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Introduction
Additional affiliations
May 2017 - present
April 2014 - April 2017
July 2009 - March 2014
Publications
Publications (20)
In power systems modelling, optimization methods based on certain objective function(s) are widely used to provide solutions for decision makers. For complex high-dimensional problems, such as network hosting capacity evaluation of intermittent renewables, simplifications are often used which can lead to the ‘optimal’ solution being suboptimal or n...
The evaluation of probabilistic forecasts plays a central role both in the interpretation and in the use of forecast systems and their development. Probabilistic scores (scoring rules) provide statistical measures to assess the quality of probabilistic forecasts. Often, many probabilistic forecast systems are available while evaluations of their pe...
The evaluation of probabilistic forecasts plays a central role both in the interpretation and in the use of forecast systems and their development. Probabilistic scores (scoring rules) provide statistical measures to assess the quality of probabilistic forecasts. Often, many probabilistic forecast systems are available while evaluations of their pe...
The evaluation of probabilistic forecasts plays a central role both in the interpretation and in the use of forecast systems and their development. Probabilistic scores (scoring rules) provide statistical measures to assess the quality of probabilistic forecasts. Often, many probabilistic forecast systems are available while evaluations of their pe...
Probabilistic forecasting is common in a wide variety of fields including geoscience, social science and finance. It is sometimes the case that one has multiple probability-forecasts for the same target. How is the information in these multiple nonlinear forecast systems best “combined”? Assuming stationarity, in the limit of a very large forecast-...
Energy and environmental data is collected from 5 tower blocks each containing 90 apartments to create two representative calibrated energy models. Three towers (heated by individual natural gas boilers) characterise medium (137.3 kWh/m2/yr.) and two (heated by electrical night storage heaters) characterise low (75.4 kWh/m2/yr.) thermal demands whe...
Probability forecasting is common in the geosciences, the finance sector, and elsewhere. It is sometimes the case that one has multiple probability-forecasts for the same target. How is the information in these multiple forecast systems best "combined"? Assuming stationary, then in the limit of a very large forecast-outcome archive, each model-base...
Predictive skill of complex models is often not uniform in model-state space;
in weather forecasting models, for example, the skill of the model can be
greater in populated regions of interest than in "remote" regions of the globe.
Given a collection of models, a multi-model forecast system using the cross
pollination in time approach can be genera...
The evaluation of forecast performance plays a central role both in the interpretation and use of forecast systems and in their development. Different evaluation measures (scores) are available, often quantifying different characteristics of forecast performance. The properties of several proper scores for probabilistic forecast evaluation are cont...
Berliner (Likelihood and Bayesian prediction for chaotic systems, J. Am.
Stat. Assoc. 1991) identified a number of difficulties in using the likelihood
function within the Bayesian paradigm for state estimation and parameter
estimation of chaotic systems. Even when the equations of the system are given,
he demonstrated "chaotic likelihood functions...
Simulation models are widely employed to make probability forecasts of future conditions on seasonal to annual lead times. Added value in such forecasts is reflected in the information they add either to purely empirical statistical models, or to simpler simulation models. An evaluation of seasonal probability forecasts from the DEMETER and the ENS...
State estimation lies at the heart of many meteorological tasks. Pseudo-orbit-based data assimilation provides an attractive alternative approach to data assimilation in nonlinear systems such as weather forecasting models. In the perfect model scenario, noisy observations prevent a precise estimate of the current state. In this setting, ensemble K...
Data assimilation and state estimation for nonlinear models is a challenging task mathematically. Performing this task in real time, as in operational weather forecasting, is even more challenging as the models are imperfect: the mathematical system that generated the observations (if such a thing exists) is not a member of the available model clas...
The sensitive dependence on initial conditions (SDIC) associated with nonlinear models imposes limitations on the models’ predictive power. We draw attention to an additional limitation than has been under-appreciated, namely structural model error (SME). A model has SME if the model-dynamics differ from the dynamics in the target system. If a nonl...
Dynamical modelling lies at the heart of our understanding of physical
systems. Its role in science is deeper than mere operational forecasting, in
that it allows us to evaluate the adequacy of the mathematical structure of our
models. Despite the importance of model parameters, there is no general method
of parameter estimation outside linear syst...
Parameter estimation in nonlinear models is a common task, and one for
which there is no general solution at present. In the case of linear
models, the distribution of forecast errors provides a reliable guide to
parameter estimation, but in nonlinear models the facts that (1)
predictability may vary with location in state space, and that (2) the
d...
Parameter estimation in nonlinear models is a common task, and one for which there is no general solution at present. In the case of linear models, the distribution of forecast errors provides a reliable guide to parameter estimation, but in nonlinear models the facts that predictability may vary with location in state space, and that the distribut...
Physical processes such as the weather are usually modelled using nonlinear dynamical systems. Statistical methods are found to be difficult to draw the dynamical information from the observations of nonlinear dynamics. This thesis is focusing on combining statistical methods with dynamical insight to improve the nonlinear estimate of the initial s...
