On the use of Bayesian decision theory for issuing natural hazard warnings

Theo Economou, David Stephenson, Jonathan Rougier, Robert Neal, Ken Mylne

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Warnings for natural hazards improve societal resilience and are a good example of decision-making under uncertainty. A warning system is only useful if well-defined and thus understood by stakeholders. However, most operational warning systems are heuristic: not formally or transparently defined.

Bayesian decision theory provides a framework for issuing warnings under uncertainty but has not been fully exploited. Here, a decision theoretic framework is proposed for hazard warnings. The framework allows any number of warning levels and future states of nature, and a mathematical model for constructing the necessary loss functions for both generic and specific end-users is described.

The approach is illustrated using one-day ahead warnings of daily severe precipitation over the UK, and compared to the current decision tool used by
the UK Met Office. A probability model is proposed to predict precipitation, given ensemble forecast information, and loss functions are constructed for two generic stakeholders: an end-user and a forecaster. Results show that the Met Office tool issues fewer high level warnings compared to our system for the generic end-user, suggesting the former may not be suitable for risk averse end-users. Also, raw ensemble forecasts are shown to be unreliable and result in higher losses from warnings.
Original languageEnglish
Article number20160295
Number of pages19
JournalProceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences
Issue number2194
Early online date26 Oct 2016
Publication statusPublished - Oct 2016


  • Natural hazards
  • Early warning system
  • Decision theory
  • Ensemble forecasting


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