dc.contributor.author | Economou, T | |
dc.contributor.author | Stephenson, DB | |
dc.contributor.author | Rougier, JC | |
dc.contributor.author | Neal, RA | |
dc.contributor.author | Mylne, KR | |
dc.date.accessioned | 2016-10-27T12:46:37Z | |
dc.date.issued | 2016-10-26 | |
dc.description.abstract | 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 with our system for the generic end-user, suggesting the former may not be suitable for risk averse end-users. In addition, raw ensemble forecasts are shown to be unreliable and result in higher losses from warnings. | en_GB |
dc.description.sponsorship | This work was supported by the Natural Environment Research Council (Consortium on Risk in the Environment: Diagnostics, Integration, Benchmarking, Learning and Elicitation (CREDIBLE); grant no. NE/J017043/1). | en_GB |
dc.identifier.citation | Vol. 472 (2194), article 20160295 | en_GB |
dc.identifier.doi | 10.1098/rspa.2016.0295 | |
dc.identifier.uri | http://hdl.handle.net/10871/24111 | |
dc.language.iso | en | en_GB |
dc.publisher | Royal Society | en_GB |
dc.rights | Open access article. © 2016 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited. | en_GB |
dc.subject | natural hazards | en_GB |
dc.subject | early warning system | en_GB |
dc.subject | decision theory | en_GB |
dc.subject | ensemble forecasting | en_GB |
dc.subject | ensemble post-processing | en_GB |
dc.title | On the use of Bayesian decision theory for issuing natural hazard warnings | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2016-10-27T12:46:37Z | |
dc.identifier.issn | 1364-5021 | |
dc.description | This is the final version of the article. Available from the Royal Society via the DOI in this record. | en_GB |
dc.identifier.journal | Proceedings of the Royal Society of London. Series A, Mathematical and physical sciences | en_GB |