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| Titel |
A global aerosol classification algorithm incorporating multiple satellite data sets of aerosol and trace gas abundances |
| VerfasserIn |
M. J. M. Penning de Vries, S. Beirle, C. Hörmann, J. W. Kaiser, P. Stammes, L. G. Tilstra, O. N. E. Tuinder, T. Wagner |
| 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 ; 15, no. 18 ; Nr. 15, no. 18 (2015-09-25), S.10597-10618 |
| Datensatznummer |
250120052
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| Publikation (Nr.) |
 copernicus.org/acp-15-10597-2015.pdf |
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| Zusammenfassung |
| Detecting the optical properties of aerosols using
passive satellite-borne measurements alone is
a difficult task due to the broadband effect of
aerosols on the measured spectra and the influences of
surface and cloud reflection. We present another
approach to determine aerosol type, namely by studying
the relationship of aerosol optical depth (AOD) with
trace gas abundance, aerosol absorption, and mean
aerosol size. Our new Global Aerosol Classification
Algorithm, GACA, examines relationships between aerosol
properties (AOD and extinction Ångström exponent from
the Moderate Resolution Imaging Spectroradiometer
(MODIS), UV Aerosol Index from the second Global Ozone
Monitoring Experiment, GOME-2) and trace gas column
densities (NO2, HCHO, SO2 from
GOME-2, and CO from MOPITT, the Measurements of
Pollution in the Troposphere instrument) on a monthly
mean basis. First, aerosol types are separated based on
size (Ångström exponent) and absorption (UV Aerosol
Index), then the dominating sources are identified
based on mean trace gas columns and their correlation
with AOD. In this way, global maps of dominant aerosol
type and main source type are constructed for each
season and compared with maps of aerosol composition
from the global MACC (Monitoring Atmospheric
Composition and Climate) model. Although GACA cannot
correctly characterize transported or mixed aerosols,
GACA and MACC show good agreement regarding the global
seasonal cycle, particularly for urban/industrial
aerosols. The seasonal cycles of both aerosol type and
source are also studied in more detail for selected
5° × 5° regions. Again, good
agreement between GACA and MACC is found for all
regions, but some systematic differences become
apparent: the variability of aerosol composition
(yearly and/or seasonal) is often not well captured by
MACC, the amount of mineral dust outside of the dust
belt appears to be overestimated, and the abundance of
secondary organic aerosols is underestimated in
comparison with GACA. Whereas the presented study is
of exploratory nature, we show that the developed
algorithm is well suited to evaluate climate and
atmospheric composition models by including aerosol
type and source obtained from measurements into the
comparison, instead of focusing on a single parameter,
e.g., AOD. The approach could be adapted to constrain
the mix of aerosol types during the process of
a combined data assimilation of aerosol and trace gas
observations. |
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