Robin Hogan

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Approaches for variational liquid-cloud
retrievals using radar, lidar and radiometers

• Robin Hogan
• Julien Delanoë
• Nicola Pounder
• Chris Westbrook
• University of Reading

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The drizzle problem
Drizzle dominates Z
Liquid cloud dominates Z

• Maritime airmasses
• x Continental airmasses

Fox and Illingworth (1997)
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What other obs can be exploited?

• From space no single instrument provides water
  content and size
• Need synergy of multiple instruments, for example
  from space
• Solar radiances provide optical depth and
  near-cloud-top mean radius
• Surface radar return from the oceans provides LWP
• High spectral resolution lidar provides
  extinction at cloud top
• Multiple FOV lidar provides extinction profile
  (in principle)
• Rate of increase of depolarization related to
  cloud-top extinction via multiple scattering
• Very difficult to estimate cloud base height
• from the ground
• Zenith-pointing sun photometer for optical depth
• Multi-wavelength microwave radiometer for LWP
• Radar Doppler spectra for liquid clouds embedded
  in drizzle or ice
• AERI infrared spectrometer
• Dual-wavelength radar for LWC profile
• Can be difficult to identify multiple layers

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Dual-wavelength radar for LWC

• Radar reflectivity factor dominated by drizzle
• Lidar sees cloud base
• Dual-wavelength ratio
• DWRdB dBZ35 dBZ94
• Increases with range due to liquid attenuation
• Derivative provides LWC
• For radiative studies and model evaluation, how
  important is the vertical structure?
• Is the Cloudnet scaled adiabatic method good
  enough?

Hogan et al. (2005)
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Examples of multiple scattering
LITE lidar (lltr, footprint1 km) Cloud
Sat radar (lgtr)
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Fast multiple scattering fwd model
Hogan and Battaglia (J. Atmos. Sci. 2008)

• New method uses the time-dependent two-stream
  approximation
• Agrees with Monte Carlo but 107 times faster (3
  ms)
• Added to CloudSat simulator

CloudSat-like example
CALIPSO-like example
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Multiple FOV lidar retrieval

• To test multiple scattering model in a retrieval,
  and its adjoint, consider a multiple
  field-of-view lidar observing a liquid cloud
• Wide fields of view provide information deeper
  into the cloud
• The NASA airborne THOR lidar is an example with
  8 fields of view
• Simple retrieval implemented with state vector
  consisting of profile of extinction coefficient
• Different solution methods implemented, e.g.
  Gauss-Newton, Levenberg-Marquardt and
  Quasi-Newton (L-BFGS)

100 m
10 m
600 m
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Results for a sine profile

• Simulated test with 200-m sinusoidal structure in
  extinction
• With one FOV, only retrieve first 2 optical
  depths
• With three FOVs, retrieve structure of extinction
  profile down to 6 optical depths
• Beyond that the information is smeared out

Nicola Pounder
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THOR lidar
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Forward model for depolarization subject to
multiple scattering
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Time-dependent 2-stream

• Describe diffuse flux in terms of outgoing stream
  I and incoming stream I, and numerically
  integrate the following coupled PDEs
• I and I are used to calculate total
  (unpolarized) backscatter btot b bT

Source Scattering from the quasi-direct beam
into each of the streams
Time derivative Remove this and we have the
time-independent two-stream approximation
Gain by scattering Radiation scattered from
the other stream
Loss by absorption or scattering Some of lost
radiation will enter the other stream
Spatial derivative Transport of radiation
from upstream
Hogan and Battaglia (J. Atmos. Sci., 2008.)
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...with depolarization

• Define co-polar weighted streams K and K and
  use them to calculate the co-polar backscatter
  bco b bT
• Evolution of these streams governed by the same
  equations but with a loss term related to the
  rate at which scattering is taking place, since
  every scattering event randomizes the
  polarization and hence reduces the memory of the
  original polarization
• But the single scattering albedo, w,represents
  the rate of loss due to absorption used in
  calculating g1, so this may be achieved simply by
  multiplying w by a factor ?, where 0 lt? lt 1
• This factor can be determined by comparison with
  Monte Carlo calculations provided by Alessandro
  Battaglia
• Depolarization ratio is then calculated from

Robin Hogan and Chris Westbrook
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1.2 optical depths
btot
bco
12 optical depths
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• Backscatter Depolarization ratio
• Comparison to Monte Carlo in isotropic clouds
  shows promising agreement for ? 0.8
• Need to refine behaviour for few scattering
  events does double scattering depolarize?
• If we can forward model this behaviour, we can
  exploit it in a retrieval

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Unified algo. work since PM2

• Interface to generic merged observation files
• Flexible configuration control to adapt to very
  different input data without recompiling
• A-Train, EarthCARE, airborne, ground-based (in
  principle)
• Applied to Juliens A-Train files
• Radar, lidar, MODIS and classification on the
  same grid
• Basic liquid and ice properties retrieved from
  radar and lidar
• Alternative minimizers implemented
• Nelder-Mead simplex method (no gradient info
  required)
• Gauss-Newton method with numerical Jacobian is
  being implemented
• Simple code profiling to locate the slowest part
  of the algorithm
• Parts could be sped-up, e.g. look-up of
  scattering properties is currently slower than
  radiative transfer!
• With numerical adjoint, currently takes 1 s per
  ray (expect large speed-up with analytic adjoint)

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Unified retrieval

• Ingredients developed before
• Not yet developed

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Lidar and forward model

• Only forward-model molecular signal where it has
  been affected by attenuation

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Radar and forward model

• Note no rain retrieved yet

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Retrieved ice and liquid

• Liquid clouds rather weakly constrained by
  observations at the moment

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Remaining tasks...

• Forward models for liquid clouds observed by
  EarthCARE
• Implement LIDORT for solar radiances
• Path-integrated attenuation model for radar using
  sea surface
• Fix adjoints of various forward models
• Finalize model of multiple scattering effect on
  depolarization
• Other tasks
• Include appropriate constraints for liquid
  retrievals (e.g. gradient constraint)
• Apply to ground-based observations
• Add aerosol and rain retrieval
• Lots more things to do

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