ACP Atmospheric Chemistry and Physics ACP 1680-7324 Copernicus GmbH Göttingen, Germany 10.5194/acp-10-11851-2010 Cloud thermodynamic phase inferred from merged POLDER and MODIS data Riedi J. 1 Marchant B. 1 Platnick S. 2 Baum B. A. 3 Thieuleux F. 1 Oudard C. 1 Parol F. 1 Nicolas J.-M. 4 Dubuisson P. 1 Laboratoire d'Optique Atmosphérique, UMR 8518, Université de Lille 1 - Sciences et Technologies, CNRS, France NASA Goddard Space Flight Center, MD, USA SSEC, University of Wisconsin-Madison, WI, USA ICARE Data and Services Center, Université des Sciences et Technologies de Lille, France 13 12 2010 10 23 11851 11865 This is an open-access article ditributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. This article is available from http://www.atmos-chem-phys.net/10/11851/2010/acp-10-11851-2010.html The full text article is available as a PDF file from http://www.atmos-chem-phys.net/10/11851/2010/acp-10-11851-2010.pdf

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). <br><br> 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. <br><br> 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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