Data assimilation in the decision support system rodos.
2003
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 managementin Europe.
Data assimilationcapabilities, based on Kalman filters, are under development for several modules of the RODOS system, including the atmospheric dispersion, deposition,
food chainand
hydrological models. The use of such a generic
data assimilationmethodology enables the
propagationof
uncertaintiesthroughout 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.
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