Article

Uncertainty propagation in atmospheric dispersion models for radiological emergencies in the pre- and early release phase: summary of case studies

¹ IRSN – Institute for Radiation Protection and Nuclear Safety, Fontenay-aux-Roses, France
² PHIMECA engineering, Clermont-Ferrand, France
³ EEAE/NCSRD − Greek Atomic Energy Commission/National Centre for Scientific Research “Demokritos”, Agia Paraskevi, Greece
⁴ PHE – Public Health England, Didcot, UK
⁵ NMI MET – Norwegian Meteorological Institute, Oslo, Norway
⁶ KNMI – Royal Netherlands Meteorological Institute, de Bilt, The Netherlands
⁷ BfS − Federal Office for Radiation Protection, Neuherberg, Germany
⁸ Met Office, Exeter, UK
⁹ NMBU/CERAD − Norwegian University of Life Sciences, Centre for Environmental Radioactivity, Ås, Norway
¹⁰ EK − Centre for Energy Research, Budapest, Hungary
¹¹ DTU Wind Energy, Roskilde, Denmark
¹² DSA – Norwegian Radiation and Nuclear Safety Authority, Østerås, Norway
¹³ RIVM – National Institute for Public Health and the Environment, Bilthoven, The Netherlands

^* Corresponding author: irene.korsakissok@irsn.fr

Abstract

In the framework of the European project CONFIDENCE, Work Package 1 (WP1) focused on the uncertainties in the pre- and early phase of a radiological emergency, when environmental observations are not available and the assessment of the environmental and health impact of the accident largely relies on atmospheric dispersion modelling. The latter is subject to large uncertainties coming from, in particular, meteorological and release data. In WP1, several case studies were identified, including hypothetical accident scenarios in Europe and the Fukushima accident, for which participants propagated input uncertainties through their atmospheric dispersion and subsequent dose models. This resulted in several ensembles of results (consisting of tens to hundreds of simulations) that were compared to each other and to radiological observations (in the Fukushima case). These ensembles were analysed in order to answer questions such as: among meteorology, source term and model-related uncertainties, which are the predominant ones? Are uncertainty assessments very different between the participants and can this inter-ensemble variability be explained? What are the optimal ways of characterizing and presenting the uncertainties? Is the ensemble modelling sufficient to encompass the observations, or are there sources of uncertainty not (sufficiently) taken into account? This paper describes the case studies of WP1 and presents some illustrations of the results, with a summary of the main findings.