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SCENARIO: Understanding forecast value in complex decision-making systems


Project Description

A forecast is only useful to a decision-maker if it can be acted upon
and only valuable insofar as the resulting action leads to improved outcomes.

The application of forecasts to produce socio-economic value has long been a goal of weather and climate research. The Climate Services sector in particular – the provision of user-relevant predictions spanning weeks to decades ahead – has seen rapid growth in recent years. However, despite the apparent “end use” focus of these activities, scientific understanding of how meteorological forecasts interact with decisions in complex human- and environmental- systems is limited. This multidisciplinary project seeks to address this important research gap and lead a step-change in how meteorological forecasts are assessed.

Examples of meteorology influencing decisions in complex systems are widespread, e.g., in national infrastructure such as telecommunications and energy. To date, however, the user-value of a forecast has been typically measured through a static “cost-loss” framework (i.e., a binary decision of act/do not act; Murphy 1985, Richardson 2000; see figure). Although this cost-loss approach has provided useful insights, it is severely limited for many real-world decisions. In particular, for complex impacted systems, weather states cannot always be uniquely mapped to impact states (Brayshaw 2018).

Moreover, the decisions themselves may:

• depend on the preceding “trajectory” of forecasts over time,
• contain multiple actions across a range of lead-times,
• be updated as new forecasts become available.

Drawing upon a range of disciplines (atmospheric sciences, mathematics, statistics and engineering), this project will seek to extend the “cost-loss” framework. It will develop novel methods to maximize the quality of impact forecasts for infrastructure applications and, by linking meteorological forecasts to a suite of decision-models, it will develop an in-depth process understanding of how forecast quality transfers to forecast value. These decision models will be informed by “real world” examples, spanning a hierarchy of complexity and timescales (e.g., day-to-day operations to multi-year planning). A specific example concerning the management of the UK’s fixed-line telecommunications network will be explored through a CASE partnership with BT Research. Alongside developing novel scientific insights, the project will also therefore contribute to informing improved weather-management techniques for critical aspect UK national infrastructure.
.
Training opportunities:
The student will join the Energy Meteorology research group (research.reading.ac.uk/met-energy) in the Meteorology Department at Reading University and work with an experienced supervisory team. Worldleading MSc-level training in meteorological science will be provided, and students encouraged to participate in relevant summer schools, seminars and workshops. Through a CASE studentship with BT Research (cosupervisor Jensen) and there will be opportunities to engage with and undertake a placement with the industry partner. On completion, it is expected that the student will have a good grounding in climate science and its applications with excellent career prospects, particularly within the growing Climate Services sector.

Funding Notes

This project is potentially funded by the SCENARIO NERC Doctoral Training Partnership, subject to a competition to identify the strongest applicants. To apply, please follow the instructions at View Website .

Applicants should hold or expect to gain a minimum of a 2:1 Bachelor Degree, Masters Degree with Merit, or equivalent in (ideally) mathematics or a closely related environmental or physical science.

Due to restrictions on the funding this studentship is only open to UK students and EU students who have lived in the UK for the past three years.

How good is research at University of Reading in Earth Systems and Environmental Sciences?

FTE Category A staff submitted: 75.68

Research output data provided by the Research Excellence Framework (REF)

Click here to see the results for all UK universities

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