Uncertainties were propagated through the chain of atmospheric dispersion and radiological assessment models based on an ensemble approach for a range of scenarios. It was apparent that the time taken to complete model runs, ranging from several hours to a few tens of hours, was not appropriate for an emergency response. Thus, for an operational method, there was a requirement to reduce the number of ensemble members and/or reduce model run time for a single ensemble member, such that a measure of uncertainty may be obtained within the timeframe of one hour, but without significant detriment to the model endpoints derived, the uncertainty estimated and the radiation protection advice inferred. This study proposes recommendations for operationalising an ensemble approach used in the description of uncertainty in atmospheric dispersion modelling and an emergency response.
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A methodology should be developed for simplifying ensemble model runs, focusing on reducing the time taken to process and output model results. Different spatial and temporal resolutions of meteorological data could be considered in an investigation of the effect on the accuracy of results and computational time. The use of an emulator to save computational time could be investigated. It would be beneficial to investigate the inclusion of other meteorological variables (such as wind speed, boundary layer height, atmospheric stability and precipitation) as part of any clustering criteria. Further work assessing the ability of different methods to suitably sample ensemble members from a full ensemble configuration would be of value (e.g. adaptive sampling and Latin Hypercube Methods). Investigation into whether the work of Klonner (2013) and Galmarini et al. (2004a, 2004b and 2004c) could be applied to the estimation of uncertainty within the provision of radiological protection advice following an accidental release to atmosphere would also be worthwhile. Further work is required to assess the suitability of a priori clustering on the basis of statistical analysis. The work already carried out in relation to evaluating uncertainty estimation by comparison with environmental observations (Korsakissok et al., 2019a) should be extended. Furthermore, efficient methods should be developed for combining prior knowledge of uncertainties with observational data. A more detailed description of proposed future work can be found in Bedwell et al. (2019). Acknowledgement. The work described in this paper was conducted within the CONFIDENCE project which was part of the CONCERT project. This project has received funding from the Euratom research and training programme 2014– 2018 under grant agreement No. 662287.
© The Authors, published by EDP Sciences 2020.
- Atmospheric dispersion
- Emergency response