Ensemble based first guess support towards a risk-based severe weather warning service

Neal, Robert A.; Boyle, Patricia; Grahame, Nicholas J.; Mylne, Ken; Sharpe, Michael

doi:10.1002/met.1377

Cited by 55 publications

(65 citation statements)

References 31 publications

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“…By quantifying impacts and their likelihood, which is particularly relevant to recipients, the acceptance of weather warnings might be strongly enhanced. As one of the first national weather services, the UK Met Office has recently moved on to a risk-based warning system (Neal et al, 2013). The basis of such a warning system is formed by the risk matrix, composed of the two dimensions impact and likelihood.…”

Section: Summary Discussion and Conclusionmentioning

confidence: 99%

An analysis of uncertainties and skill in forecasts of winter storm losses

Pardowitz¹,

Osinski²,

Kruschke

et al. 2016

Nat. Hazards Earth Syst. Sci.

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Abstract. This paper describes an approach to derive probabilistic predictions of local winter storm damage occurrences from a global medium-range ensemble prediction system (EPS). Predictions of storm damage occurrences are subject to large uncertainty due to meteorological forecast uncertainty (typically addressed by means of ensemble predictions) and uncertainties in modelling weather impacts. The latter uncertainty arises from the fact that local vulnerabilities are not known in sufficient detail to allow for a deterministic prediction of damages, even if the forecasted gust wind speed contains no uncertainty. Thus, to estimate the damage model uncertainty, a statistical model based on logistic regression analysis is employed, relating meteorological analyses to historical damage records. A quantification of the two individual contributions (meteorological and damage model uncertainty) to the total forecast uncertainty is achieved by neglecting individual uncertainty sources and analysing resulting predictions. Results show an increase in forecast skill measured by means of a reduced Brier score if both meteorological and damage model uncertainties are taken into account. It is demonstrated that skilful predictions on district level (dividing the area of Germany into 439 administrative districts) are possible on lead times of several days. Skill is increased through the application of a proper ensemble calibration method, extending the range of lead times for which skilful damage predictions can be made.

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Section: Summary Discussion and Conclusionmentioning

confidence: 99%

An analysis of uncertainties and skill in forecasts of winter storm losses

Pardowitz¹,

Osinski²,

Kruschke

et al. 2016

Nat. Hazards Earth Syst. Sci.

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“…Ainda como destacado por MacKenzie (2014), o desenvolvimento de um novo índice deve ser adequado ao público-alvo, a fim de determinar o formato final e a respectiva aplicação. Como debatido por Neal et al (2014) e Caruzzo et al (2015, para interpretar a previsão de tempo e transformá-la em um índice de decisão, é necessário incorporar as percepções dos usuários não-meteorologistas em relação às três características específicas da informação meteorológica, sendo:…”

Section: Etapa 2: íNdice De Decisãounclassified

“…Apesar do desenvolvimento dos sistemas computacionais e de novas técnicas de observação da atmosfera, o prognóstico das condições atmosféricas ainda possuem incertezas (Neal et al, 2014). Além disso, muitas vezes os usuários não-meteorologistas tem o desafio de avaliar a previsão de tempo e tomar decisões baseados em uma informação determinística, sem uma análise das probabilidades.…”

Section: Introductionunclassified

Decisão em Condições de Incerteza Meteorológica e Proteção de Infraestruturas no Centro de Lançamento de Alcântara

Caruzzo

Belderrain

Fisch

et al. 2017

Rev. bras. meteorol.

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A tomada de decisão utilizando informações meteorológicas muitas vezes é um processo complexo, de alto impacto e, em algumas situações, pode comprometer o funcionamento de instalações tecnológicas. Neste contexto, o objetivo deste artigo é apresentar uma abordagem de apoio à decisão em condições de incerteza meteorológica para a proteção das infraestruturas no Centro de Lançamento de Alcântara. A partir de uma demanda operacional, foi desenvolvido um índice meteorológico de modo a identificar a atitude dos decisores em relação às três características da informação meteorológica: a) probabilidade da previsão de tempo; b) prazo de validade; c) valor da variável atmosférica. Como estudo de caso, foi analisado um problema real de decisão para proteção das instalações do principal centro espacial brasileiro. Através da interação com os usuários na construção do índice, foi possível concluir que para probabilidades baixas (< 20%) ou para prazos de validade estendidos (> 24 h), o grupo de usuários entrevistados não utiliza a informação meteorológica para tomar decisões, mesmo com o prognóstico de condições extremas. Os resultados quantitativos demonstraram que o modelo de apoio à decisão proposto conseguiu incorporar a atitude do usuário não-meteorologista em relação à incerteza da informação meteorológica. Palavras-chave: índice de decisão meteorológico, risco meteorológico, análise multicritério, centro de lançamento de foguetes, proteção de infraestrutura crítica. Decision Under Weather Uncertainty and Infrastructure Protection for Alcantara Launch CenterAbstract Decision-making using weather information is often a complex and high impact process, and in some situations, can disturb the functioning of technological facilities. In this sense, the aim of this paper is to present a decision support approach for infrastructure protection under weather uncertainty at the Alcântara Launch Center, in Brazil. From an operational demand, a weather index was developed to identify the decision maker's attitude about the three characteristics of meteorological information: a) weather forecast probability; b) lead time of the information; c) value of a meteorological variable. As a case study, we analyzed a real decision-making problem for the protection of an aerospace vehicle and integration tower in the Brazilian space center. Through the development of an index with the users, it was concluded that for low probabilities (< 20%) or extended lead time (> 24 h), the group of users do not use weather information to make decisions, even with a prognosis of extreme conditions. The quantitative results showed that the decision model managed to incorporate the attitude of non-meteorologist users regarding the handling of uncertainty in weather forecasts.

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“…The UK Met Office NSWWS [2] provides warnings to civil responder services and the public using a risk-based ‘traffic light’ colour scheme where risk is assessed as a combination of likelihood and impact severity using the matrix illustrated in figure 2. The four warning levels (green, yellow, amber, red) are associated with top-level responder advice of ‘no severe weather’, ‘be aware’, ‘be prepared’ and ‘take action’.…”

Section: Example: Severe Weather Warningsmentioning

confidence: 99%

“…Many operational EWSs, such as the Met Office National Severe Weather Warning Service (NSWWS) [2,3] and the flood warning system of the UK Environment Agency [4], are heuristic. The response to (and thus the overall effectiveness of) a warning system depends heavily on users believing that the warning is credible and accurate [5].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2016

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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 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.

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Ensemble based first guess support towards a risk-based severe weather warning service

Cited by 55 publications

References 31 publications

An analysis of uncertainties and skill in forecasts of winter storm losses

An analysis of uncertainties and skill in forecasts of winter storm losses

Decisão em Condições de Incerteza Meteorológica e Proteção de Infraestruturas no Centro de Lançamento de Alcântara

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

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