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

2. Victor.Venema@uni-bonn.de, http://www.meteo.uni-bonn.de/venema Applications  Downscaling 3D CRM/NWP model fields  Downscaling of 2D satellite measurements  Coarse mean LWC  Coarse cloud fraction

3. Victor.Venema@uni-bonn.de, http://www.meteo.uni-bonn.de/venema 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. Victor.Venema@uni-bonn.de, http://www.meteo.uni-bonn.de/venema Downscaling - Cumulus No. clear subpixels Coarse means Original  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

5. Victor.Venema@uni-bonn.de, http://www.meteo.uni-bonn.de/venema Downscaling - Cumulus No. clear subpixels Surrogate Coarse means  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

6. Victor.Venema@uni-bonn.de, http://www.meteo.uni-bonn.de/venema Downscaling - Cumulus No. clear subpixels Surrogate Coarse means Original  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

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  N c = 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  N c = 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. Victor.Venema@uni-bonn.de, http://www.meteo.uni-bonn.de/venema Algorithm – flow diagram

11. Victor.Venema@uni-bonn.de, http://www.meteo.uni-bonn.de/venema Extrapolation power spectrum  Algorithm works with any power spectrum  Cumulus clouds –Assumption:  Intermediate to small scales are fractal  follow power law (Variance=ak b ) –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. Victor.Venema@uni-bonn.de, http://www.meteo.uni-bonn.de/venema Example 3D fields CumulusStratocumulus Original Extrapolated Surrogate Coarse field

13. Victor.Venema@uni-bonn.de, http://www.meteo.uni-bonn.de/venema Example 3D fields CumulusStratocumulus Original Extrapolated Surrogate Coarse field

14. Victor.Venema@uni-bonn.de, http://www.meteo.uni-bonn.de/venema Two ExtrapolatedCoarse fieldInterpolated originals surrogatefield Scatterplot irradiances Cu Reflectance SZA 0° Reflectance SZA 60° Transmittance SZA 0° Transmittance SZA 60°

15. Victor.Venema@uni-bonn.de, http://www.meteo.uni-bonn.de/venema Two ExtrapolatedCoarse fieldInterpolated originals surrogatefield Scatterplot irradiances Sc Reflectance SZA 0° Reflectance SZA 60° Transmittance SZA 0° Transmittance SZA 60°

16. Victor.Venema@uni-bonn.de, http://www.meteo.uni-bonn.de/venema RMS relative difference Rel.Diff. = (Field-Orig)/Orig CumulusStratocumulus Field ReflectanceTransmittanceReflectanceTransmittance Second original0.010.00010.0020.0002 Coarse field0.520.02710.1440.0115 Interpol. field0.990.05400.2080.0157 Extrapolated spect.0.070.00320.0380.0032 Fractal spectrum0.070.00420.0200.0009 Exact spectrum0.010.00020.0070.0005

17. Victor.Venema@uni-bonn.de, http://www.meteo.uni-bonn.de/venema RMS relative difference Rel.Diff. = (Field-Orig)/Orig CumulusStratocumulus Field ReflectanceTransmittanceReflectanceTransmittance Second original0.010.00010.0020.0002 Coarse field0.520.02710.1440.0115 Interpol. field0.990.05400.2080.0157 Extrapolated spect.0.070.00320.0380.0032 Fractal spectrum0.070.00420.0200.0009 Exact spectrum0.010.00020.0070.0005

18. Victor.Venema@uni-bonn.de, http://www.meteo.uni-bonn.de/venema Conclusions  Downscaling algorithm works –Large improvement for irradiances compared to coarse cloud fields  Extrapolation is a significant error source –Low number of pixels in coarse fields –Best extrapolation method is application dependent

19. Victor.Venema@uni-bonn.de, http://www.meteo.uni-bonn.de/venema 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, R eff measurements

20. Victor.Venema@uni-bonn.de, http://www.meteo.uni-bonn.de/venema 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, R eff measurements Thank you for your attention!