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Home All issues Volume 55 (May 2020) HS1 Radioprotection, 55 (2020) S51-S55 References
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Issue
Radioprotection
Volume 55, May 2020
Coping with uncertainties for improved modelling and decision making in nuclear emergencies. Key results of the CONFIDENCE European research project
Page(s) S51 - S55
Section EARLY PHASE MODELLING
DOI https://doi.org/10.1051/radiopro/2020012
Published online 26 June 2020
  • Andronopoulos S, Geertsema G, Klein H, de Vries H. 2018. Guidelines ranking uncertainties for atmospheric dispersion, D9.1.2 using meteorological measurements to reduce uncertainty. CONCERT Deliverable D9.1. Available from https://concert-h2020.eu/en/Publications. [Google Scholar]
  • Astrup P, Mikkelsen TK. 2010. Comparison of NWP prognosis and local monitoring data from NPPs. Radioprotection 45(5): 97–111. https://doi.org/10.1051/radiopro/2010019. [Google Scholar]
  • Bedwell P, Wellings J, Leadbetter S, Tomas J, Andronopoulos S, Korsakissok I, Périllat R, Mathieu A, Geertsema G, Klein H, de Vries H, Hamburger T, Pázmándi T, Rudas Cs, Sogachev A, Szántó P. 2018. Guidelines ranking uncertainties for atmospheric dispersion. D9.1.4 Guidelines detailing the range and distribution of atmospheric dispersion model input parameter uncertainties. CONCERT Deliverable D9.1. Available from https://concert-h2020.eu/en/Publications. [Google Scholar]
  • Chatelard P, Reinke N, Arndt S, Belon S, Cantrel L, Carenini L, Chevalier-Jabet K, Cousin F, Eckel J, Jacq F, Marchetto C, Mun C, Piar L. 2014. ASTEC V2 severe accident integral code main features, current V2.0 modelling status, perspectives. Nucl. Eng. Des. 272: 119–135. [CrossRef] [Google Scholar]
  • Chevalier-Jabet K. 2019a. Source term prediction in case of a severe nuclear accident. In: 5th NERIS Workshop, 3–5 April 2019, Roskilde, Denmark. [Google Scholar]
  • Chevalier-Jabet K. 2019b. Design and use of Bayesian networks for the diagnosis/prognosis of severe nuclear accidents, EU program for research and innovation H2020 – FAST Nuclear Emergency Tools (FASTNET) project. Report FASTNET-DATA-D2.3. [Google Scholar]
  • Davakis E, Andronopoulos S, Kovalets I, Gounaris N, Bartzis JG, Nychas SG. 2007. Data assimilation in meteorological pre-processors: Effects on atmospheric dispersion simulations. Atmos. Environ. 41(14): 2917–2932. https://doi.org/10.1016/j.atmosenv.2006.12.031. [Google Scholar]
  • Descamps L, Labadie C, Joly A, Bazile E, Arbogast P, Cébron P. 2015. PEARP, the Météo France short-range ensemble prediction system. Q. J. Royal Meteorol. Soc. 141(690): 1671–1685. https://doi.org/10.1002/qj.2469. [CrossRef] [Google Scholar]
  • Flowerdew. 2012. Calibration and combination of medium-range ensemble precipitation forecasts. Met Office Forecasting Technical Report 567. [Google Scholar]
  • Geertsema G, De Vries H, Sheele R. 2019. High resolution meteorological ensemble data for CONFIDENCE research on uncertainties in atmospheric dispersion in the (pre-)release phase of a nuclear accident. In : 19th International Conference on Harmonisation within Atmospheric Dispersion Modelling for Regulatory Purposes, Bruges, Belgium. [Google Scholar]
  • Girard S, Mallet V, Korsakissok I, Mathieu A. 2016. Emulation and sobol sensitivity analysis of an atmospheric dispersion model applied to the Fukushima nuclear accident. J. Geophys. Res.: Atmos. 121(7): 3484–3496. https://doi.org/10.1002/2015JD023993. [CrossRef] [Google Scholar]
  • Haiden T, Janousek M, Bidlot J, Ferranti L, Prates F, Vitart F, Bauer P, Richardson DS. 2016. Evaluation of ECMWF forecasts, including the 2016 resolution upgrade. Available from https://www.ecmwf.int/sites/default/files/elibrary/2016/16924-evaluation-ecmwf-forecastsincluding-2016-resolution-upgrade.pdf. [Google Scholar]
