Cloud%20and%20precipitation%20best%20estimate

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Cloud and precipitation best estimateand
things I dont know that I want to know

• Robin Hogan
• University of Reading

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Unified retrieval
1. Define state variables to be retrieved Use
classification to specify variables describing
each species at each gate Ice and snow
extinction coefficient, N0, lidar ratio, riming
factor Liquid extinction coefficient and number
concentration Rain rain rate, drop diameter and
melting ice Aerosol extinction coefficient,
particle size and lidar ratio

• Ingredients developed
• Done since December
• Not yet developed

2. Forward model
2a. Radar model With surface return and multiple
scattering
2b. Lidar model Including HSRL channels and
multiple scattering
2c. Radiance model Solar IR channels
4. Iteration method Derive a new state vector
Gauss-Newton or quasi-Newton scheme
Not converged
3. Compare to observations Check for convergence
Converged
5. Calculate retrieval error Error covariances
averaging kernel
Proceed to next ray of data
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CloudSat
Calipso
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Simulated EarthCARE
CloudSat
EarthCARE CPR

• Higher sensitivity than CloudSat gives strikingly
  better sensitivity to tropical cirrus
• This case perhaps can form the basis for some
  ECSIM and Doppler simulations for an EarthCARE
  Bull Am Met Soc paper

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Extending ice retrievals to riming snow

• Heymsfield Westbrook (2010) fall speed vs.
  mass, size area
• Brown Francis (1995) ice never falls faster
  than 1 m/s

Brown Francis (1995)
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Simulated observations no riming
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Simulated retrievals no riming
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Simulated retrievals riming
But retrieval is completely dependent on how well
we can model scattering by rimed snowflakes!
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• There are known knowns. These are things we know
  that we know.
• There are known unknowns. That is to say, there
  are things that we know we don't know.
• But there are also unknown unknowns. There are
  things we don't know we don't know.
• Donald Rumsfeld

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A Rumsfeldian taxonomy

• The known knowns, things we know so well no error
  bar is needed
• Drops are spheres, density of water is 1000 kg
  m-3
• The known unknowns, things we can explicitly
  assign an well-founded error bar to in a
  variational retrieval
• Random errors in measured quantities (e.g. photon
  counting errors)
• Errors and error covariances in a-priori
  assumptions (e.g. rain number conc. parameter Nw
  varies climatologically with a factor of 3
  spread)
• The unknown unknowns where we dont know what the
  error is in an assumption or model
• Errors in radiative forward model, e.g.
  radar/lidar multiple scattering
• Errors in microphysical assumptions, e.g.
  mass-size relationship
• How do errors in classification feed through to
  errors in radiation?
• How do we treat systematic biases in measurements
  or assumptions?
• (also the ignored unknowns that we are too lazy
  to account for!)
• How can we move more things into the known
  unknowns category?

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Number concentration Size distribution width/shape Particle shape Radar scattering absorption Lidar scattering absorption
Warm liquid droplets Miles et al. (2000) Many aircraft campaigns Sphere Mie Mie
Supercooled droplets A few aircraft studies? Same as for warm droplets? Sphere Attenuation unknown! Mie
Drizzle Abel and Boutle (2012) Aircraft studies? Sphere Mie Mie
Rain Many distrometer studies Illingworth Blackman (2002) Spheroid, known aspect ratio T-matrix (Mie OK too) Mie is OK
Ice Delanoe and Hogan (2008) Delanoe et al. (2005), Field et al. (2005) Aggregate aspect ratio 0.6 Spheroid agrees with obs (Hogan et al. 2012) Retrieved lidar ratio encapsulates variations
Snow (possibly rimed) Same as ice? Same as ice? How do we represent riming? Scattering uncertain! Lidar ratio encapsulates variations
Melting ice Lies between snow rain? Lies between snow rain? Very uncertain Attenuation uncertain! Ignore
Aerosol Many aircraft campaigns Many aircraft campaigns Dry aerosol shape uncertain Ignore Lidar ratio encapsulates variations
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Melting layer modelling

