Hostname: page-component-cd9895bd7-jn8rn Total loading time: 0 Render date: 2024-12-29T05:22:18.935Z Has data issue: false hasContentIssue false
  • English
  • Français

Guidance on monitoring and data assimilation

Published online by Cambridge University Press:  16 September 2010

Wolfgang Raskob ,
Michel Hugon ,
J. Lahtinen ,
H.K. Aage ,
M. Ammann ,
J. E. Dyve ,
S. Hoe ,
C. Rojas-Palma  and
E. Wirth
J. Lahtinen
Affiliation:
Radiation and Nuclear Safety Authority (STUK), PO Box 14, 00881 Helsinki, Finland
H.K. Aage
Affiliation:
Danish Emergency Management Agency (DEMA), Datavej 16, 3460 Birkeroed, Denmark
M. Ammann
Affiliation:
Radiation and Nuclear Safety Authority (STUK), PO Box 14, 00881 Helsinki, Finland
J. E. Dyve
Affiliation:
Norwegian Radiation Protection Authority (NRPA), PO Box 55, 1332 Österås, Norway
S. Hoe
Affiliation:
Danish Emergency Management Agency (DEMA), Datavej 16, 3460 Birkeroed, Denmark
C. Rojas-Palma
Affiliation:
Belgian Nuclear Research Center (SCK/CEN), 200 Boeretang, 2400 Mol, Belgium
E. Wirth
Affiliation:
Bundesamt f¸r Strahlenschutz (BfS), PO Box 10 01 49, 38201 Salzgitter, Germany
Get access

Abstract

Decision makers must react in a prompt and appropriate manner in various emergency situations. The bases for decisions are often predictions produced with decision support systems (DSS). Actual radiation measurement data can be used to improve the reliability of the predictions. Data assimilation is an important link between model calculations and measurements and thus decreases the overall uncertainty of the DSS predictions. However, different aspects have to be taken into account for the optimal use of the data assimilation technique: different countries may have differing measurement strategies and systems as well as differing calculation models. The scenario and the amount and composition of radionuclides released may vary. In this paper we analyse the situation during and after an accident and draw up a list of recommendations that can help modellers to take into account the measurements that are best suited for data assimilation.

Type
Article
Information
Radioprotection , Volume 45 , Issue 5: Enhancing nuclear and radiological emergencymanagement and rehabilitation: Key Results of the EURANOS European Project. , 2010 , pp. S161 - S169
Copyright
© EDP Sciences, 2010

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Astrup P., Turcanu C., Puch R.O., Rojas-Palma C., Mikkelsen T. (2004) Data assimilation in the early phase: Kalman filtering Rimpuff, Report Risø-R-1466(EN). Risø National Laboratory, Roskilde, Denmark.
Ehrhardt J., Weis A. (2000) RODOS: Decision support for off-site nuclear emergency management in Europe, Report EUR 19144 EN. European Commission, Luxembourg.
European Commission (2006) “Guidance on model adaptation driven by monitoring data”, the work package CAT1RTD12 in the integrated project European Approach to Nuclear and Radiological Emergency Management and Rehabilitation Strategies (EURANOS) of the Sixth Framework Programme of the European Union (Contract number: FI6R-CT-2004-508843).
French S., Smith J.Q. (1997) The Practice of Bayesian Analysis, Arnold, UK.
Hoe S., Müller H., Thykier Nielsen S. (2000) Integration of dispersion and radio-ecological modelling in ARGOS NT. In: Proceedings of the 10th International Congress of the International Radiation Protection Association – Harmonization of Radiation, Human Life and the Ecosystem, May 14-19, 2000, Hiroshima, Paper P-11-288, 7 p. http://www.irpa.net/irpa10/cdrom/00754.pdf.
Kaiser, J.C., Pröhl, G. (2007) Harnessing monitoring measurements in urban environments for decision making after nuclear accidents, Kerntechnik 72, 218-221.CrossRefGoogle Scholar
Kaiser, J.C. et al. (2010) Data assimilation approaches in the EURANOS projet, Radioprotection 45, S123-S131.CrossRefGoogle Scholar
Lahtinen, J., Toivonen, H., Hänninen, R. (2007) Effective use of radiation monitoring data and dispersion calculations in an emergency, IJEM 4, 468-480.CrossRefGoogle Scholar
Meckbach, R, Jacob, P. (1988) Gamma exposures due to radionuclides deposited in urban environments. Part I: Kerma rates from contaminated urban surfaces, Radiat. Prot. Dosim. 25, 167-179.Google Scholar
Meckbach, R., Jacob, P., Paretzke, H.G. (1988) Gamma exposures due to radionuclides deposited in urban environments. Part II: Location factors for different deposition patterns, Radiat. Prot. Dosim. 25, 181-190.Google Scholar
Quélo, D., Sportisse, B., Isnard, O. (2005) Data assimilation for short range atmospheric dispersion of radionuclides: A case study of second-order sensitivity, J. Environ. Radioact. 84, 393-408.CrossRefGoogle ScholarPubMed
Raskob W. (2008) The real-time on-line decision support system RODOS. Presentation given in the training course Preparedness and response for nuclear and radiological emergencies, September 15-19, 2008, Mol, Belgium. http://www.sckcen.be/en/Media/Files/Events/tcmol2008/L21_Raskob_Rodos08.pdf.
Rojas-Palma, C., Madsen, H., Gering, F., Puch, R., Turcanu, C., Astrup, P., Müller, H., Richter, K., Zheleznyak, M., Treebushny, D., Kolomeev, M., Kamaev, D., Wynn, H. (2003) Data assimilation in the decision support system RODOS, Radiat. Prot. Dosim. 104, 31-40.CrossRefGoogle ScholarPubMed
Wirth E., Kirchner G. (2008) One environmental monitoring strategy for emergency situations is enough. In: Proceedings of the International Conference on Radioecology & Environmental Radioactivity, June15-20, 2008, Bergen, Norway, Oral & Oral Poster Presentations, Part I (Strand P., Brown P., Jølle T., Eds.) pp. 158-161. Norwegian Radiation Protection Authority, Østerås, Norway.
Zähringer, M., Wirth, E. (2007) The interaction between off-site decision making, decision support systems, modelling and monitoring in a nuclear emergency situation, IJEM 4, 564-572.CrossRefGoogle Scholar