ICE CLOUD PROPERTIES FROM THE A-TRAIN

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ICE CLOUD PROPERTIES FROM THE A-TRAIN
CloudSat Meeting- Seattle 2008

• Julien Delanoë Robin Hogan
• University of Reading, UK
• Thanks to
• Richard Forbes (ECMWF)
• Alejandro Bodas-Salcedo (Met-Office)

CloudSat Meeting August 19-21 2008
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Remote sensing synergy for Clouds
CloudSat Meeting- Seattle 2008
A-Train 28th April 2006

• CloudSat Cloud profiler radar 94GHz
• CALIPSO Cloud profiler lidar 532, 1064nm Infra
  Red Imager
• AQUA radiometers MODIS, AIRS, CERES, AMSR-E

• An opportunity to tackle questions concerning
  role of clouds in climate
• Need to combine all these observations to get an
  optimum estimate of global cloud properties

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CloudSat Meeting- Seattle 2008
An algorithm to combine radar, lidar, radiometers

• Why combine radar, lidar and radiometers?
• Radar Z?D6, lidar b?D2 so the combination
  provides particle size
• Radiances ensure that the retrieved profiles can
  be used for radiative transfer studies
• Single channel information on extinction near
  cloud top
• Pair of channels ice particle size information
  near cloud top
• Some limitations of existing radar/lidar ice
  retrieval schemes (Donovan et al. 2000, Tinel et
  al. 2005, Mitrescu et al. 2005)
• Only work in regions of cloud detected by both
  radar and lidar
• Difficult to make use of other measurements, e.g.
  passive radiances
• A unified variational scheme can solve all of
  these problems

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Formulation of variational scheme
CloudSat Meeting- Seattle 2008
We know the observations (instrument
measurements) and we would like to know cloud
properties visible extinction, Ice water
content, effective radius

• Observation vector State vector (which
  we want to retrieve)
• Elements may be missing

Iterative process compare predicted observations
and measurements, with an a-priori and
measurement errors as a constraint Delanoë and
Hogan 2008, JGR (doi10.1029/2007JD009000)
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CloudSat Meeting- Seattle 2008
Ice cloud properties from A-TRAINCloudSat-CALIPS
OCase study
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CloudSat Meeting- Seattle 2008
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CloudSat Meeting- Seattle 2008
Ice cloud properties from A-TRAINCloudSat-CALIPS
OStatistics one month data July 2006Ice water
content and optical thickness
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CloudSat Meeting- Seattle 2008
Frequency of occurrence of IWC vs temperature

• IWC increases with temperature
• but spread over 2 to 3 orders of magnitude at
  low temperatures
• reach 5 orders of magnitude close to 0 C

• Advantage of the algorithm
• Deep ice clouds radar
• Thin ice clouds lidar
• When radar and lidar work well together very good
  confidence in the retrievals
• Obvious complementarity
• radar-lidar

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CloudSat Meeting- Seattle 2008
Differences between Hemispheres July 2006
No obvious differences in the general trend IWC
shifted to low temperatures in southern
hemisphere
Temperature C
Temperature C
Boreal Summer
Austral Winter
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Comparison of ice water path
CloudSat Meeting- Seattle 2008

• Mean of all skies
• Mean of clouds

CloudSat-CALIPSO
MODIS
Need longer period than just one month (July
2006) to obtain adequate statistics from poorer
sampling of radar and lidar
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Comparison of optical depth
CloudSat Meeting- Seattle 2008

• Mean of all skies
• Mean of clouds

CloudSat-CALIPSO
MODIS
Mean optical depth from CloudSat-CALIPSO is lower
than MODIS simply because CALIPSO detected many
more optically thin clouds not seen by MODIS gt
need to compare PDFs as well
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CloudSat Meeting- Seattle 2008
Ice cloud properties from A-TRAINCloudSat-CALIPS
OModel comparisons (July 2006)UK
Met-OfficeECMWF
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CloudSat Meeting- Seattle 2008
Forecasts Vertical profiles were extracted from
the model along the CloudSat-Calipso tracks at
the closest time to the observations A-train
data averaged to models grid
Good trend but cant capture variability
lack of IWC _at_ Tlt -50C
gt Models capture the trend of the IWC-T
distribution (not the rest)
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CloudSat Meeting- Seattle 2008
Ice cloud properties from A-TRAINCloudSat-CALIPS
OModel comparison (July 2006)UK
Met-OfficeDoes the model capture the latitude
characteristics?
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CloudSat Meeting- Seattle 2008
A-Train vs UK met-Office
Northern Hemisphere
Southern Hemisphere
A-Train
A-Train
UK met-Office
UK met-Office
Temperature C
log10(IWC)
log10(IWC)
log10(IWC)
log10(IWC)
Ok for the trend but not the variability
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Conclusion /Future work
CloudSat Meeting- Seattle 2008

• Conclusion
• New variational scheme, combining radar, lidar,
  radiometer and/or any other relevant measurement
  in order to retrieve ice cloud properties
  profiles
• Develop ice cloud climatology and use to evaluate
  forecast/climate models
• Future work
• More than one month
• Develop algorithms for ESAs EarthCARE
  satellite, HSRL lidar and Doppler radar
• Retrieve properties of liquid-water layers,
  drizzle and aerosol
• Incorporate microwave radiances for deep
  precipitating clouds
• A-train data and validate using in-situ
  underflights
• Acknowledgements
• These data were obtained from the NASA Langley
  Research Center Atmospheric Science Data Center
  and the NASA CloudSat project.

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Attenuated lidar backscatter from CALIPSO
Radar Reflectivity from CloudSat
We have developed a simple cloud phase
identification algorithm
Cloud seen by lidar
Temperature model (ECMWF) gt Ice / Liquid
water Simple method Exploit the different
response of radar and lidar in presence of
supercooled liquid water -Very strong lidar
signal -Very weak radar signal Within a 300m
cloud layer
Cloud seen by radar
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Northern hemisphere
July 2006
Southern hemisphere
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CloudSat Meeting- Seattle 2008
Maps of ice water path (IWP) and ice visible
optical depth (t) for July 2006
Small amount of ice clouds between 0S and 30S,
in the descending branch of the Hadley
circulation Much larger in the Inter Tropical
Convergence Zone (ICTZ)
Northern/Southern middle latitudes t is much
higher in southern hemisphere (austral winter)