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Quantifying the potential for improved management of weather risk using subseasonal forecasting: the case of UK telecommunications infrastructure

Brayshaw, D. J., Halford, A., Smith, S. and Kjeld, J. (2019) Quantifying the potential for improved management of weather risk using subseasonal forecasting: the case of UK telecommunications infrastructure. Meteorological Applications. ISSN 1469-8080 (In Press)

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To link to this item DOI: 10.1002/met.1849

Abstract/Summary

Reliable and affordable telecommunications are an integral part of service-based economies but the nature of the associated physical infrastructure leads to considerable exposure to weather. With unique access to observational records of the UK fixed-line telecommunications infrastructure, an end-to-end demonstration of how extended range forecasts can be used to improve the management of weather risk is presented, assessing forecast value on both short term ‘operational’ (weeks) and longer term ‘planning’ timeframes (months/years). A robust long-term weather-related fault-rate climatology is first constructed at weekly resolution, based on the ERA-Interim reanalysis. A clear dependence of winter fault rates on large-scale atmospheric circulation indices is demonstrated. The ECMWF subseasonal forecast system is subsequently shown to produce skilful forecast of winter-time weekly fault-rates at lead times of 3-4 weeks ahead (i.e., days 14-20 and 21-28). Forecast skill at a given lead-time is, however, a necessary rather than sufficient condition for improved risk management. It is shown that practical decision-making leads to dependencies across multiple forecasts times that cannot be modelled using traditional “cost-loss matrix” methods as errors in previous forecasts influence the value of subsequent forecasts. A parsimonious model representing operational decision-making for fault repair scheduling is therefore constructed to show that fault-rate forecast skill does improve both short-term and long-term management outcomes (in this case meeting performance targets more often in the short-term, or reducing the resources required to achieve these targets in the long-term). Consequently, it is argued that new methods are needed for forecast skill assessment in complex decision environments.

Item Type:Article
Refereed:Yes
Divisions:Interdisciplinary centres and themes > Centre for Technologies for Sustainable Built Environments (TSBE)
Interdisciplinary centres and themes > Energy Research
Faculty of Science > School of Mathematical, Physical and Computational Sciences > NCAS
Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
ID Code:86344
Publisher:Royal Meteorological Society

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