Ranking uncertainties in atmospheric dispersion modelling following the accidental release of radioactive material

Abstract

During the pre-release and early phase of an accidental release of radionuclides into the atmosphere there are few or no measurements, and dispersion models are used to assess the consequences and assist in determining appropriate countermeasures. However, uncertainties are high during this early phase and it is important to characterise these uncertainties and, if possible, include them in any dispersion modelling. In this paper we examine three sources of uncertainty in dispersion modelling; uncertainty in the source term, uncertainty in the meteorological information used to drive the dispersion model and intrinsic uncertainty within the dispersion model. We also explore the possibility of ranking these uncertainties dependent on their impact on the dispersion model outputs.

Quantification of uncertainties in the assessment of an atmospheric release source applied to the autumn 2017 <sup>106</sup>Ru event

“…Weather forecast uncertainties arise from errors in the initial conditions and approximations in the construction of the numerical model. They can be evaluated through the use of an ensemble forecast (Leith, 1974). Several realisations of the same forecast are considered, where for each realisation, the initial condition and the numerical model are perturbed.…”

Modeling Urban Atmospheric Lead Dispersion from a Mining Tailings Basin in Bahia, Brazil

“…where λ dep is the deposition coefficient. Dry deposition is a continuous process, while wet deposition can only occur in the presence of precipitation, when atmospheric hydrometeors collide with airborne particles collecting them as they fall (Leadbetter et al 2020;Leelossy et al 2018). Moreover, as the particles are removed from the air, street washing also occurs, resulting in ë air lav air L P C…”

Quantification of the modelling uncertainties in atmospheric release source assessment and application to the reconstruction of the autumn 2017 Ruthenium 106 source

“…For most of the times, the ensemble values do not even recover the HRES value, which indicates that the ensemble has a small dispersion. ECMWF ensemble forecasts indeed tend to be under-dispersive in the boundary layer (especially in the short range) (Pinson and Hagedorn, 2011;Leadbetter et al, 2020). Furthermore, the release height parameter has low chances to be of significant impact.…”