This paper presents an operational framework for assessing the probability of occurrence of in-cloud and precipitation icing and its duration. The framework utilizes the features of the Bayesian Probabilistic Networks. and its performance is illustrated through a case study of the cable-stayed Oresund Bridge. The Bayesian Probabilistic Network model used for the estimation of the occurrence and duration probabilities is studied and it is found to be robust with respect to changes in the choice of distribution types used to model the meteorological variables that influence the two icing mechanisms and their duration. The model is found to be more sensitive to changes in the discretization levels of the input variables. Finally, it is shown how forecasting of the meteorological variables, that is the probabilities of the occurrence of ice accretion and its duration, can be used to update the model. The updated probabilities can be used as a decision support tool for the management of risk and safety with respect to falling ice.