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• W80228477 abstract "Summary and Future Work In this intercomparison of 13 different retrieval algorithms for upper tropospheric ice clouds, we examined several different types of algorithms that utilize different combinations of remote sensors. We grouped the results according to algorithms that use similar assumptions, and found that three optical depth regions best identify the strengths and weaknesses of each class of algorithm. The good news in the findings is that most algorithms converge for thin clouds with optical depth between 0.1 and 1.0, which constitutes the majority of midlatitude cirrus cloud observations (Sassen and Comstock 2001). However, there are significant discrepancies in very thin clouds (τ<0.1). These clouds will not have a significant impact on the radiation budget in midlatitude regions, but can produce significant warming in the cold upper troposphere typically found in the tropics. In general, the IWP comparisons show good agreement and typically vary by a factor of 2 to 5. The ZV algorithms are the only techniques that can consistently retrieve properties from thick clouds and has the added benefit of providing results when multiple cloud layers exist and low opaque clouds block high cirrus clouds. However, there is still some work that needs to be done to supply the correct coefficients for ice crystal fall speed estimates. Further in situ measurements will help to reduce the uncertainty in these coefficients. In this preliminary study, we did not attempt to identify the “best” algorithm. This is the subject of future work that will involve both flux and radiance closure studies for various cloud thicknesses. This will be difficult for ice clouds because most algorithms are sensitive to assumptions of particle habit, which will impact radiative transfer simulations. Radiative flux closure is also problematic due to 3D effects, which can increase uncertainty caused by the inherent inhomogeneity of cirrus clouds. Another option is to perform radiance closure using a narrow field-of-view instrument to minimize the 3D effects. We will also compare with any in situ data that coincides with our chosen cases. Eventually we will need to examine a larger statistical sample for the algorithms that are identified as having the most potential. One approach to evaluating how well an algorithm performs is to compare results with a simple algorithm, such as an empirical relationship. By using a large sample size, if the more complex algorithm performs better than the empirical retrieval, then it likely has some skill in retrieving cloud microphysical properties." @default.
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• W80228477 date "2004-01-01" @default.
• W80228477 modified "2023-09-27" @default.
• W80228477 title "High Clouds Microphysical Retrievals Intercomparison" @default.
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