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Iterative Surrogate Cloud Fields: Enhancing Cloud Structure Analysis

Explore how the Iterative Surrogate Cloud Fields method improves cloud structure analysis using an iterative algorithm to generate 3D surrogate cloud fields. Learn about power spectra, fractal features, and the validation of surrogates against real cloud data.

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Iterative Surrogate Cloud Fields: Enhancing Cloud Structure Analysis

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  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 Templates Surrogates

  9. Reflectance template and surrogate stratocumulus

  10. Templates Surrogates Surrogate cumulus - old

  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%, <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. 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) 3SPL 3SP 3_PL 2__L 2__L 2SP_ 2SP_ 2S__ 2S__ Comparison cloud generators

  20. 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!!

  21. 1D Iterative LWP surrogates

  22. 1D Iterative LWP surrogates

  23. 2D Iterative LWP surrogates

  24. 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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