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| Titel |
Study of global cloud droplet number concentration with A-Train satellites |
| VerfasserIn |
S. Zeng, J. Riedi, C. R. Trepte, D. M. Winker, Y.-X. Hu |
| 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. 14 ; Nr. 14, no. 14 (2014-07-16), S.7125-7134 |
| Datensatznummer |
250118884
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| Publikation (Nr.) |
 copernicus.org/acp-14-7125-2014.pdf |
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| Zusammenfassung |
Cloud droplet number concentration (CDNC) is an important microphysical
property of liquid clouds that impacts radiative forcing, precipitation and
is pivotal for understanding cloud–aerosol interactions. Current studies
of this parameter at global scales with satellite observations are still
challenging, especially because retrieval algorithms developed for passive
sensors (i.e., MODerate Resolution Imaging Spectroradiometer (MODIS)/Aqua)
have to rely on the assumption of cloud adiabatic growth. The active sensor
component of the A-Train constellation (i.e., Cloud-Aerosol Lidar with
Orthogonal Polarization (CALIOP)/CALIPSO) allows retrievals of CDNC from
depolarization measurements at 532 nm. For such a case, the retrieval does not
rely on the adiabatic assumption but instead must use a priori information
on effective radius (re), which can be obtained from other passive
sensors.
In this paper, re values obtained from MODIS/Aqua and Polarization and
Directionality of the Earth Reflectance (POLDER)/PARASOL (two passive
sensors, components of the A-Train) are used to constrain CDNC retrievals
from CALIOP. Intercomparison of CDNC products retrieved from MODIS and
CALIOP sensors is performed, and the impacts of cloud entrainment,
drizzling, horizontal heterogeneity and effective radius are discussed. By
analyzing the strengths and weaknesses of different retrieval techniques,
this study aims to better understand global CDNC distribution and
eventually determine cloud structure and atmospheric conditions in which
they develop. The improved understanding of CDNC can contribute to future
studies of global cloud–aerosol–precipitation interaction and
parameterization of clouds in global climate models (GCMs). |
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