The effect of the surface roughness of ice crystals is not routinely accounted for in
current cloud retrieval algorithms that are based on pre-computed lookup libraries. In this
study, we investigate the effect of ice crystal surface roughness on the retrieval of ice
cloud effective particle size, optical thickness and cloud-top temperature. Three particle
surface conditions, smooth, moderately rough and deeply rough, are considered in the
visible and near-infrared channels (0.65 and 3.75 õm). The discrete ordinates radiative
transfer (DISORT) model is used to compute the radiances for a set of optical
thicknesses, particle effective sizes, viewing and illumination angles, and cloud
temperatures. A parameterization of cloud bi-directional reflectances and effective
emittances is then developed from a variety of particle surface conditions. This
parameterization is applied in a 3-channel retrieval method for Moderate Resolution
Imaging Spectroradiometer (MODIS) data at 0.65, 3.75, and 10.8 õm. Cloud optical
properties are derived iteratively for each pixel that contains ice clouds. The impact of ice
crystal surface roughness on the cloud parameter retrievals is examined by comparing the
results for particles with smooth surfaces and rough surfaces. Retrieval results from two
granules of MODIS data indicate that the retrieved cloud optical thickness is significantly reduced if the parameterization for roughened particles is used, as compared with the case
of smooth particles. For the retrieval of cloud effective particle size, the inclusion of the
effect of surface roughness tends to decrease the retrieved effective particle size if ice
crystals are small. The reversed result is noticed for large ice crystals. It is also found that
surface roughness has a very minor effect on the retrieval of cloud-top temperatures.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/5970 |
| Date | 17 September 2007 |
| Creators | Xie, Yu |
| Contributors | North, Gerald, Yang, Ping |
| Publisher | Texas A&M University |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Book, Thesis, Electronic Thesis, text |
| Format | 27229287 bytes, electronic, application/pdf, born digital |
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