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| Titel |
Estimate of the atmospheric turbidity from three broad-band solar radiation algorithms. A comparative study |
| VerfasserIn |
G. López, F. J. Batlles |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
0992-7689
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| Digitales Dokument |
URL |
| Erschienen |
In: Annales Geophysicae ; 22, no. 8 ; Nr. 22, no. 8 (2004-09-07), S.2657-2668 |
| Datensatznummer |
250014954
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| Publikation (Nr.) |
 copernicus.org/angeo-22-2657-2004.pdf |
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| Zusammenfassung |
| Atmospheric turbidity is an important parameter for assessing the air
pollution in local areas, as well as being the main parameter controlling the
attenuation of solar radiation reaching the Earth's surface under cloudless
sky conditions. Among the different turbidity indices, the Ångström
turbidity coefficient β is frequently used. In this work, we analyse the
performance of three methods based on broad-band solar irradiance
measurements in the estimation of β. The evaluation of the performance of the models was undertaken by graphical and statistical (root mean square errors
and mean bias errors) means. The data sets used in this study comprise
measurements of broad-band solar irradiance obtained at eight radiometric
stations and aerosol optical thickness measurements obtained at one
co-located radiometric station. Since all three methods require estimates of
precipitable water content, three common methods for calculating atmospheric
precipitable water content from surface air temperature and relative
humidity are evaluated. Results show that these methods exhibit significant
differences for low values of precipitable water. The effect of these
differences in precipitable water estimates on turbidity algorithms is
discussed. Differences in hourly turbidity estimates are later examined. The
effects of random errors in pyranometer measurements and cloud interferences
on the performance of the models are also presented. Examination of the
annual cycle of monthly mean values of β for each location has shown that all three turbidity algorithms are suitable for analysing long-term trends and
seasonal patterns. |
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