intermittent coupling between the observed system and one or more unobserved drivers. We develop Bayesian state space methods for modelling changes to the mean level or temporal correlation structure due to intermittent coupling. Improved system diagnostics and prediction ar...
Article
Full-text available
In this paper we discuss climate model tuning and present an iterative automatic tuning method from the statistical science literature. The method, which we refer to here as iterative refocussing (though also known as history matching), avoids many of the common pitfalls of automatic tuning procedures that are based on optimisation of a cost functi...
Article
Full-text available
Expensive computer codes, particularly those used for simulating environmental or geological processes, such as climate models, require calibration (sometimes called tuning). When calibrating expensive simulators using uncertainty quantification methods, it is usually necessary to use a statistical model called an emulator in place of the computer...
Article
Full-text available
In this paper we discuss climate model tuning and present an iterative automatic tuning method from the statistical science literature. The method, which we refer to here as iterative refocussing (though also known as history matching), avoids many of the common pitfalls of automatic tuning procedures that are based on optimisation of a cost functi...
Article
The process of parameter estimation targeting a chosen set of observations is an essential aspect of numerical modeling. This process is usually named tuning in the climate modeling community. In climate models, the variety and complexity of physical processes involved, and their interplay through a wide range of spatial and temporal scales, must b...
Article
In this paper, we are concerned with attributing meaning to the results of a Bayesian analysis for a problem which is sufficiently complex that we are unable to assert a precise correspondence between the expert probabilistic judgements of the analyst and the particular forms chosen for the prior specification and the likelihood for the analysis. I...
Article
Full-text available
In this paper we present a novel, flexible, and multi-purpose class of designs for initial exploration of the parameter spaces of computer models, such as those used to study many features of the environment. The idea applies existing technology aimed at expanding a Latin Hypercube (LHC) in order to generate initial LHC designs that are composed of...
Article
We describe the method of history matching, a method currently used to help quantify parametric uncertainty in climate models, and argue for its use in identifying and removing structural biases in climate models at the model development stage. We illustrate the method using an investigation of the potential to improve upon known ocean circulation...
Article
We develop Bayesian dynamic linear model Gaussian processes for emulation of time series output for computer models that may exhibit chaotic behavior, but where this behavior retains some underlying structure. The statistical technology is particularly suited to emulating the time series output of large climate models that exhibit this feature and...
Article
We apply an established statistical methodology called history matching to constrain the parameter space of a coupled non-flux-adjusted climate model (the third Hadley Centre Climate Model; HadCM3) by using a 10,000-member perturbed physics ensemble and observational metrics. History matching uses emulators (fast statistical representations of clim...
Article
Full-text available
In this paper we tackle the problem of generating uniform designs in very small subregions of computer model input space that have been identified in previous experiments as worthy of further study. The method is capable of producing uniform designs in subregions of computer model input space defined by a membership function that consists of a cont...
Article
A number of studies have set out to obtain a range of atmosphere and ocean model behavior by perturbing parameters in a single climate model (perturbed physics ensemble: PPE). Early studies used shallow layer slab ocean or flux-adjusted coupled ocean-atmosphere models to obtain a broad range of behavior as characterized by climate sensitivity. A re...
Article
We present some results from a very large ensemble of the UK Met Office coupled climate model HadCM3. The ensemble is being run through climateprediction.net (CPDN), and there is now around 5 million years of model output freely available to analyse. Plots of the time evolution of the Atlantic meridional overturning circulation (AMOC) from a 10,000...
Article
When using computer models to provide policy support it is normal to encounter ensembles that test only a handful of feasible or idealized decision scenarios. We present a new methodology for performing multilevel emulation of a complex model as a function of any decision within a predefined class that makes specific use of a scenario ensemble of o...
Article
In this paper, we discuss combining expert knowledge and computer simulators in order to provide decision support for policy makers managing complex physical systems. We allow future states of the complex system to be viewed after initial policy is made, and for those states to influence revision of policy. The potential for future observations and...
Article
Efforts have been made in past research to attain a wide range of atmosphere and ocean model behaviors by perturbing the model physics of Global Climate Models. However, obtaining a large spread of behaviors of the ocean model has so far been unsuccessful. In an ongoing project within RAPID-WATCH, physical parameters of HadCM3 have been perturbed w...