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Combination of measurements and model predictions after a release of radionuclides

Kombination von Messdaten und Modellprognosen nach einer Freisetzung von Radionukliden
F. Gering, K. Richter and H. Müller
From the journal Kerntechnik

Abstract

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, contamination measurements of foodstuffs) 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 and the food chain 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.

Kurzfassung

Modellrechnungen zur schnellen Einschätzung und Prognose von möglichen radiologischen Konsequenzen nach einer unfallbedingten Freisetzung von Radionukliden spielen eine große Rolle im radiologischen Notfallmanagement. Zur Verbesserung dieser Modellrechnungen können in erster Linie radiologische Messungen (z. B. Messungen der Gammadosisrate oder der Kontamination von Nahrungsmitteln) verwendet werden. Ein Verfahren zur Kombination von Modellergebnissen und Messungen (Datenassimilation) wird in dieser Arbeit für das Depositions- und das Nahrungskettenmodell des europäischen radiologischen Entscheidungshilfe-Systems RODOS vorgestellt. Das Verfahren beruht auf dem Ensemble Kalman Filter, eine Monte-Carlo Variante des Kalman Filters.

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Received: 2004-10-6
Published Online: 2013-05-02
Published in Print: 2004-11-01

© 2004, Carl Hanser Verlag, München