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| Titel |
Cloud thermodynamic phase inferred from merged POLDER and MODIS data |
| VerfasserIn |
J. Riedi, B. Marchant, S. Platnick, B. A. Baum, F. Thieuleux, C. Oudard, F. Parol, J.-M. Nicolas, P. Dubuisson |
| 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 ; 10, no. 23 ; Nr. 10, no. 23 (2010-12-13), S.11851-11865 |
| Datensatznummer |
250008957
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| Publikation (Nr.) |
 copernicus.org/acp-10-11851-2010.pdf |
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| Zusammenfassung |
The global spatial and diurnal distribution of cloud properties is a key issue for understanding
the hydrological cycle, and critical for advancing efforts to improve numerical weather models
and general circulation models. Satellite data provides the best way of gaining insight into global
cloud properties. In particular, the determination of cloud thermodynamic phase is a critical first
step in the process of inferring cloud optical and microphysical properties from satellite
measurements. It is important that cloud phase be derived together with an estimate of the
confidence of this determination, so that this information can be included with subsequent retrievals
(optical thickness, effective particle radius, and ice/liquid water content).
In this study, we combine three different and well documented approaches for
inferring cloud phase into a single algorithm. The algorithm is applied to data
obtained by the MODIS (MODerate resolution Imaging Spectroradiometer) and POLDER3
(Polarization and Directionality of the Earth Reflectance) instruments. It is
shown that this synergistic algorithm can be used routinely to derive cloud
phase along with an index that helps to discriminate ambiguous phase from
confident phase cases.
The resulting product provides a semi-continuous index ranging from confident
liquid to confident ice instead of the usual discrete classification of liquid
phase, ice phase, mixed phase (potential combination of ice and liquid particles),
or simply unknown phase clouds. The index value provides simultaneously information
on the phase and the associated confidence. This approach is expected to be useful for
cloud assimilation and modeling efforts while providing more insight into the global cloud
properties derived from satellite data. |
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