Understanding forecast value in complex decision-making systems

The application of meteorological forecasts to produce socio-economic value is a major focus of the World Weather Research Programme (WMO-WWRP), with "climate services" particularly seeing rapid growth. However, despite the apparent "end use" focus of these nascent services, the complexity of the impacted human- or environmental- system is often neglected in value assessments of meteorological forecasts. This is a significant gap in understanding, as decision-making is contingent on the responses of complex human- or environmental- system to weather inputs, many of which can be non-intuitive. This project addresses this research gap and will lead a step-change in how meteorological forecast value is assessed for decision making.

Subseasonal-to-seasonal numerical weather prediction offers the potential for skillful probabilistic meteorological forecasts weeks- to months- ahead. While considerable effort has been devoted to improving forecast quality, an understanding of forecast value (the benefit to a decision-maker wishing to act on a forecast; Murphy 1993) remains limited. To date, user-value has been typically viewed through a static "cost-loss" framework applied at a fixed time-horizon (i.e., a binary decision of "act"/"do not act" at some time-point; Murphy 1993, Richardson 2000). Although this approach is general and provides useful insights, it is severely limited for many real-world decisions. In particular, weather states cannot necessarily be uniquely linked to particular states of a complex impacted system (Brayshaw 2018) and decisions also often:
- depend on the preceding "trajectory" of forecast errors over time
- contain multi-leveled actions (i.e., at any given time, a whole set of actions may be taken across multiple lead times)
- updated as new forecasts become available.

Decisions with these properties are found in many sectors (e.g., energy, telecommunications). In this project, a CASE partner responsible for operating ~90% of the fixed-line telecommunications infrastructure in Great Britain will provide a concrete example of a decision-making context. In the UK, an estimated net economic contribution of £33bn/year is attributable to telecommunications infrastructure (Kelly, 2015) but the exposed nature of fixed-line telecommunications leads to significant weather risk (BT annual reports 2013-2018). Skillfully predicting "faults" (i.e., improving fault rate forecast quality) and managing resources for their subsequent repair (i.e., extracting forecast value) is therefore a key issue, with management decisions taken across many time-horizons for both short-term operations (weeks) and longer-term planning (years).

This project will address two research areas:

1. Developing novel methods to maximize the quality of impact forecasts for infrastructure applications. This includes, for example, contrasting pattern-based forecasting methods against grid-point based forecasts.

2. Understanding the link between forecast value and forecast quality in complex decision-making systems. This will draw on a range of decision-making examples across a hierarchy of complexity and time-scale (e.g., day-to-day operations vs. seasonal planning).

Accessing business data and identifying decision protocols is typically a major obstacle for understanding forecast value. However, through the CASE partner, the student will have access to both historic data and decision protocols pertaining to a large part of the UK fixed-line telecommunications network. Alongside the novel and general scientific methods developed, the project is therefore also expected to contribute to informing the design of improved weather-management techniques for a key aspect of UK infrastructure.