Modeling atmospheric dispersion: Uncertainty management of release height after a nuclear accident

Atmospheric dispersion is a process that involves many uncertainties in model parameters, inputs and source parameters. In this article, we present an uncertainty management procedure for the height release at source which is a key parameter in modeling the subsequent dispersal of contamination after a nuclear accident. When setting the initial parameters of a dispersal model, it is difficult to estimate the height of the release and this will obviously affect the consequences. This procedure reduces the risk of setting an erroneous height value by running mixed model. That is, we include several models in our analysis, each with a different release height. The Bayesian methodology assigns probabilities to each model representing its relative likelihood and updates these probabilities in the light of monitoring data. The effect this has is that the data give most weight to the most likely model and thus models, which consistently badly perform can be discarded. As an illustration we perform sequential learning with an atmospheric dispersion model on a real site under real atmospheric conditions using data from tracer experiments.