"Ensemble dispersion forecasting, Part I: Concept, approach and indicators"
dc.contributor.author | Galmarini, S. | |
dc.contributor.author | Bianconi, R. | |
dc.contributor.author | Klug, W. | |
dc.contributor.author | Mikkelsen, T. | |
dc.contributor.author | Addis, R. | |
dc.contributor.author | Andronopoulos, S. | |
dc.contributor.author | Astrup, P. | |
dc.contributor.author | Baklanov, A. | |
dc.contributor.author | Bartnicki, J. | |
dc.contributor.author | Bartzis, J.C. | |
dc.contributor.author | Bellasio, R. | |
dc.contributor.author | Bompay, F. | |
dc.contributor.author | Buckley, R. | |
dc.contributor.author | Bouzom, M. | |
dc.contributor.author | Champion, H. | |
dc.contributor.author | Amours, R.D. | |
dc.contributor.author | Davakis, E. | |
dc.contributor.author | Eleveld, H. | |
dc.contributor.author | Geertsema, G.T. | |
dc.contributor.author | Glaab, H. | |
dc.contributor.author | Kollax, M. | |
dc.contributor.author | Ilvonen, M. | |
dc.contributor.author | Manning, A. | |
dc.contributor.author | Pechinger, U. | |
dc.contributor.author | Persson, C. | |
dc.contributor.author | Polreich, E. | |
dc.contributor.author | Potemski, S. | |
dc.contributor.author | Prodanova, M. | |
dc.contributor.author | Saltbones, J. | |
dc.contributor.author | Slaper, H. | |
dc.contributor.author | Sofiev, M.A. | |
dc.contributor.author | Syrakov, D. | |
dc.contributor.author | Sorensen, J. H. | |
dc.contributor.author | Van der Auwera, L. | |
dc.contributor.author | Valkama, I. | |
dc.contributor.author | Zelazny, R. | |
dc.coverage.temporal | 21st century | |
dc.date | 2004 | |
dc.date.accessioned | 2016-03-07T16:16:48Z | |
dc.date.accessioned | 2021-12-09T09:53:30Z | |
dc.date.available | 2016-03-07T16:16:48Z | |
dc.date.available | 2021-12-09T09:53:30Z | |
dc.identifier.uri | https://orfeo.belnet.be/handle/internal/8670 | |
dc.description | The paper presents an approach to the treatment and analysis of long-range transport and dispersion model forecasts. Long-range is intended here as the space scale of the order of few thousands of kilometers known also as continental scale. The method is called multi-model ensemble dispersion and is based on the simultaneous analysis of several model simulations by means of ad-hoc statistical treatments and parameters. The models considered in this study are operational long-range transport and dispersion models used to support decision making in various countries in case of accidental releases of harmful volatile substances, in particular radionuclides to the atmosphere. The ensemble dispersion approach and indicators provide a way to reduce several model results to few concise representations that include an estimate of the models’ agreement in predicting a specific scenario. The parameters proposed are particularly suited for long-range transport and dispersion models although they can also be applied to short-range dispersion and weather fields. | |
dc.language | eng | |
dc.publisher | IRM | |
dc.publisher | KMI | |
dc.publisher | RMI | |
dc.relation.ispartofseries | Atmospheric Environment,38,28 | |
dc.title | "Ensemble dispersion forecasting, Part I: Concept, approach and indicators" | |
dc.type | Article | |
dc.subject.frascati | Earth and related Environmental sciences | |
dc.audience | General Public | |
dc.audience | Scientific | |
dc.subject.free | dispersion | |
dc.subject.free | forecast | |
dc.subject.free | analysis | |
dc.source.issue | Atmospheric Environment,38,28 | |
dc.source.page | pp. 4607-4617 | |
Orfeo.peerreviewed | Not pertinent |
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