Parameterizing Size Distribution in Ice Clouds Page: 4 of 89
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temperatures, recent reliable in situ PSD measurements (when converted to optical
properties) indicate the mean p should not exceed this value for the ice PSDs (Jensen
et al. 2009). But when liquid water is present, p is predicted to exceed this value due to
the relatively small droplet sizes and the difference in the imaginary part of the refractive
index for water between 11 and 12 um wavelengths. Therefore, when p retrieved by
ground- or space-based remote sensing significantly exceeds this value for cirrus
clouds, it indicates the presence of cloud liquid water.
An algorithm has been designed to exploit this principle for AERI (Atmospheric
Emitted Radiance Interferometer) retrievals and is described in the peer reviewed paper
in Appendix D. Appendix D illustrates the retrieval for a cirrus cloud sampled during M-
PACE, showing how the AERI retrieval interprets the cloud to be mostly ice but with a
few patches of liquid water (Fig. 3). Radar depolarization ratios confirmed that the cloud
is mostly all ice. The AERI instrument measures the total water path (TWP) regardless
of phase. But using the above p principle, the liquid water fraction can also be retrieved
from AERI as shown in Fig. 3.
Also shown in Fig. 3 of Appendix D is the retrieved ice water path (IWP) in the
same cloud based on the MMCR radar. This was done by employing the radar-IWC
equation described in Mitchell et al. (2006). It is seen that for this Arctic cirrus case
study, the IWP from the MMCR radar generally matches the TWP retrieved from the
AERI in most cases. This partially validates the TWP retrieval from the AERI since we
know the cloud is almost all ice.
In spite of these encouraging results, there is a drawback to this technique. The
AERI retrieval for p depends on Dr. Dave Turner's radiation transfer model which
includes multiple scattering for greater accuracy. This RT model combines with a
retrieval algorithm developed by Dr. Turner's group that estimates p for our
microphysics algorithm. Unfortunately some of the assumptions made in the p retrieval
(such as an assumed effective diameter) may add a relatively high level of uncertainty
to our microphysical mixed phase cloud retrievals. For this reason, we turned to a
satellite method that appears to be free of these complicating factors.
b. Satellite retrievals of mixed phase cloud composition
The AERI mixed phase cloud retrieval described above can be easily adapted to
satellite remote sensing. While not part of the work plan of our new ARM project, it
would be a missed opportunity not to apply our mixed phase microphysics retrieval
algorithm from the last ARM project to satellite remote sensing. To date we know of no
technique that retrieves the ice and liquid water fraction of mixed phase clouds from
space. Thus the work described in this section was completed during our current ARM
project (start date 15 March 2009) and exploits advances made during the last project
(the subject of this report).
Accurate prediction of the feedback from clouds in global climate models (GCMs)
requires the partitioning of ice and liquid water in clouds. Whether this is done by
parameterizing measurements or physical processes, accurate measurements of the
ice/liquid fraction will be needed. Since this fraction may differ based on many factors
including cloud type and latitude, global measurements from satellites may be
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DeSlover, Daniel & Mitchell, David L. Parameterizing Size Distribution in Ice Clouds, report, September 25, 2009; Madison, Wisconsin. (digital.library.unt.edu/ark:/67531/metadc925707/m1/4/: accessed September 23, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.