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Iterative surrogate cloud fields Victor Venema. Amplitude distribution  Amplitude (LWP, LWC,  ) alone is already good: See Independent Pixel Approximation.

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Presentation on theme: "Iterative surrogate cloud fields Victor Venema. Amplitude distribution  Amplitude (LWP, LWC,  ) alone is already good: See Independent Pixel Approximation."— Presentation transcript:

1 Iterative surrogate cloud fields Victor Venema

2 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

3 Measured power spectrum  Fractal power spectrum?  Measured power spectrum –Scale breaks –Waves –… Satellite pictures: Eumetsat

4 Iterative algorithm

5 Add an dimension  Assume isotropy  Rotate and scale power spectrum

6 3D surrogate clouds

7 Validation surrogate clouds  3D LWC fields from LES modelling  Make surrogates from their statistics  Calculate radiative properties –Radiances –Irradiances –Actinic fluxes  Compare them

8 Surrogate stratocumulus TemplatesSurrogates

9 Reflectance template and surrogate stratocumulus

10 Surrogate cumulus - old TemplatesSurrogates

11 Surrogate cumulus – radiance

12 Validation cumulus - new  Developed a more accurate Stochastic IAAFT algorithm  Surrogates are copies of templates  In practise the bias is likely still there as you cannot measure the power spectrum that accurately

13 Validation broken clouds

14 Scanning measurements  Structure maintaining interpolation  Anisotropic power spectrum  More samples  Better decorrelation

15 Scanning measurements  Scanning measurement –Amplitude distribution –2D power spectrum  Force the measured values on the spiral  Measured: 16.5 %

16 Scanning measurements Surrogate with cloud mask

17 Conclusions  IAAFT algorithm –Full 3D structure –LWC height profile –Local forcing of measurements –Flexible  Dimensions  Measurements  Vary the statistics independently  Validated for Sc and sparse Cu –Cloud cover > 80%, 80%, <20%  3D Cloud fields based on the BBC and BBC2 campaign on the BBC-server

18 Outlook  Improve convergence for broken clouds  Validate for broken clouds  Iterative wavelet surrogates  Constrained surrogates  Sebastián Gimeno García: 2D and 3D radiative transfer  Sebastian Schmidt: 3D surrogates from in situ measurements  3D Surrogates from scanning measurement

19 Comparison cloud generators   Reviewer: “It is not clear why this technique is an improvement over simpler approaches.”  IAAFT method  Cumulus fields (Evans; structure of a binary mask)  CALBAUTAIR (Schreirer and Schmidt)  Shift cloud (Schmidt; Los and Duynkerke)  2D-2D Ice cloud (Liou et al.)  tdMAP (A. Benassi, F. Szczap, et al.)  Multi-fractal clouds  Bounded Cascade and other fractal clouds  Fourier method –SITCOM (F. di Giuseppe; 2D structure) –Ice clouds (R. Hogan, S. Kew; 2.5D structure) 3SPL3SP3_PL2__L2__L2SP_2SP_2S__2S__

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21 Bounded Cascade  It is a power spectral method just as the Fourier method  The amplitude distribution is fixed, ‘Log-normal like’  Why take block functions?  At least for the Fourier method there is a large amount of literature  But Bounded Cascade clouds are fractals!!

22 1D Iterative LWP surrogates

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24 2D Iterative LWP surrogates

25 Conclusions and outlook  Improve convergence  Cumulus fields are very intermittant –Smooth clear sky part –Structured cloudy part  Maybe iterative wavelet surrogates would converge bettter  Evolutionary search algorithm doesn‘t get easily stuck in local minima


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