Toward understanding of differences in current cloud retrievals of ARM ground-based measurements
Zhao, C., Xie, S., Klein, S., Protat, A., Shupe, M., McFarlane, S. , Comstock, J., Delanoë, J. , Deng, M., Dunn, M. , Hogan, R. J., Huang, D., Jensen, M. , Mace, G., McCoy, R., O'Connor, E. J., Turner, D. and Wang, Z. (2012) Toward understanding of differences in current cloud retrievals of ARM ground-based measurements. Journal of Geophysical Research - Atmospheres, 117. D10206. ISSN 0148-0227
To link to this article DOI: 10.1029/2011JD016792
Accurate observations of cloud microphysical properties are needed for evaluating and improving the representation of cloud processes in climate models and better estimate of the Earth radiative budget. However, large differences are found in current cloud products retrieved from ground-based remote sensing measurements using various retrieval algorithms. Understanding the differences is an important step to address uncertainties in the cloud retrievals. In this study, an in-depth analysis of nine existing ground-based cloud retrievals using ARM remote sensing measurements is carried out. We place emphasis on boundary layer overcast clouds and high level ice clouds, which are the focus of many current retrieval development efforts due to their radiative importance and relatively simple structure. Large systematic discrepancies in cloud microphysical properties are found in these two types of clouds among the nine cloud retrieval products, particularly for the cloud liquid and ice particle effective radius. Note that the differences among some retrieval products are even larger than the prescribed uncertainties reported by the retrieval algorithm developers. It is shown that most of these large differences have their roots in the retrieval theoretical bases, assumptions, as well as input and constraint parameters. This study suggests the need to further validate current retrieval theories and assumptions and even the development of new retrieval algorithms with more observations under different cloud regimes.
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