Probabilistic data assimilation within RODOS

An outline of the uncertainty handling and data assimilation framework within a Bayesian frame is given, illustrated by its use within RODOS. Particular emphasis is paid to the need for compatible methodologies and data structures to hold uncertainty assessments throughout all the modules of a decision support system. Progress at developing techniques and modules is described: (i) to use belief nets to predict the source term when an accidental release threatens; (ii) to estimate the source term using a ring of gamma monitors at the periphery of the plant: and (iii) to estimate the source term from both near and more distant measurements. The management of uncertainties in the food chain modelling is briefly indicated. Finally, the issue of moving the basis of prediction from modelling approaches to databases of environmental measurements is discussed.