  • Hamburger T, Gering F. 2017. Data assimilation and uncertainty handling in food chain and dose models based on model ensembles. In : CONFIDENCE Ensembles Workshop, Paris. [Google Scholar]
  • Haywood S.M. 2008. Key sources of imprecision in radiological emergency assessments. J. Radiol. Protect. 28(2): 169–183. [CrossRef] [Google Scholar]
  • Katata G, Chino M, Kobayashi T, Terada H, Ota M, Nagai H, Kajino M, Draxler R, Hort MC, Malo A, Torii T, Sanada Y. 2015. Detailed source term estimation of the atmospheric release for the Fukushima Daiichi Nuclear Power Station accident by coupling simulations of an atmospheric dispersion model with an improved deposition scheme and oceanic dispersion model. Atmos. Chem. Phys. 15(2): 1029–1070. [Google Scholar]
  • Korsakissok I, Périllat R, Andronopoulos S, Bedwell P, Berge E, Charnock T, Geertsema G, Gering F, Hamburger T, Klein H, Leadbetter S, Lind O-C, Pazmandi T, Rudas Cs, Salbu B, Sogachev A, Syed N, Tomas J, Ulimoen M, De Vries H, Wellings J. 2020. Uncertainty propagation in atmospheric dispersion models for radiological emergencies in the pre and early release phase: Summary of case studies. Radioprotection 55(HS1). https://doi.org/10.1051/radiopro/2020013. [Google Scholar]
  • Leadbetter SJ, Hort MC, Jones AR, Webster HN, Draxler RR. 2015. Sensitivity of the modelled deposition of Caesium-137 from the Fukushima Dai-ichi nuclear power plant to the wet deposition parameterisation in NAME. J. Environ. Radioact. 139: 200–211. https://doi.org/10.1016/j.jenvrad.2014.03.018. [CrossRef] [PubMed] [Google Scholar]
  • Leadbetter S, Andronopuulos S, Bedwell P, Geertsema G, Jones AR, Korsaikssok I, Thomas J, de Vries H. 2018. Guidelines ranking uncertainties for atmospheric dispersion, D9.1.1 using ensemble meteorological forecasts to represent meteorological uncertainty in dispersion models. CONCERT Deliverable D9.1. Available from https://concert-h2020.eu/en/Publications. [Google Scholar]
  • Quérel A, Roustan Y, Quélo D, Benoit J-P. 2015. Hints to discriminate the choice of wet deposition models applied to an accidental radioactive release. Int. J. Environ. Pollution 58(4). https://doi.org/10.1504/IJEP2015.077457. [Google Scholar]
  • Rao KS. 2005. Uncertainty analysis in atmospheric dispersion modeling. Pure Appl. Geophys. 162: 1893–1917. https://doi.org/10.1007/s00024-005-2697-4. [CrossRef] [Google Scholar]
  • Saunier O, Mathieu A, Didier D, Tombette M, Quélo D, Winiarek V, Bocquet M. 2013. An inverse modelling method to assess the source term of the Fukushima Nuclear Power Plant accident using gamma dose rate observations. Atmos. Chem. Phys. 13(22): 11403–11421. https://doi.org/10.5194/acp-13-11403-2013. [Google Scholar]
  • Straume AG, Koffi EN, Nodop K. 1998. Dispersion modelling using ensemble forecasts compared to ETEX measurements. J. Appl. Meteorol. 37(11): 1444–1456. [CrossRef] [Google Scholar]
  • Tennant W. 2015. Improving initial condition perturbations for MOGREPS-UK. Q. J. Royal Meteorol. Soc. 141(691): 2324–2336. https://doi.org/10.1002/qj.2524. [CrossRef] [Google Scholar]
  • Wellings J, Bedwell P, Leadbetter S, Tomas J, Andronopoulos S, Korsakissok I, Périllat R, Mathieu A, Geertsema G, de Vries H, Hamburger T, Gering F, Pázmándi T, Szánto P, Rudas Cs, Sogachev A, Davis N, Twenhofel C. 2018. Guidelines ranking uncertainties for atmospheric dispersion, D9.1.5 Guidelines for ranking uncertainties in atmospheric dispersion. CONCERT Deliverable D9.1. Available from https://concert-h2020.eu/en/Publications. [Google Scholar]

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