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

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.