Model predictions for a rapid assessment and prognosis of possible radiological consequences after an accidental release of radionuclides play an important role in nuclear emergency management. Radiological observations, e.g. dose rate measurements, can be used to improve such model predictions. The process of combining model predictions and observations, usually referred to as data assimilation, is described in this article within the framework of the real time on-line decision support system (RODOS) for off-site nuclear emergency management in Europe. Data assimilation capabilities, based on Kalman filters, are under development for several modules of the RODOS system, including the atmospheric dispersion, deposition, food chain and hydrological models. The use of such a generic data assimilation methodology enables the propagation of uncertainties throughout the various modules of the system. This would in turn provide decision makers with uncertainty estimates taking into account both model and observation errors. This paper describes the methodology employed as well as results of some preliminary studies based on simulated data.
An experimental study of radionuclide dispersion in the atmosphere has been conducted at the BR1 research reactor in Mol, Belgium. Artificially generated aerosols ('white smoke') were mixed with the routine releases of (41)Ar in the reactor's 60-m tall venting stack. The detailed plume geometry was measured with remote sensing (Lidar) of the aerosol plumes while surface radiation levels were measured under the plume using gamma detectors at downwind distances of up to 1500 m from the release point. A database was built with simultaneous measurements of plume geometry and radiation field from (41)Ar decay, together with in-situ measurements of meteorological parameters. The joint tracer/radiation experimental dataset has been subsequently used to evaluate the accuracy of predictions of dispersion parameters and gamma fluence rates obtained by the atmospheric dispersion and dose rate model RIMPUFF.
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