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| Titel |
Improved retrieval of nitrogen dioxide (NO2) column densities by means of MKIV Brewer spectrophotometers |
| VerfasserIn |
H. Diémoz, A. M. Siani, A. Redondas, V. Savastiouk, C. T. McElroy, M. Navarro-Comas, F. Hase |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1867-1381
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| Digitales Dokument |
URL |
| Erschienen |
In: Atmospheric Measurement Techniques ; 7, no. 11 ; Nr. 7, no. 11 (2014-11-28), S.4009-4022 |
| Datensatznummer |
250115968
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| Publikation (Nr.) |
 copernicus.org/amt-7-4009-2014.pdf |
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| Zusammenfassung |
| A new algorithm to retrieve nitrogen dioxide (NO2) column densities
using MKIV ("Mark IV") Brewer spectrophotometers is described. The method includes
several improvements, such as a more recent spectroscopic data set, the
reduction of measurement noise, interference by other
atmospheric species and instrumental settings, and a better
determination of the zenith sky air mass factor. The technique was
tested during an ad hoc calibration campaign at the high-altitude site
of Izaña (Tenerife, Spain) and the results of the direct sun and zenith
sky geometries were compared to those obtained by two reference instruments
from the Network for the Detection of Atmospheric Composition Change (NDACC):
a Fourier Transform Infrared Radiometer (FTIR) and an
advanced visible spectrograph (RASAS-II) based on the differential optical
absorption spectrometry (DOAS) technique.
To determine the extraterrestrial constant, an
easily implementable extension of the standard Langley technique
for very clean sites without tropospheric NO2
was developed which takes into account the daytime linear drift of
stratospheric nitrogen dioxide due to photochemistry.
The measurement uncertainty was thoroughly determined by using
a Monte Carlo technique. Poisson noise and wavelength misalignments
were found to be the most influential contributors to the overall
uncertainty, and possible solutions are proposed for future improvements.
The new algorithm is backward-compatible, thus allowing for the reprocessing
of historical data sets. |
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