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Data assimilation approaches in the EURANOSproject

Published online by Cambridge University Press:  16 September 2010

J.C. Kaiser
Affiliation:
Helmholtz-Zentrum München – Institute of Radiation Protection, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany
F. Gering
Affiliation:
Bundesamt für Strahlenschutz - SW 3.2, Ingolstädter Landstr. 1, 85764 Oberschleißheim, Germany
P. Astrup
Affiliation:
Risø National Laboratory DTU, PO Box 49, 4000 Roskilde, Denmark
T. Mikkelsen
Affiliation:
Risø National Laboratory DTU, PO Box 49, 4000 Roskilde, Denmark
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Abstract

Within the EURANOS project data assimilation (DA) approaches have been successfullyapplied in two areas to improve the predictive power of simulation models used in theRODOS and ARGOS decision support systems. For the areas of atmospheric dispersionmodelling and of modelling the fate of radio-nuclides in urban areas the results ofdemonstration exercises are presented here. With the data assimilation module of theRIMPUFF dispersion code, predictions of the gamma dose rate are corrected with simulatedreadings of fixed detector stations. Using the DA capabilities of the IAMM package formapping the radioactive contamination in inhabited areas, predictions of a large scaledeposition model have been combined with hypothetical measurements on a local scale. Inboth examples the accuracy of the model predictions has been improved and theuncertainties have been reduced.

Type
Article
Copyright
© EDP Sciences, 2010

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References

Astrup P., Turcanu C., Puch R.O., Rojas-Palma C., Mikkelsen T. (2004) Data assimilation in the early phase: Kalman filtering RIMPUFF, Risø-R-1466(EN), www.risoe.dk/rispubl/VEA/ris-r-1466.htm.
Evensen, G. (2003) The Ensemble Kalman Filter: Theoretical Formulation and Practical Implementation, Ocean Dyn. 53, 343-367.CrossRefGoogle Scholar
Gering F. (2005) Data assimilation methods for improving the prognoses of radionuclide deposition from radio-ecological models with measurements, Ph.D. Thesis, Leopold-Franzens-Universität Innsbruck, Austria, 145 p.
Kaiser, J.C., Pröhl, G. (2007) Harnessing monitoring measurements in urban environments for decision making after nuclear accidents, Kerntechnik 7, 218-221.CrossRefGoogle Scholar
Kalman, R.E. (1960) A new approach to linear filtering and prediction problems, Trans. ASME. J. Basic Eng. 82, 35-45.CrossRefGoogle Scholar
Thiessen, K.M., Andersson, K.G., Batandjieva, B., Cheng, J.-J., Hwang, W.-T., Kaiser, J.C., Kamboj, S., Steiner, M., Tomas, J., Trifunovic, D., Yu, C. (2009) Modelling the long-term consequences of a hypothetical dispersal of radioactivity in an urban area including remediation alternatives, J. Environ. Radioact. 100, 445455.CrossRefGoogle Scholar