This paper investigates the nature of model error in complex deterministic nonlinear systems such as weather forecasting models. Forecasting systems incorporate two components, a forecast model and a data assimilation method. The latter projects a collection of observations of reality into a...

Ensemble prediction systems aim to account for uncertainties of initial conditions and model error. Ensemble forecasting is sometimes viewed as a method of obtaining (objective) probabilistic forecasts. How is one to judge the quality of an ensemble at forecasting a system? The probability that...

The relationship between short-term exposure to air pollution and mortality or morbidity has been the subject of much recent research, in which the standard method of analysis uses Poisson linear or additive models. In this paper, we use a Bayesian dynamic generalised linear model (DGLM) to...

This thesis aims to develop the foundations for a new validation strategy for route-based road weather forecasts that will enable validation of route-based models at a vastly improved spatial and temporal resolution, and in doing so provide a tool for rapid appraisal of new model...

Quantitative modeling of risk and hazard from flooding involves decisions regarding the choice of model and goal of the modeling exercise, expressed by some measure of performance. This paper shows how the subjectivity in the choices of performance measures and observation sets used for model...

The European Union Water Framework Directive (WFD) represents a new approach to the management of water across Europe. As part of the implementation of the WFD, integrated, catchment-scale plans for the protection and restoration of aquatic ecosystems must be developed. These plans need to be...

A multi-scale framework for decision support is presented that uses a combination of experiments, models, communication, education and decision support tools to arrive at a realistic strategy to minimise diffuse pollution. Effective partnerships between researchers and stakeholders play a key...

The objective of this contribution is to form a clear picture of uncertainties we encounter in flood estimation, including both real-time flood forecasting and simulation for flood risk estimation. In simulation, we prefer the thesis of equifinality to obtain global optima. Many models producing...

The generalized likelihood uncertainty estimation (GLUE) methodology is applied to the problem of predicting the spatially distributed, time-varying probabilities of inundation of all points on a floodplain. Advantage is taken of the relative independence of different effective conveyance...

This paper discusses the modelling of rainfall-flow (rainfall-run-off) and flow-routeing processes in river systems within the context of real-time flood forecasting. It is argued that deterministic, reductionist (or 'bottom-up') models are inappropriate for real-time forecasting because of the...