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A new algorithm for the downscaling of 3-dimensional cloud fields

A new algorithm for the downscaling of 3-dimensional cloud fields. Victor Venema Sebastián Gimeno García Clemens Simmer. Applications. Downscaling 3D CRM/NWP model fields Downscaling of 2D satellite measurements Coarse mean LWC Coarse cloud fraction. Requirements downscaling method.

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A new algorithm for the downscaling of 3-dimensional cloud fields

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  1. A new algorithm for the downscaling of 3-dimensional cloud fields Victor VenemaSebastián Gimeno García Clemens Simmer

  2. Applications • Downscaling 3D CRM/NWP model fields • Downscaling of 2D satellite measurements • Coarse mean LWC • Coarse cloud fraction

  3. Requirements downscaling method • Nonlinear processes • Sub (coarse) scale distribution • IPA-bias: if you average  instead of (ir)radiances • Non-local processes • For example spatial correlations • 3D bias: ignore horizontal photon transport to low 

  4. Downscaling - Cumulus • High resolution original => • Coarse means • No clear subpixels • 2 coarse fields • Input downscaling • Real application start with coarse fields • Compare high-resolution fields • Physical • Radiative Coarse means Original No. clear subpixels

  5. Downscaling - Cumulus • High resolution original => • Coarse means • No clear subpixels • 2 coarse fields • Input downscaling • Real application start with coarse fields • Compare high-resolution fields • Physical • Radiative Coarse means No. clear subpixels Surrogate

  6. Downscaling - Cumulus • High resolution original => • Coarse means • No clear subpixels • 2 coarse fields • Input downscaling • Real application start with coarse fields • Compare high-resolution fields • Physical • Radiative Coarse means Original No. clear subpixels Surrogate

  7. Cumulus validation data • Diurnal cycle of Cu • Land (ARM) • 51 fields • High resolution • 64x64 pixels • Horizontal resolution 100m • Coarse resolution • 16x16 • Horizontal resolution 400m • Nc = 300 cm-3 Brown, A.R., R.T. Cederwall, A. Chlond, P.G. Duynkerke, J.C. Golaz, M. Khairoutdinov, D.C. Lewellen, A.P. Lock, M.K. MacVean, C.H. Moeng, R.A.J. Neggers, A.P. Siebesma and B. Stevens, 2002. Large-eddy simulation of the diurnal cycle of shallow cumulus convection over land, Q. J. R. Meteorol. Soc., 128(582), 1075-1093.

  8. Stratocumulus validation data • Dissolving broken Sc • Ocean (ASTEX) • 29 fields • High resolution • 200x200 pixels • Horizontal resolution 50m • Coarse resolution • 20x20 • Horizontal resolution 500m • Nc = 200 cm-3 Chosson, F., J.-L. Brenguier and L. Schüller, "Entrainment-mixing and radiative Transfer Simulation in Boundary-Layer Clouds", J Atmos. Res.

  9. Algorithm • Preparations • Calculate power spectrum coarse LWC field • Extrapolate spectrum to smaller scales • Main iterative loop • Adjust to the extrapolated spectrum • Adjust to the coarse fields • Remove jumps at edges of coarse field

  10. Algorithm – flow diagram

  11. Extrapolation power spectrum • Algorithm works with any power spectrum • Cumulus clouds • Assumption: • Intermediate to small scales are fractal • follow power law (Variance=akb) • Linear regression in log-log spectrum • Fitting range: • small scales of coarse field (intermediate scales full field) • Stratocumulus cloud • Not fractal at intermediate scales • Assumption: • Shape power spectrum same for all clouds • Computed an average isotropic spectrum over all clouds • Scaled by average variance at intermediate scales

  12. Original Extrapolated Surrogate Coarse field Example 3D fields Cumulus Stratocumulus

  13. Original Extrapolated Surrogate Coarse field Example 3D fields Cumulus Stratocumulus

  14. Reflectance SZA 0° Reflectance SZA 60° Transmittance SZA 0° Transmittance SZA 60° Scatterplot irradiances Cu Two Extrapolated Coarse field Interpolated originals surrogate field

  15. Reflectance SZA 0° Reflectance SZA 60° Transmittance SZA 0° Transmittance SZA 60° Scatterplot irradiances Sc Two Extrapolated Coarse field Interpolated originals surrogate field

  16. RMS relative difference

  17. RMS relative difference

  18. Conclusions • Downscaling algorithm works • Large improvement for irradiancescompared to coarse cloud fields • Extrapolation is a significant error source • Low number of pixels in coarse fields • Best extrapolation method is application dependent

  19. Outlook • Importance of the coarse cloud fraction field • Include a distribution for the anomalies • Wavelets, increment distributions? • Applications • Downscaling CRM/NWP model fields • Anomalies, small-scale spectrum from LES or observations • Downscaling of satellite measurements • Coarse LWP fields • High resolution in situ LWC, Reff measurements

  20. Outlook • Importance of the coarse cloud fraction field • Include a distribution for the anomalies • Wavelets, increment distributions? • Applications • Downscaling CRM/NWP model fields • Anomalies, small-scale spectrum from LES or observations • Downscaling of satellite measurements • Coarse LWP fields • High resolution in situ LWC, Reff measurements Thank you for your attention!

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