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| Titel |
Relations between erythemal UV dose, global solar radiation, total ozone column and aerosol optical depth at Uccle, Belgium |
| VerfasserIn |
V. De Bock, H. De Backer, R. Van Malderen, A. Mangold, A. Delcloo |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1680-7316
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| Digitales Dokument |
URL |
| Erschienen |
In: Atmospheric Chemistry and Physics ; 14, no. 22 ; Nr. 14, no. 22 (2014-11-20), S.12251-12270 |
| Datensatznummer |
250119178
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| Publikation (Nr.) |
 copernicus.org/acp-14-12251-2014.pdf |
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| Zusammenfassung |
| At Uccle, Belgium, a long time series (1991–2013) of simultaneous measurements of
erythemal ultraviolet (UV) dose (Sery), global solar radiation
(Sg), total ozone column (Q_{O3}$) and aerosol optical
depth (τaer) (at 320.1 nm) is available, which allows for an
extensive study of the changes in the variables over time. Linear trends were
determined for the different monthly anomalies time series. Sery,
Sg and QO3 all increase by respectively 7, 4 and
3% per decade. τaer shows an insignificant negative trend
of −8% per decade. These trends agree with results found in the literature
for sites with comparable latitudes. A change-point analysis, which
determines whether there is a significant change in the mean of the time
series, is applied to the monthly anomalies time series of the variables.
Only for Sery and QO3, was a significant change point
present in the time series around February 1998 and March 1998,
respectively. The change point in QO3 corresponds with results
found in the literature, where the change in ozone levels around 1997 is
attributed to the recovery of ozone. A multiple linear regression (MLR)
analysis is applied to the data in order to study the influence of Sg,
QO3 and τaer on Sery. Together
these parameters are able to explain 94% of the variation in
Sery. Most of the variation (56%) in Sery is
explained by Sg. The regression model performs well, with a slight
tendency to underestimate the measured Sery values and with a
mean absolute bias error (MABE) of 18%. However, in winter, negative
Sery are modeled. Applying the MLR to the individual seasons
solves this issue. The seasonal models have an adjusted R2 value higher
than 0.8 and the correlation between modeled and measured Sery
values is higher than 0.9 for each season. The summer model gives the best
performance, with an absolute mean error of only 6%. However, the
seasonal regression models do not always represent reality, where an increase
in Sery is accompanied with an increase in QO3 and a
decrease in τaer. In all seasonal models, Sg is the
factor that contributes the most to the variation in Sery, so
there is no doubt about the necessity to include this factor in the
regression models. The individual contribution of τaer to
Sery is very low, and for this reason it seems unnecessary to
include τaer in the MLR analysis. Including QO3,
however, is justified to increase the adjusted R2 and to decrease the
MABE of the model. |
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