Using Cutting-Edge Statistical and Data Science Techniques for Optimising and Improving Weather Forecasts - Mathematics - EPSRC DTP funded PhD Studentship

Project Description

The importance of weather forecasting cannot be overstated: it has impacts on a range of decision making, from taking an umbrella to whether flights should be cancelled or areas evacuated. It forms an important area of research which brings together disciplines such as mathematics, statistics, data science and physics.

Accurate and reliable weather forecasts are valuable, but in practice they rely on imperfect information. Mathematical forecasting models based on physics make approximations in order to forecast weather, while past and current weather data cannot be measured accurately, if at all. In addition, there many competing forecasting models (systems) making the task even more challenging. This PhD studentship will focus on investigating and developing statistical and data science techniques to optimally blend all sources of information available for forecasting weather, while at the same time quantifying all sources of uncertainty and error.

The project will look at a range of methods for achieving the goal of optimally merging various data sources. These include (but are not limited to) statistical modelling techniques such as hierarchical Bayesian modelling, Bayesian melding and machine learning algorithms. An added challenge to operational weather forecasting is the need for any such technique to be practical and computationally efficient. Therefore much of the effort will be on the optimal implementation of the developed techniques on the computer. Emphasis will also be on defining suitable metrics with which to compare the developed techniques with each other as well are with current “baseline” approaches.

This project offers a unique opportunity for a student to gain experience in advanced transferable skills across data science, statistics and machine learning. At the same time, the work is motivated by the real world challenge of improving weather forecast accuracy, which provides the opportunity to gain experience in working for and with the UK Met Office (UKMO).

The student will be based at the University of Exeter Streatham campus, but will also be expected to spend time at the UKMO in Exeter, collaborating with scientists there. The UKMO will provide CASE support so that the student will benefit from expertise in the weather forecasting team at the UKMO and training with respect to weather forecasting.

The student will also be expected to disseminate their work to the research community by attending relevant workshops and conferences across the UK, Europe but also internationally. There is a strong end-user element motivating this work, specifically the renewable energy industry, so the student will also be spending time with weather forecast users such as National Grid who strongly support this project.

The ideal candidate for this funded PhD scholarship should have a quantitative background and be interested in data analysis and fields such as statistics, data science and machine learning. They should also have an interest in computing, as it is envisioned that the techniques developed will be implemented on state-of-the-art cloud computing.

The scholarship includes UK tuition fees as well as £14,553 maintenance allowance per year. It also includes cover for development such as training courses and for travel (e.g. attending conferences).