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Bayesian updating of atmospheric dispersion after a nuclear accident
SMHI, Research Department, Air quality.ORCID iD: 0000-0003-2738-5556
2004 (English)In: Journal of the Royal Statistic Society, Series C: Applied Statistics, ISSN 0035-9254, E-ISSN 1467-9876, Vol. 53, 583-600 p.Article in journal (Refereed) Published
Abstract [en]

We consider a Bayesian forecasting. system to predict the dispersal of contamination on a large scale grid in the event of an accidental release of radioactivity. The statistical model is built on a physical model for atmospheric dispersion and transport called MATCH. Our spatiotemporal model is a dynamic linear model where the state parameters are the (essentially, deterministic) predictions of MATCH; the distributions of these are updated sequentially in the light of monitoring data. One of the distinguishing features of the model is that the number of these parameters is very large (typically several hundreds of thousands) and we discuss practical issues arising in its implementation as a realtime model. Our procedures have been checked against a variational approach which is used widely in the atmospheric sciences. The results of the model are applied to test data from a tracer experiment.

Place, publisher, year, edition, pages
2004. Vol. 53, 583-600 p.
Keyword [en]
adjoint equations, atmospheric dispersion, data assimilation, dynamic linear models
National Category
Environmental Sciences
Research subject
Environment
Identifiers
URN: urn:nbn:se:smhi:diva-1331DOI: 10.1111/j.1467-9876.2004.04837.xISI: 000224094900004OAI: oai:DiVA.org:smhi-1331DiVA: diva2:814115
Available from: 2015-05-26 Created: 2015-05-26 Last updated: 2017-12-04Bibliographically approved

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Robertson, Lennart

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