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• W2893182214 endingPage "266" @default.
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• W2893182214 abstract "During volcanic eruptions, aviation stakeholders require an assessment of the volcanic ash hazard. Operators and regulators are required to make fast decisions based on deterministic forecasts, which are subject to various sources of uncertainty. For a robust decision to be made, a measure of the uncertainty of the hazard should be considered, but this can lead to added complexity preventing fast decision‐making. A proof‐of‐concept risk‐matrix approach is presented that combines uncertainty estimation and volcanic ash hazard forecasting into a simple warning system for aviation. To demonstrate the methodology, an ensemble of 600 dispersion model simulations is used to characterize uncertainty (due to eruption source parameters, meteorology and internal model parameters) in ash dosages and concentrations for a hypothetical Icelandic eruption. To simulate aircraft encounters with volcanic ash, trans‐Atlantic air routes between New York (JFK) and London (LHR) are generated using time‐optimal routing software. This approach was developed in collaboration with operators, regulators and engine manufacturers; it demonstrates how an assessment of ash dosage and concentration risk can be used to make fast and robust flight‐planning decisions, even when the model uncertainty spans several orders of magnitude. The results highlight the benefit of using an ensemble over a deterministic forecast and a new method for visualizing dosage risk along flight paths. The risk‐matrix approach is applicable to other aviation hazards such as sulphur dioxide (SO 2 ) dosages, desert dust, aircraft icing and clear‐air turbulence, and is expected to aid flight‐planning decisions by improving the communication of ensemble‐based forecasts to aviation." @default.
• W2893182214 created "2018-10-05" @default.
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• W2893182214 date "2018-12-06" @default.
• W2893182214 modified "2023-10-18" @default.
• W2893182214 title "Calculating and communicating ensemble‐based volcanic ash dosage and concentration risk for aviation" @default.
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• W2893182214 doi "https://doi.org/10.1002/met.1759" @default.
• W2893182214 hasPublicationYear "2018" @default.
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