A suite of radarlidarradiometer cirrus retrieval algorithms for the combined cloudsat, calipso, and

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A suite of radar-lidar-radiometer cirrus
retrieval algorithms for the combined cloudsat,
calipso, and aqua datastreams development and
initial results

• Yuying Zhang, Jay Mace (University of Utah), Ping
  Yang (Texas AM University)

Data provided by Gerry Heymsfield, Matt McGill,
Andy Heymsfield
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Cloud observations in the near future
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II. Retrieval algorithms
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II. Retrieval algorithms

• 2. Forward model

Qabs fitted Yang et al. 2003
empirical relation
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II. Retrieval algorithms

• 3. Optimal estimation framework (Rodgers, 1976
  Rodgers, 2002)

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III. Sensitivity





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III. Sensitivity

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III. Sensitivity
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IV. Case Study

• CRYSTAL-FACE July 26 2002

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Courtesy G.Heymsfield
Courtesy G.Heymsfield M. McGill
Courtesy G.Heymsfield S. Platnick
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Avalone et al.
Weinstock et al. 2002
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MOD06 Cloud Products compare with
lidar-Radiometer retrievals
MOD06 (King et al. 1997) retrieval
Optical thickness
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IV. Summary

• A major advantage of the A-Train use multiple
  data streams for cloud property retrieval
• Goal develop an algorithm suite to exploit
  this resource
• From IWC comparison with WB57, the
  lidar-radiometer algorithm is able to retrieve
  reliable microphysical properties
• Retrieval algorithms are sensitive to empirical
  constants
• In future, use radar and lidar profiles to
  retrieve vertical structure of cirrus clouds

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Aqua MODIS

• ? Passive radiometer
• scattered and emitted radiance
• ? Integral constraint
• ? Algorithm development
• Radiance ? emissivity
• CO2 channel (Wylie and Menzel, 1989 Wylie et
  al., 1994 Liou, 2002)

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CloudSat Radar

• ? Millimeter radar
• ? Vertical profile of Ze

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CALIPSO

• Optical lidar
• Vertical profile of
• attenuated backscatter

Height (km)

• Lidar signal ? cloud layer transmissivity
• (Mitrescu and Stephens, 2002 Young 1995)

Lidar signal