Actions for Determination of Ice Cloud Models Using MODIS and MISR Data

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Determination of Ice Cloud Models Using MODIS and MISR Data

Author
Hu, Yongxiang
Published
July 10, 2012.
Physical Description
1 electronic document
Additional Creators
Kattawar, George W., Wu, Dong L., Xie, Yu, Yang, Ping, and Minnis, Patrick
Online Version

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Summary
Representation of ice clouds in radiative transfer simulations is subject to uncertainties associated with the shapes and sizes of ice crystals within cirrus clouds. In this study, we examined several ice cloud models consisting of smooth, roughened, homogeneous and inhomogeneous hexagonal ice crystals with various aspect ratios. The sensitivity of the bulk scattering properties and solar reflectances of cirrus clouds to specific ice cloud models is investigated using the improved geometric optics method (IGOM) and the discrete ordinates radiative transfer (DISORT) model. The ice crystal habit fractions in the ice cloud model may significantly affect the simulations of cloud reflectances. A new algorithm was developed to help determine an appropriate ice cloud model for application to the satellite-based retrieval of ice cloud properties. The ice cloud particle size retrieved from Moderate Resolution Imaging Spectroradiometer (MODIS) data, collocated with Multi-angle Imaging Spectroradiometer (MISR) observations, is used to infer the optical thicknesses of ice clouds for nine MISR viewing angles. The relative differences between view-dependent cloud optical thickness and the averaged value over the nine MISR viewing angles can vary from 0.5 to 0.5 and are used to evaluate the ice cloud models. In the case for 2 July 2009, the ice cloud model with mixed ice crystal habits is the best fit to the observations (the root mean square (RMS) error of cloud optical thickness reaches 0.365). This ice cloud model also produces consistent cloud property retrievals for the nine MISR viewing configurations within the measurement uncertainties.
Other Subject(s)
Collection
NASA Technical Reports Server (NTRS) Collection.
Note
Document ID: 20140011049.
GSFC-E-DAA-TN9683.
International Journal of Remote Sensing(ISSN 0143-1161); Volume 33; Issue 13; 4219-4253.
Terms of Use and Reproduction
Copyright, Distribution as joint owner in the copyright.
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