Uncertainties in short term prediction of atmospheric dispersion of radionuclides. A case study of a hypothetical accident in a nuclear floating power plant off the West coast of Norway

Experiences from the ARGOS user group nuclear emergency exercise

The Accident Reporting and Guiding Operational System (ARGOS) is a decision support system used to assist in the Emergency Preparedness and Response (EPR) to nuclear and radiological incidents. The ARGOS user group has been formed that is made up of government agencies across many countries that have a role in EPR to nuclear and radiological incidents. In 2020, a desktop exercise was organised for the members of the ARGOS user group. The exercise involved two hypothetical accidents at different times on the same date, namely a radiological release from a floating nuclear power plant (NPP) off the Norwegian coast and from the Loviisa NPP in Finland. The objectives of the exercise were to train and increase knowledge of the ARGOS system, to perform a comparison of model outputs, and to compare the recommendations of protective actions. In the case of the floating NPP the source term was provided, while in the Loviisa NPP scenario the participants were required to provide their own source term based on a description of the accident. The results on radiological consequences based on dispersion modelling, protective actions, source terms and dispersion modelling settings were collected from participants. A comparison was made between each of these reported aspects. In general, it was found that there was general agreement between the results for the floating nuclear power plant scenario in the sense of plume direction and extent, while in the case of the Loviisa NPP scenario, there was much greater variation, with the difference in source term estimates between the participants being an influencing factor. The participants acknowledged that taking part in an exercise of this nature increased their knowledge and understanding about using decision support tools such as ARGOS in planning and responding to nuclear and radiological emergencies.

Localisation of atmospheric release of radioisotopes using inverse methods and footprints of receptors as sources

Releases of radionuclides to the atmosphere occasionally occur with no warning and with first observation at radioactivity monitoring stations. The Chernobyl accident of 1986 was first detected at Forsmark, Sweden, long before the official announcement by the Soviet Union, and the release of Ruthenium 106 detected across Europe in 2017 still has no official release location. The current study details a method based on footprint analysis of an atmospheric dispersion model to locate the source of an atmospheric release. The method was applied to the European Tracer EXperiment of 1994 to validate the method and to the Ruthenium observations of autumn 2017 to determine likely release locations and time characteristics of this release. The method can readily utilise an ensemble of numerical weather prediction data which improves the localisation results by taking into account meteorological uncertainties compared to only using deterministic weather data. In applying the method to the ETEX scenario, the most likely release location improved from a distance of 113 km from the true release location when using deterministic meteorology, to a distance of 63 km when using ensemble meteorology data, although such improvements may be scenario dependent. The method was constructed to be robust with respect to the choices of model parameters and measurement uncertainties. The localisation method can be useful for decision makers to enact countermeasures to protect the environment against the effects of radioactivity when observations are available from environmental radioactivity monitoring networks.

Comparing model skills for deterministic versus ensemble dispersion modelling: The Fukushima Daiichi NPP accident as a case study

Atmospheric dispersion models are crucial for nuclear risk assessment and emergency response systems since they rapidly predict air concentrations and deposition of released radionuclides, providing a basis for dose estimations and countermeasure strategies. Atmospheric dispersion models are associated with relatively large and often unknown uncertainties that are mostly attributed to meteorology, source terms and parametrisation of the dispersion model. By developing methods that can provide reliable uncertainty ranges for model outputs, decision makers have an improved basis for handling nuclear emergency situations. In the present work, model skill of the Severe Nuclear Accident Programme (SNAP) model was quantified by employing an ensemble method in which 51 meteorological realisations from a numerical weather prediction model were combined with 9 source term descriptions for the accidental ¹³⁷Cs releases from Fukushima Daiichi Nuclear Power Plant during 14th-17th March 2011. The meteorological forecast was compared to observations of wind speed from 30 meteorological stations. The 459 dispersion realisations were compared with hourly observations of activity concentrations from 100 air filter stations. Exclusive use of deterministic meteorology resulted in most members of the dispersion ensemble showing too low concentration values, however this was mitigated by applying ensemble meteorology. Ensemble predictions, including both the meteorological and source term ensemble, show an overall higher prediction skill compared to individual meteorology and source term runs, with true predictive rate accuracy increasing from 30%-50% to 70%-90%, with a decrease in positive predictive rate accuracy from 75%-80% to 65%-75%. Skill scores and other ensemble indicators also showed improvements in using ensembles of source terms and meteorology. From the present study on the Fukushima accident there are strong indications that ensemble predictions improve the basis for decision making in the early phase after a nuclear accident, which emphasises the importance of including ensemble prediction in nuclear preparedness tools of the future.