• Fabry and Szyrmer (1999) found 10 dB spread in
  melting-layer reflectivity at 10 GHz although
  their Model 5 agreed best with obs
• But what about 94-GHz attenuation?
• What we really need is PIA through the melting
  layer versus rain rate, with an error

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Haynes et al. (2009)
Top of melting layer
Base of melting layer

• The same Szymer and Zawadzki (1999) model
• Rain rate 2 mm h-1
• 6-7 dB 2-way attenuation

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Matrosov (2008)

• 2-way radar attenuation in dB is 2.2 times rain
  rate in mm h-1
• Attenuation at 2 mm h-1 is 5-5.5 dB
• We need observational constraints!
• E.g. aircraft flying above and below melting
  layer, each with 94-GHz radar and the one below
  with airborne distrometer
• Or multi-wavelength ground-based technique?

Matrosov (IEEE Trans. Geosci. Rem. Sens. 2008)
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Snow
Simulated aggregate (Westbrook et al.)

• Whats the 94 GHz backscatter cross-section of
  this?
• Spheroid model works up to D l, but not for
  larger particles
• Rayleigh-Gans approximation works well describe
  structure simply by area of particle A(z) as
  function of distance in direction of propagation
  of radiation

Area of slice through particle A(z)
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Self-similar Rayleigh Gans approx.

• A spheroid has a very similar A(z) to the mean
  A(z) over many snow aggregates but for 1 cm snow
  at 94 GHz, the spheroid model underestimates
  backscatter by 2-3 orders of magnitude!
• Radiation resonates with structure in the
  particle on the scale of the wavelength, leading
  to a much higher backscatter, on average

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Supercooled water absorption

• No laboratory observations of 10-1000-GHz
  absorption of water colder than 6C!
• All models in the literature are therefore an
  extrapolation, and unsurprisingly they differ
  significantly
• This is of most concern for microwave radiometry
• For EarthCARE, it is of concern in convective
  clouds, but very unclear whether the observations
  can usefully tell us about supercooled water in
  these clouds anyway perhaps as a contribution to
  PIA in addition to rain?

Stefan Kneifel et al (submitted)
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Summary the unknown unknowns

• We need a best estimate and error for the
  following
• Snow backscatter cross-section at 94 GHz
• Self-Similar Rayleigh Gans equation test with
  multi-wavelength radar?
• Structure and radar scattering of riming
  particles
• How do we represent the continuous transition
  between low-density aggregates and high density
  graupel, and validate observationally?
• Melting-layer radar attenuation versus rain rate
• Key issue for interpreting PIA in rain need
  observational constraints
• Super-cooled water in convective clouds
• Radar absorption unknown, as well as vertical
  distribution
• Lidar backscatter of complex ice and aerosol
  shapes
• Is it sufficient to simply retrieve lidar ratio
  from the HSRL and so bypass the difficulty in
  modelling the scattering of such particles?
• A comprehensive algorithm inter-comparison
  project would also help to identify unknown
  unknowns!

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Examples of snow35 GHz radar at Chilbolton

• 1 m/s no riming or very weak

2-3 m/s riming?
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Simulated observations riming
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Power spectrum of snow structure
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New formula for backscatter

• Rayleigh-Gans formula
• Fourier-like decomposition of A(z)
• Assume amplitudes decrease at smaller scales as a
  power-law
• Formula for backscatter
• Wavenumber k
• Volume V
• Radius zmax
• Power-law parameters b g

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Prior information about size distribution

• Radarlidar enables us to retrieve two variables
  extinction a and N0 (a generalized intercept
  parameter of the size distribution)
• When lidar completely attenuated, N0 blends back
  to temperature-dependent a-priori and behaviour
  then similar to radar-only retrieval

• Aircraft obs show decrease of N0 towards warmer
  temperatures T
• (Acually retrieve N0/a0.6 because varies with T
  independent of IWC)
• Trend could be because of aggregation, or reduced
  ice nuclei at warmer temperatures
• But what happens in snow where aggregation could
  be much more rapid?

Delanoe and Hogan (2008)