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3D cloud fields with measured power spectra and LWC height distributions for radiative transfer calculations. Victor Venema, Sebastián Gimeno García, Steffen Meyer, Clemens Simmer, Susanne Crewell, Ulrich Löhnert, Thomas Trautmann, Andreas Macke.

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slide1

3D cloud fields with measured power spectra and LWC height distributions for radiative transfer calculations

Victor Venema, Sebastián Gimeno García, Steffen Meyer,

Clemens Simmer, Susanne Crewell, Ulrich Löhnert, Thomas Trautmann, Andreas Macke

slide2

3D cloud fields with measured power spectra and LWC height distributions for radiative transfer calculations

Victor Venema, Clemens Simmer, Susanne Crewell, Ulrich Löhnert

University of Bonn

Sebastián Gimeno García, Thomas Trautmann

University of Leipzig / DLR

Steffen Meyer, Andreas Macke

University of Kiel

3d surrogate clouds

3

LWC template [kg/m

]

LWC Surrogate

2.2

0.5

6

6

0.4

2

4

4

0.3

1.8

Height [km]

2

2

0.2

1.6

0.1

0

0

1.4

0

2

4

6

0

2

10.5

11

1.5

Time [hr] UT

0

2

4

6

3D surrogate clouds

2D Measurement

3D LWC field

problem
Problem
  • Radiative transfer through clouds
    • Radiative transfer models
    • Realistic cloud fields
  • Dynamical cloud models
  • Fractal clouds
  • Empirical surrogate clouds
    • Stay closer to the measurement
empirical surrogate clouds
Empirical surrogate clouds
  • Surrogate clouds have (statistical) properties of measured clouds
  • Retrievals & parameterisations
    • Empirical alternative
  • Validation, closure experiments
    • Close to the measured cloud field
power spectrum
Power spectrum
  • Fourier transform, square the coefficients
  • Describes the linear spatial correlations in the field
  • Signal is a super positioning of sinuses
  • Equivalent to an spatial autocorrelation function
  • Gaussian PDF
amplitude distribution
Amplitude distribution
  • Amplitude (LWP, LWC, ) alone is already good: See Independent Pixel Approximation (IPA)
  • Especially very important are the cloud free portions
  • Together with power spectrum it also ‘defines’ the structure
add an dimension
Add an dimension
  • Assume isotropy
  • Rotate and scale power spectrum
lwc profiles
LWC profiles

Template

Surrogate

3d surrogate clouds1

R

Surrogate

3

LWC template [kg/m

]

LWC Surrogate

e

f

f

2.2

0.5

6

6

6

6

0.4

2

4

4

4

4

0.3

1.8

Height [km]

2

2

2

2

0.2

1.6

0.1

0

0

0

0

1.4

0

2

4

6

0

2

4

6

0

2

2

10.5

11

1.5

1.5

Time [hr] UT

0

2

4

6

0

2

4

6

3

LWC template [kg/m

]

LWC Surrogate

1.5

8

8

2

6

6

1

4

4

Height [km]

1.5

2

2

0.5

0

0

0

2

4

6

8

1

0

13.2

13.4

Time [hr] UT

0

2

4

6

8

3D surrogate clouds

R

Surrogate

e

f

f

8

8

6

6

4

4

2

2

0

0

0

2

4

6

8

2

2

1.5

1.5

1

1

0

2

4

6

8

validation surrogate clouds
Validation surrogate clouds
  • Which statistical parameters are needed to describe cloud structure?
  • Does the distribution and spectrum suffice?
  • Method
    • Modelled 3D LWC fields (template)
    • Make surrogates from their statistics
    • Calculate radiative properties
    • Compare them
surrogate stratocumulus
Surrogate stratocumulus

Rel. bias: 2 10-4,

Rel. RMS: 2 10-4

Rel. bias: < 7 10-4,

Rel. RMS: 6 10-3

surrogate cumulus
Surrogate cumulus

Rel. bias: < 1 10-2

Rel. RMS: 1 10-2

Rel. bias: < 2.3 10-2

Rel. RMS: < 4 10-2

conclusions and outlook
Conclusions and outlook
  • Validation LES: description is good
  • More validation cases available?
    • stratocumulus with holes, dense cumulus
    • Raining clouds, more cloud top structure
  • Scanning measurement
    • More samples
    • Better decorrelation
    • Anisotropic power spectrum
more information webpage
More information - Webpage
  • Algorithms (Matlab)
  • Examples
    • Measurements
    • Theoretical conditions
  • Articles in PDF
  • http://www.meteo.uni-bonn.de/ victor/themes/surrogates/
  • Google: surrogate cloud fields