The benefit of modeled ozone data for the reconstruction of a 99‐year UV radiation time series
Identifieur interne : 000755 ( Main/Exploration ); précédent : 000754; suivant : 000756The benefit of modeled ozone data for the reconstruction of a 99‐year UV radiation time series
Auteurs : J. Junk [Luxembourg (pays)] ; U. Feister [Allemagne] ; A. Helbig [Allemagne] ; K. Görgen [Luxembourg (pays)] ; E. Rozanov [Suisse] ; J. W. Krzy Cin [Pologne] ; L. Hoffmann [Luxembourg (pays)]Source :
- Journal of Geophysical Research: Atmospheres [ 0148-0227 ] ; 2012-08-27.
Descripteurs français
- Pascal (Inist)
- Allemagne, Berlin, Bâti, Concentration, Contenu total ozone, Couche ozone, Distance zénithale solaire, Durée insolation, Elément météorologique, Long terme, Mesure basée sol, Modèle, Observatoire, Ozone, Rayonnement UV, Récupération, Réseau neuronal, Santé publique, Siècle 21eme, Stratosphère, Série temporelle, Variation long terme, Variation temporelle.
- Wicri :
- geographic : Allemagne.
- topic : Ozone, Santé publique.
English descriptors
- KwdEn :
- Berlin, Century 21st, Germany, Ground based measurement, Long term, Long term variation, Meteorological variable, Ozone layer, Public health, Solar zenithal distance, Sunshine duration, Time series, Total ozone content, concentration, frame structure, models, neural networks, observatories, ozone, recovery, stratosphere, time variations, ultraviolet rays.
Abstract
Solar erythemal UV radiation (UVER) is highly relevant for numerous biological processes that affect plants, animals, and human health. Nevertheless, long‐term UVER records are scarce. As significant declines in the column ozone concentration were observed in the past and a recovery of the stratospheric ozone layer is anticipated by the middle of the 21st century, there is a strong interest in the temporal variation of UVERtime series. Therefore, we combined ground‐based measurements of different meteorological variables with modeled ozone data sets to reconstruct time series of daily totals of UVER at the Meteorological Observatory, Potsdam, Germany. Artificial neural networks were trained with measured UVER, sunshine duration, the day of year, measured and modeled total column ozone, as well as the minimum solar zenith angle. This allows for the reconstruction of daily totals of UVERfor the period from 1901 to 1999. Additionally, analyses of the long‐term variations from 1901 until 1999 of the reconstructed, new UVER data set are presented. The time series of monthly and annual totals of UVERprovide a long‐term meteorological basis for epidemiological investigations in human health and occupational medicine for the region of Potsdam and Berlin. A strong benefit of our ANN‐approach is the fact that it can be easily adapted to different geographical locations, as successfully tested in the framework of the COSTAction 726.
Url:
DOI: 10.1029/2012JD017659
Affiliations:
- Allemagne, Luxembourg (pays), Pologne, Suisse
- Rhénanie-Palatinat
- Trèves (Allemagne)
- Université de Trèves
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Le document en format XML
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Nevertheless, long‐term UVER records are scarce. As significant declines in the column ozone concentration were observed in the past and a recovery of the stratospheric ozone layer is anticipated by the middle of the 21st century, there is a strong interest in the temporal variation of UVERtime series. Therefore, we combined ground‐based measurements of different meteorological variables with modeled ozone data sets to reconstruct time series of daily totals of UVER at the Meteorological Observatory, Potsdam, Germany. Artificial neural networks were trained with measured UVER, sunshine duration, the day of year, measured and modeled total column ozone, as well as the minimum solar zenith angle. This allows for the reconstruction of daily totals of UVERfor the period from 1901 to 1999. Additionally, analyses of the long‐term variations from 1901 until 1999 of the reconstructed, new UVER data set are presented. The time series of monthly and annual totals of UVERprovide a long‐term meteorological basis for epidemiological investigations in human health and occupational medicine for the region of Potsdam and Berlin. A strong benefit of our ANN‐approach is the fact that it can be easily adapted to different geographical locations, as successfully tested in the framework of the COSTAction 726.</div></front></TEI><affiliations><list><country><li>Allemagne</li><li>Luxembourg (pays)</li><li>Pologne</li><li>Suisse</li></country><region><li>Rhénanie-Palatinat</li></region><settlement><li>Trèves (Allemagne)</li></settlement><orgName><li>Université de Trèves</li></orgName></list><tree><country name="Luxembourg (pays)"><noRegion><name sortKey="Junk, J" sort="Junk, J" uniqKey="Junk J" first="J." last="Junk">J. Junk</name></noRegion><name sortKey="Gorgen, K" sort="Gorgen, K" uniqKey="Gorgen K" first="K." last="Görgen">K. Görgen</name><name sortKey="Hoffmann, L" sort="Hoffmann, L" uniqKey="Hoffmann L" first="L." last="Hoffmann">L. Hoffmann</name><name sortKey="Junk, J" sort="Junk, J" uniqKey="Junk J" first="J." last="Junk">J. Junk</name></country><country name="Allemagne"><noRegion><name sortKey="Feister, U" sort="Feister, U" uniqKey="Feister U" first="U." last="Feister">U. Feister</name></noRegion><name sortKey="Helbig, A" sort="Helbig, A" uniqKey="Helbig A" first="A." last="Helbig">A. Helbig</name></country><country name="Suisse"><noRegion><name sortKey="Rozanov, E" sort="Rozanov, E" uniqKey="Rozanov E" first="E." last="Rozanov">E. Rozanov</name></noRegion><name sortKey="Rozanov, E" sort="Rozanov, E" uniqKey="Rozanov E" first="E." last="Rozanov">E. Rozanov</name></country><country name="Pologne"><noRegion><name sortKey="Krzy Cin, J W" sort="Krzy Cin, J W" uniqKey="Krzy Cin J" first="J. W." last="Krzy Cin">J. W. 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