Correction of deposition predictions with data assimilation

Model predictions for rapid assessment and prognosis of possible radiological consequences after an accidental release of radionuclides play an important role in nuclear emergency management. Radiological measurements (e. g., dose rate measurements) can be used to improve such model predictions. This paper describes a method for combining model predictions and measurements (data assimilation) in the deposition model of the European radiological decision support system RODOS. The data assimilation approach is based on the Ensemble Kalman Filter, a Monte Carlo variant of the Kalman filter. (orig.)